US20140187973A1 - System and method for tracking brain states during administration of anesthesia - Google Patents
System and method for tracking brain states during administration of anesthesia Download PDFInfo
- Publication number
- US20140187973A1 US20140187973A1 US14/115,682 US201214115682A US2014187973A1 US 20140187973 A1 US20140187973 A1 US 20140187973A1 US 201214115682 A US201214115682 A US 201214115682A US 2014187973 A1 US2014187973 A1 US 2014187973A1
- Authority
- US
- United States
- Prior art keywords
- patient
- drug
- processor
- phase
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4821—Determining level or depth of anaesthesia
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
- A61B5/372—Analysis of electroencephalograms
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
- A61B5/372—Analysis of electroencephalograms
- A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4836—Diagnosis combined with treatment in closed-loop systems or methods
- A61B5/4839—Diagnosis combined with treatment in closed-loop systems or methods combined with drug delivery
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7246—Details of waveform analysis using correlation, e.g. template matching or determination of similarity
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
- A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
- G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H70/00—ICT specially adapted for the handling or processing of medical references
- G16H70/40—ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
- A61B5/377—Electroencephalography [EEG] using evoked responses
- A61B5/38—Acoustic or auditory stimuli
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
Definitions
- the present invention relates to systems and methods for tracking brain states of a patient under anesthesia and, more particularly, to systems and methods for correlating anticipated effects of a given anesthetic compound administered to a patient with characteristics of the patient's brain state during the administration of the given anesthetic compound to more accurately track the effects of the given anesthetic compound and the actual brain state of the patient.
- Obvious variables include physical attributes of the patient, such as age, state of general health, height, or weight, but also less obvious variables that are extrapolated, for example, based on prior experiences of the patient when under anesthesia.
- these variables are compounded with the variables of a given anesthesiologists' practices and the variables presented by a particular anesthetic compound or, more so, combination of anesthetic compounds, the proper and effective administration of anesthesia to a given patient can appear to be an art and a science.
- the present invention overcomes the aforementioned drawbacks by providing a system and method for determining the state of a patient's brain under anesthesia using readily-available monitoring information, such as from a patient's electroencephalography (EEG).
- EEG electroencephalography
- the present invention recognizes that anesthetic compounds induce different signatures in physiological characteristics of the patient under anesthesia and aids interpretation of such information.
- the present invention aids the correlation of the physiological characteristics and signatures to a state of the patient's brain.
- a system for monitoring a patient experiencing an administration of at least one drug having anesthetic properties includes a plurality of sensors configured to acquire physiological data from the patient and at least one processor.
- the processor is configured to assemble the physiological data into sets of time-series data associated with an origin location of the patient, transform each set of time-series data into a spectrum information, and determine coherence information with respect to the associated origin locations associated with the time-series of data.
- the processor is further configured to identify signatures within at least one of the spectrum information and the coherence information indicative of at least one of a current state and a predicted future state of the patient and generate a report using the signatures including information regarding at least one of the current state and the predicted future state of the patient induced by the drug.
- a system for monitoring a patient experiencing an administration of at least one drug having anesthetic properties includes a plurality of sensors configured to acquire physiological data from the patient, a user interface configured to receive an indication of at least one of a characteristic of the patient and the at least one drug having anesthetic properties, and at least one processor.
- the processor is configured to identify signature profiles indicative of at least one of a current state and a predicted future state of the patient based on the indication and assemble the physiological data into sets of time-series data.
- the processor is further configured to analyze the sets of time-series data using the identified signature profiles and generate a report including information regarding at least one of the current state and the predicted future state of the patient induced by the drug.
- FIG. 1 is a schematic illustration of a system for determining the state of a patients brain under anesthesia in accordance with the present invention.
- FIG. 2 is a flow chart setting forth the steps of a method for determining the state of a patient's brain under anesthesia in accordance with the present invention
- FIG. 3A is a series of spectrograms acquired under different drug or patient characteristics.
- FIG. 3B is a spectrogram and associated EEG waveforms showing the overlapping influence of different drugs administered to a patient.
- FIG. 4 is a series of EEG waveforms collected to illustrate variations therein that can be observed as corresponding with respective patient states.
- FIG. 5 is a collection of data readouts including EEG waveforms, a frequency analysis, and a spectrogram illustrating key markers within the data and reflected in each data readout.
- FIG. 6 is a collection of spectrograms of the radial current density estimated at each of a plurality of electrode sites.
- FIG. 7 is a collection of EEG waveforms and spectrograms illustrating key markers within the data and reflected in each data format.
- FIG. 8 is a set of graphs, phase-amplitude histograms, and EEG waveforms illustrating phase-amplitude analysis as a mechanism for determining and predicting future patient states.
- FIG. 9 is a collection of EEG waveforms and spectrograms illustrating key markers within the data and reflected in each data format.
- FIGS. 10-15 are graphs, each figure corresponding to a different drug, that illustrate the ability to create “spectral templates” for each of a plurality of exemplary drugs, which can be used in accordance with the present invention.
- the present invention recognizes that anesthetic compounds induce different signatures in physiological characteristics of the patient under anesthesia and aids interpretation of physiological characteristics and signatures therein based on a selected anesthesia compound. Using the physiological characteristics and signatures associated with the selected anesthesia compound, the present invention aids the correlation of the physiological characteristics and signatures to a state of the patient's brain.
- a system 10 configured for use in accordance with the present invention includes a patient monitoring device 12 , such as a physiological monitoring device, illustrated in FIG. 1 as an electroencephalography (EEG) electrode array.
- EEG electroencephalography
- the patient monitoring device may also include mechanisms for monitoring galvanic skin response (GSR), for example, to measure arousal to external stimuli.
- GSR galvanic skin response
- One specific realization of this design utilizes a frontal Laplacian EEG electrode layout with additional electrodes to measure GSR.
- Another realization of this design incorporates a frontal array of electrodes that could be combined in post-processing to obtain any combination of electrodes found to optimally detect the EEG signatures described earlier, also with separate GSR electrodes.
- Another realization of this design utilizes a high-density layout sampling the entire scalp surface using between 64 to 256 sensors for the purpose of source localization, also with separate GSR electrodes.
- the patient monitoring device 12 is connected via a cable 14 to communicate with a monitoring system 16 .
- cable 14 and similar connections can be replaced by wireless connections between components.
- the monitoring system 18 may be further connected to a dedicated analysis system 18 .
- the monitoring system 16 and analysis system 18 may be integrated.
- the patient monitoring device 12 may be an EEG electrode array, for example, a 64-lead EEG electrode array.
- EEG electrode array for example, a 64-lead EEG electrode array.
- greater spatial accuracy can be achieved by increasing the number of electrodes from 64 to 128, 256, or even higher.
- the present invention can be implemented with substantially less electrodes.
- the monitoring system 16 may be configured to receive raw signals acquired by the EEG electrode array and assemble, and even display, the raw signals as EEG waveforms.
- the analysis system 18 may receive the EEG waveforms from the monitoring system 16 and, as will be described, analyze the EEG waveforms and signatures therein based on a selected anesthesia compound, determine a state of the patient based on the analyzed EEG waveforms and signatures, and generate a report, for example, as a printed report or, preferably, a real-time display of signature information and determined state.
- a report for example, as a printed report or, preferably, a real-time display of signature information and determined state.
- the functions of monitoring system 16 and analysis system 18 may be combined into a common system.
- a method for analysis and reporting in accordance with the present invention begins at process block 200 with the selection of a desired drug, such as anesthesia compound or compounds, and/or a particular patient profile, such as a patient's age height, weight, gender, or the like. Such selection may be communicated through a user interface 20 of FIG. 1 . Furthermore, drug administration information, such as timing, dose, rate, and the like, in conjunction with the above-described EEG data may be acquired and used to estimate and predict future patient states in accordance with the present invention. As will be described, the present invention recognizes that the physiological responses to anesthesia vary based on the specific compound or compounds administered, as well as the patient profile.
- the present invention accounts for this variation between an elderly patient and a younger patient. Furthermore, the present invention recognizes that analyzing physiological data for signatures particular to a specific anesthetic compound or compounds administered and/or the profile of the patient substantially increases the ability to identify particular indicators of the patient's brain being in a particular state and the accuracy of state indicators and predictions based on those indicators.
- drugs are examples of drugs or anesthetic compounds that may be used with the present invention: Propofol, Etomidate, Barbiturates, Thiopental, Pentobarbital, Phenobarbital, Methohexital, Benzodiazepines, Midazolam, Diazepam, Lorazepam, Dexmedetomidine, Ketamine, Sevoflurane, Isoflurane, Desflurane, Remifenanil, Fentanyl, Sufentanil, Alfentanil, and the like.
- the present invention recognizes that each of these drugs, induces very different characteristics or signatures, for example, within EEG data or waveforms.
- FIG. 15 provides EEG data for one prominent drug, propofol, and associated states.
- FIG. 3A a plurality of spectrograms corresponding, as labeled, to patients having been administered Propofol, Dexmedetomidine, Sevoflurane, and Ketamine are illustrated.
- FIG. 3A shows a spectrogram of an elderly patient.
- the spectrograms vary substantially, so as to be visually distinct, based on the administered drug and/or patient profile. This is particularly true, for example, when multiple drugs are combined, such as illustrated in FIG. 3B .
- the present invention recognizes the substantial variation in physiological data acquired from a patient and the signatures contained therein.
- the present invention Based on a selected drug or drugs and/or the patient profile and, by faking this recognition into account, the present invention provides systems and methods for tracking brain states during the administration of anesthesia that is greatly improved over traditional systems.
- a summary of exemplary “spectral templates” for each of a plurality of exemplary drugs is provided in the “examples” section. These “spectral templates” can be used to automatically identify a current or project a future state of the patient.
- EEG data EEG data
- FIG. 4 a series of EEG waveforms in the time domain are illustrated. As is clear in a side-by-side comparison such as illustrated in FIG. 4 , these EEG waveforms vary appreciably.
- the present invention provides systems and methods for analyzing acquired physiological information from a patient, analyzing the information and the key indicators included therein, and extrapolating information regarding a current and/or predicted future state of the patient.
- the physiological data is processed. Processing can be done in the electrode or sensor space or extrapolated to the locations in the brain. As will be described, the present invention enables the tracking of the spatiotemporal dynamics of the brain by combining spectrogram and global coherence analyses. As will be apparent, reference to “spectrogram” in may refer to a visual representation of frequency domain information, such as represented in, for example, FIG. 3A . However, a “spectrogram” within the context of the present invention need not be visually represented or displayed. Rather, within the context, for example, of processing and report generation, the spectrogram may be an intermediate processing step from which reports or visual representations are ultimately created. For example, referring to FIG.
- EEG waveforms in the time domain 500 have a spectrum 502 and can be translated into a spectrogram 504 .
- Laplacian referencing can be performed at process block 208 to estimate radial current densities perpendicular to the scalp at each electrode site of, for example, the monitoring device 12 of FIG. 1 . Accordingly, though “spectrogram” processing is performed, a visual representation of the spectrogram need not be displayed.
- V i m (t) denotes the voltage recording at the m th closest electrode to electrode i.
- the EEG recorded at a particular location was locally referenced to an average of the EEG recorded at the neighbors.
- spectrograms at each electrode site are computed.
- spectrograms of the radial current density estimated at each of a plurality of electrode sites are illustrated.
- the spectrograms reflect, as illustrated in FIG. 7 , key signatures commonly identified or tracked in the time domain EEG waveforms.
- two time domain EEG waveforms and associated spectrograms are illustrated, one set 700 corresponding to light Dexmedetomine sedation and one set 702 corresponding to deep Dexmedetomine sedation.
- spindles 704 are visible and in the set 702 corresponding to deep Dexmedetomine sedation, strong slow wave oscillations 706 are visible.
- the local referencing is preferable so that distinct temporal patterns in the spectrogram at different electrode sites can be identified. This is in contrast to an average or single electrode referencing scheme, which would lead to an erroneous conclusion that approximately the same temporal pattern was present in the spectrogram at each electrode site.
- the spectrum of the surface Laplacian at the location of the i th electrode site is estimated by averaging over K non-overlapping segments:
- an eigenvalue decomposition can be computed of a cross-spectral matrix at each spectral frequency as a function of time.
- spectral and eigenvalue-based global coherence analyses can be used to track the spatiatemporal dynamics of the brain's anesthetic state.
- the global coherence analysis detects strong coordinated a activity in the occipital sites when the patient is awake that shifts to the frontal sites when the patient becomes unconscious.
- method-of-moments estimates of the i*n element of the cross-spectral matrix at a frequency f can be computed as:
- X i k (f) and X j k (f) are the tapered Fourier transforms of the current density estimates from electrode sites i and j, respectively, at frequency f.
- C x (f) is an N ⁇ N matrix of cross-spectra.
- An orthogonal basis can be obtained by performing a Karhunen-Loeve transform at each frequency, f:
- U(f) H is chosen so that under the Karhunen-Loeve transform the cross-spectral matrix in the new basis:
- This ratio is called the global coherence.
- C Global (f) is close to 1.
- examining the contributions of different sites to the corresponding eigenvector by using the elements of the weight matrix provides a summary of coordinated activity at this frequency.
- These elements are row weights.
- the row weights can be obtained by the absolute value square of the elements of the row of U(f) H , which leads to the eigenvector with the highest eigenvalue.
- process blocks 208 - 212 yield two pieces of valuable information, namely, the spectrogram and global coherence information, which show different spatiotemporal activity at different states of the patient receiving anesthesia.
- the spectrograms will show strong occipital ⁇ activity.
- the spectrograms will show a loss of ⁇ activity and an increase in ⁇ activity in the occipital sites and strong ⁇ and ⁇ activity in the frontal sites, increased power in the ⁇ , ⁇ , and ⁇ ranges in the frontal sites will occur after loss of consciousness, consistent with the well-known pattern of anteriorization.
- global coherence and weight matrices along with spectrograms provide a first level of data for determining a current state and predicting a future state of a patient's brain under anesthesia. Further details regarding initial testing and validation of such processes are provided in Cimenser A, Purdon P L, Pierce E T, Walsh J L, Salazar-Gomez A F, Harrell P G, Tavares-Stoeckel C, Habeeb K, Brown E N (2011) Tracking brain states under general anesthesia by using global coherence analysis. Proceedings of the National Academy of Sciences of the United States of America 108:8832-8837.
- phase-amplitude analysis is performed that considers the amplitude of a given signal with respect to the phase of other signals and vice versa.
- spectral analysis of EEG recordings allows the present invention to track systematic changes in the power in specific frequency bands associated with administration of anesthesia, including changes in ⁇ (1-4 Hz), ⁇ (5-8 Hz), ⁇ (8-14 Hz), ⁇ (12-30 Hz), and ⁇ (30-80 Hz).
- spectral analysis treats oscillations within each frequency band independently, ignoring correlations in either phase or amplitude between rhythms at different frequencies.
- Power spectral measures are invariant with respect to changes in the complex phase of a signal's Fourier transform. It is thus natural to extend power spectral analysis by using measures that are sensitive to signal phase. Bispectral analysis can detect the presence of correlation in the phases of oscillation at different frequencies. Bispectrum-based statistics have been used in quantitative clinical depth-of-anesthesia monitors, in a manner that compares the bispectrum across broad low- and high-frequency ranges.
- phase-amplitude analysis instead of a traditional “cross-frequency correlation”, phase-amplitude analysis, is used, in phase-amplitude analysis, the amplitude or envelope of activity in one frequency band is consistently largest at a particular phase of a lower frequency rhythm. For example, given two non-overlapping frequency bands, then in phase-amplitude coupling, the amplitude of the activity in the higher frequency band is consistently highest at a particular phase of the lower frequency rhythm.
- an analysis can be performed to measure phase-amplitude coupling in a time-resolved fashion to identify at least two distinct modes of phase-amplitude coupling corresponding to shallow and deep planes of anesthesia, respectively.
- a time-varying phase-amplitude modulogram M(t, ⁇ ) can be created that describes the relative ⁇ (or other) amplitude at a particular phase at each SO cycle.
- ultra-low-frequency drift is removed by subtracting a least-square errors spline fit to the signal with one knot for every 2 minutes (or other selected duration) of data.
- a band-pass filter may be applied to extract the rhythmic component within each frequency band of interest, x b (t),b ⁇ ,SO ⁇ .
- Symmetric finite impulse response filters designed using a least-squares approach SO: passband 0.1-1 Hz, transition bands 0.085-0.1 and 1-1.15 Hz, ⁇ 17 dB attenuation in stop bands, order 2207 at 250 Hz; ⁇ : passband 8-13.9 Hz, transition bands 5.9-8 and 13.9-16 Hz, ⁇ 60 dB attenuation in stop bands, order 513
- and SO phase, ⁇ (t) arg[z SO (t)].
- the modulogram is computed by assigning each temporal sample to one of, for example, 18 equally spaced phase bins based on the instantaneous value of ⁇ /(t), then averaging the corresponding values of A(t) within, for example, a 2-minute epoch:
- M ⁇ ( t , ⁇ ) ⁇ t - ⁇ ⁇ ⁇ t 2 t + ⁇ ⁇ ⁇ t 2 ⁇ ⁇ ⁇ - ⁇ ⁇ ⁇ t 2 ⁇ + ⁇ ⁇ ⁇ t 2 ⁇ A ⁇ ( t ′ ) ⁇ ⁇ ⁇ ( ⁇ ⁇ ( t ′ ) - ⁇ ′ ) ⁇ ⁇ ⁇ ′ ⁇ ⁇ t ′ 2 ⁇ ⁇ ⁇ t - ⁇ ⁇ ⁇ t 2 t + ⁇ ⁇ ⁇ t 2 ⁇ A ⁇ ( t ′ ) ⁇ ⁇ t ′ ; Eqn . ⁇ 7
- FIG. 8 illustrates two distinct patterns of phase-amplitude modulation. Namely, a first phase-amplitude modulation is similar to slow wave sleep (peak-max—i.e., high-frequency activity is highest at the peak of the low-frequency oscillation, corresponding to a low-frequency phase of 0), and a second phase-amplitude modulation foreshadows the return of consciousness (trough-max—i.e., high-frequency activity is highest at the trough of the low-frequency oscillation, corresponding to a low-frequency phase of +/ ⁇ ).
- Slow oscillation phase modulates alpha/beta (8-14 Hz) amplitude, in relation to probability of response, can be studied and is reflected in FIG. 8 .
- a group behavioral curves 802 show the probability of response to click and verbal stimuli during induction in the first graph 804 and emergence in the second graph 806 .
- a set of phase-amplitude histograms 808 show the relationship between the slow oscillation phase (y-axis, shown with reference sinusoid) and mean-normalized alpha/beta amplitude as a function of time (x-axis) relative to LOG 810 and ROC 812 .
- a trough-max phase-amplitude relationship can be observed at the LOC/ROC transition points, where the amplitude of alpha is maximal at the slow oscillation troughs.
- a peak-max phase-amplitude relationship can be observed during the unconscious state, where the amplitude of alpha is maximal at slow oscillation peaks.
- These trough-max and peak-max modulation patterns can be observed in raw EEG traces 814 , which shows the trough-max and 816 , which shows the peak-max, respectively.
- the trough-max pattern re-appears during emergence prior to ROC, illustrating that it can be used to predict when patients are able to regain consciousness during anesthesia.
- the transition to the trough-max pattern occurs reliably before return of consciousness, the trough-max relationship to predict when patients are likely to recover consciousness while emerging from anesthesia, in cases where trough-max modulation is absent, due to pathology that impairs alpha waves, or drug choice (e.g., sevoflurane), or where electrode placement makes detection of trough-max modulation difficult, the absence or loss of peak-max modulation could also be used to predict recovery of consciousness during emergence. More particularly, during emergence from propofol anesthesia, the peak-max modulation relationship between the phase of the slow oscillation and higher frequencies changes to the trough-max modulation relationship, and does so prior to the return of consciousness, with little change to the underlying power spectrum. The trough-max modulation has a frontal distribution, whereas the peak-max modulation is distributed approximately uniformly across the scalp.
- phase-amplitude information can provide a reliable indicator of a current or probable future patients state.
- the peak-max modulation pattern represents a state of unconsciousness that is more profound than that observed during trough-max modulation, but less profound than burst-suppression
- the peak-max modulation pattern could be used as a target for maintenance of a surgical level of anesthesia.
- the trough-max modulation represents a state of unconsciousness that is less profound than the peak-max modulation, one where patients can respond to external stimuli
- the trough-max modulation pattern could be used as a target for maintenance of sedation.
- elderly patients often exhibit diminished alpha waves or a lack of alpha waves.
- monitoring can also be performed by calculating slow oscillation phase-amplitude modulation across a broad-band frequency range including theta, alpha, beta, and gamma bands.
- MI(t) can be defined, as the Kullback-Leibler divergence, in bits, between M (t, ⁇ ) and a uniform phase distribution over the interval ( ⁇ , ⁇ ):
- MI ⁇ ( t ) ⁇ - ⁇ ⁇ ⁇ M ⁇ ( t , ⁇ ) 2 ⁇ ⁇ ⁇ log 2 ⁇ M ⁇ ( t , ⁇ ) ⁇ ⁇ ⁇ . Eqn . ⁇ 8
- the power spectrum and phase-amplitude coupling may be complementary sources of information about brain dynamics.
- a combination of both measures may reveal greater structure than either analysis alone.
- the EEG power spectrum during gradual administration of anesthesia shows a broad-band peak that begins in gamma frequencies, and decreases in frequency and bandwidth into the low-beta and alpha bands with increasing doses of anesthesia resulting in loss of consciousness.
- the gamma and beta range effects are associated with a reduced probability of response to external stimuli. Power within this traveling peak is strongest in frontal EEG channels. This reverses after recovery of consciousness.
- the traveling peak frequency can be quantified, for example, as the median between 2 and 40 Hz, and calculate the bandwidth using the interquartile range between the same limits.
- phase-amplitude modulation effect and systems and methods for monitoring thereof is best observed using a local average of several electrodes, such as the surface Laplacian. Otherwise, phase-amplitude modulation effect can be poorly resolved or not observable.
- a beamforming procedure may be used to improve estimation of phase-amplitude modulation.
- ⁇ (t): [ ⁇ 1 (t), ⁇ 2 (t), . . . , ⁇ N (t)] T
- s(t): [s 1 (t), s 2 (t), . . .
- s N (t)] T denote the alpha rhythm and slow oscillation time-series which are obtained by band-pass filtering x(t) in the frequency bands of 8-14 Hz and 0.1-1 Hz, respectively.
- the amplitude of the alpha rhythm is modulated by the phase of the slow oscillation during anesthesia, based on an analysis of single-channel Laplacian-derived EEG.
- the problem reduces to reconstructing a single phase-amplitude modulation relationship based on the observation through the multi-channel array of EEG sensors.
- a viable solution is given by beamforming.
- the idea of beamforming is to form a scalar signal based on the array observations in order to minimize an appropriate cost function representing the underlying system model.
- the amplitude of ⁇ W (t) and the phase (argument) of s W (t) are given by:
- phase-amplitude modulation relation is defined as:
- a W ( ⁇ ; t ): E p w ⁇ A w ( t )
- a suitable model for estimating A W ( ⁇ ) is given by its truncated Fourier expansion to the first L terms, with L ⁇ 3. This reduced-order model enforces a smooth phase-amplitude modulation relation, which is consistent with empirical observations.
- a suitable cost function for estimating A W ( ⁇ ) is given by the following quadratic form:
- t w ( ⁇ ) denotes the inverse function of ⁇ w (t)
- p( ⁇ ) is the prior distribution of the slow oscillation phase
- E t denotes temporal averaging.
- the best such beamformer can be chosen by minimizing the resulting cost function over w.
- This corresponds to a cost minimization formulation for estimating the reduced-order phase-amplitude modulation relation that is most consistent with the data (in the sense of the above quadratic cost function).
- the overall optimization procedure can be expressed as:
- the inner minimization can be carried out by linear regression and the resulting solution can be expressed explicitly in terms of A w (t) and ⁇ w (t).
- the outer minimization can be performed using standard optimization routines. In particular, since the constraints on w k form a convex set, the interior point method for the outer minimization stage can be employed.
- phase-amplitude modulation of frontal EEG under anesthesia undergoes two different patterns of modulation, corresponding to depth of anesthesia.
- the first pattern occurring before and after the loss of consciousness, consists of maximum alpha amplitude occurring at the trough (surface-negative) of the slow oscillation, which can be referred to as the “trough-max” pattern.
- trough-max maximum alpha amplitude occurring at the trough (surface-negative) of the slow oscillation
- peak-max the relationship reverses and maximum alpha amplitude occurs at the peak (surfacepositive) of the slow oscillation.
- Patient 1 Trough-Max Peak-Max Trough-Max Peak-Max Bipolar 0.26 0.39 0.22 0.76 Laplacian 1.08 0.73 0.98 1.00 Optimized 1.23 0.91 1.33 1.67
- the beamforming method produced the largest modulation depth, followed by the Laplacian method, with bipolar referencing showing the lowest modulation depth in both regimes.
- the beamforming method provides a means to obtain electrode weights that minimize the least-squares error in a parametric sinusoidal model of the phase-amplitude relationship. This optimal weighting of EEG electrodes allows for improved detection of phase-amplitude modulation across time and patients. This method could be useful in studies of phase-amplitude modulation in the EEG under anesthesia, as well as other conditions where this phenomenon might arise.
- the above-described selection of an appropriate analysis context based on a selected drug or drugs (process block 200 ), the acquisition of data (process block 204 ), and the analysis of the acquired data (process blocks 206 - 214 ) set the stage for the new and substantially improved real-time analysis and reporting on the state of a patient's brain as an anesthetic or combination of anesthetics is being administered and the recovery from the administered anesthetic or combination of anesthetics occurs.
- the present invention provides a mechanism for considering each of these separate pieces of data and more to accurately indicate and/or report on a state of the patient under anesthesia and/or the indicators or signatures that indicate the state of the patient under anesthesia.
- any and ail of the above-described analysis and/or results can be reported and, in addition, can be coupled with a precise statistical characterizations of behavioral dynamics. That is, behavioral dynamics, such as the points of loss-of-consciousness and recovery-of-consciousness can be precisely, and statistically calculated and indicated in accordance with the present invention. To do so, the present invention may use dynamic Bayesian methods that allow accurate alignment of the spectral and global coherence analyses relative to behavioral markers.
- a state-space model with two state variables representing a probability of response and a conditional probability of correct response can be used to correlate outcomes with a predicted future state. Probability densities of model parameters and the response probability are computed within a Bayesian framework to provide precise statistical characterizations of behavioral dynamics.
- the experiment consists of K stimulus trials. On any trial these are three possible outcomes for the response to the verbal stimulus; the subject may respond correctly, respond incorrectly or not respond.
- the observed data at trail k is the pair (m k, n k ) which can assume the values ⁇ (1,1), (1,0), (0,0) ⁇ .
- the observation model at trail k is therefore:
- p k ,q k ) [ p k ( q k ) 1-n k ] m k (1 ⁇ p k ) 1-m k Eqn. 17;
- ⁇ k and ⁇ k are zero-mean Gaussian random variables with variances ⁇ ⁇ 2 ⁇ k and ⁇ n 2 ⁇ k where ⁇ k is the time elapsed between verbal stimulus trials k ⁇ 1 and k.
- p k , q k , 4 ) p k m k ⁇ ( 1 - p k ) 4 - m k ⁇ ( m k n k ) ⁇ q k n k ⁇ ( 1 - q k ) m k - n k . Eqn . ⁇ 22
- M ) f ⁇ ( M
- the observation models (Eqs. 17-19, 22) define f (M
- f( ⁇ ) we chose the independent prior distributions for x v,0 , z v,0 , x c,0 and z c0 to be uniform distributions each on the interval [0,100].
- the set of free parameters includes all the x 0 and ail the ⁇ 2 . These are computed using the Bayesian approach, which assumes that prior information about the parameters improves the parameter estimates. For all the x 0 , uniform prior distribution, uniform (a, b), can be chosen. For all the ⁇ 2 , the conjugate inverse gamma prior distribution, inverse gamma ( ⁇ , ⁇ ) can be chosen. Assuming values of 0 and 100 for a and b respectively to reflect the fact that the patient's behavior markers correlates perfectly at the beginning of tracking, ⁇ and ⁇ can be chosen to be 5 and 1, making the inverse gamma prior distribution non-informative.
- a Bayesian analysis implementation such as described in A. C. Smith, S.
- the result is a report that can be coupled with a precise statistical characterization of behavioral dynamics. That is, behavioral dynamics, such as the points of loss-of-consciousness and recovery-of-consciousness (or other selected states) can be precisely, and statistically calculated and indicated in accordance with the present invention. Specifically, this report can aid clinicians by allowing information that was previously unknown or incapable of being discerned from traditional monitoring systems to be identified and communicated and/or used by the clinician and/or monitoring system to identify particular states of a given patient.
- the report may serve as part of a “human in-the-loop” operational strategy, whereby the above-described systems automatically detects spectral features of interest and the report serves as a mechanism by which to highlight or communicate the spectral features and the information extrapolated therefrom to inform clinicians of a given or predicted future state and the reasoning therebehind.
- the report may include topographic maps of the patient's scalp or source localization maps on a brain image rendering to provide information relating to the location within the brain from which the EEG activity is being received.
- FIG. 9 provides two sets of time domain EEG waveforms and associated spectrograms acquired during clinical use of Propofol.
- a first set 900 illustrates two time domain EEG waveforms, a first EEG waveform demonstrating delta oscillation 902 and the second EEG waveform demonstrating delta-alpha oscillation 904 .
- an associated spectrogram 906 displays the same delta oscillation and delta-alpha oscillation, but in form that is able to be more readily interpreted, particularly in real time.
- a second set 908 illustrates two time domain EEG waveforms, a first EEG waveform demonstrating delta-alpha oscillation 910 and the second EEG waveform demonstrating burst suppression 912 . While simultaneously reading and interpreting the separate EEG waveforms 910 , 912 is difficult, an associated spectrogram 914 displays the same delta-alpha oscillation and burst suppression, but in form that is able to be more readily interpreted in real time.
- a substantial amount of new and important signatures that were previously difficult to consider or understand and/or were previously unknown or not understood as reliable without considering drug and/or patient-specific signature information can be reliably determined and used to track a current state of a patient.
- sevoflurane shows increased slow, delta, theta, and alpha power.
- the increase in theta power is pronounced and visible in the power spectrum.
- Slow oscillation phase-amplitude coupling shows that high frequency activity is greatest on the rising phase ( ⁇ /2) of the slow oscillation.
- ketamine When ketamine is administered, power in beta and gamma bands increase.
- the two drugs act on the EEG in an antagonistic fashion.
- ketamine If enough ketamine is administered, it reduces or abolishes both slow and alpha power, and increases gamma power.
- Dexmedetomidine shows increased slow, delta, and sigma (12-16 Hz) power at lower doses consistent with sedation. At higher doses the EEG is dominated by slow oscillations.
- the above-referenced report can indicate, predict, and/or track onset of loss of consciousness and recovery of consciousness based on increased gamma (25 to 40 Hz) and beta (12 to 25 Hz) activity; transition to unconsciousness and in the unconscious state, and recovery of consciousness based on increased/decreased slow (0 to 1 Hz), delta (1 to 4 Hz), theta (4 to 8 Hz), and alpha (8 to 12 Hz) activity; anesthetic drug administration and loss of consciousness/recovery of consciousness based on reduced theta (4 to 8 Hz) power; loss of consciousness and recovery of consciousness associated with changes in the ratio of alpha and delta (1 to 4 Hz) power in the occipital region of the scalp; states of profound unconsciousness by identifying strong global coherence in the alpha band; states of profound anesthesia based on strong association between global coherence and the state of anteriorization; profound unconsciousness based on strong modulation of the theta (4 to 8 Hz), alpha (8 to 12 Hz
- spectral templates for each of a plurality of exemplary drugs can be provided and used in accordance with the present invention.
- EEG data was acquired from operating room surgical cases during the administration of anesthesia. The data was separated into three age demographics: young (>35), middle-aged (36-59), and elderly ( ⁇ 60); and by drug: propofol, sevoflurane, isoflurane, dexmedetomidine, and ketamine. Spectrograms from each of the cases were analyzed to identify temporal intervals containing recurring spectral motifs correlated with putative unconscious and deep levels of anesthesia. These intervals were used to compute median spectra for each of the drug/demographic pairings.
- propofol has studied.
- the spectral motifs for propofol included three salient spectral peaks: a low frequency ( ⁇ 1 Hz) oscillation, a traveling peak (spanning gamma through alpha), and a broadband gamma.
- FIG. 11 provides information similar to that of FIG. 10 , in this case, with respect to sevoflurane.
- the spectral motifs for sevoflurane included two salient spectral peaks: a broad low frequency oscillation (spanning ⁇ 1 Hz, delta, and theta bands), and a traveling peak.
- FIG. 12 provides information similar to that of FIGS. 10 and 11 , in this case, with respect to isoflurane.
- the spectral motifs for isoflurane included two salient spectral peaks: a broad low frequency oscillation (spanning ⁇ 1 Hz, delta and theta bands), and a traveling peak.
- FIG. 13 provides information similar to that of FIGS. 10-12 , in this case, with respect to dexmedetomidine.
- the data for young and elderly patients were limited and showed spectral heterogeneity.
- the spectral motifs for dexmedetomidine included three salient spectral peaks: low frequency oscillation, a non-stationary “spindle” peak spanning alpha and sigma bands, and broadband gamma.
- FIG. 14 provides information similar to that of FIGS. 10-13 , in this case, with respect to ketamine.
- Data was acquired from one middle aged patient. The effects, however, seem marked between putative unconscious and deep states of anesthesia.
- the spectral motifs for ketamine included three salient spectral peaks: the low frequency oscillation, and a non-stationary low gamma peak, and broadband high gamma (up to 150 Hz).
- the present invention has ready use and clinical need in fields of medicine other than anesthesiology.
- other medical specialties either use or have interest in the use of drugs such as those described above and other similar drugs.
- the above-described systems and methods are useful for managing drugs in a wide variety of situations.
- the present invention can be used during pharmacological therapies to induce and maintain sedation.
- the present invention can be particularly useful in the intensive care unit where intense therapies are administered and clinicians can benefit from additional monitoring and feedback.
- the present invention can be of use in outpatient settings, including outpatient treatments involving pharmacologically-induced sleep, involving sedation, for example, using dexmedetomidine.
- the present invention can be used in psychiatry settings to aid in the treatment of depression with ketamine, for example. These are but a few of the wide-variety of clinical and non-clinical settings where the present invention can be readily applied.
Abstract
Description
- This application is based on, claims the benefit of, and incorporates herein by reference U.S. Provisional Application Ser. No. 61/483,483, filed May 6, 2011, and entitled, “A Method for Using EEG and Advanced Signal Processing Algorithms to Track Brain States Under General Anesthesia.”
- This invention was made with government support under DP1 OD003646, DP2-OD006454, and K25-NS05758 awarded by the National institutes of Health. The government has certain rights in the invention.
- The present invention relates to systems and methods for tracking brain states of a patient under anesthesia and, more particularly, to systems and methods for correlating anticipated effects of a given anesthetic compound administered to a patient with characteristics of the patient's brain state during the administration of the given anesthetic compound to more accurately track the effects of the given anesthetic compound and the actual brain state of the patient.
- Since 1846 and the first public uses of ether as a means to control pain during surgical procedures, anesthesia, analgesics, and other administered compounds to control pain have been a mainstay of medicine. However, while the use of the anesthetic and the number of compounds with anesthetic properties in clinical use have grown astronomically since the initial uses of ether, the scientific understanding of the operation of the body when under anesthesia is still developing. For example, a complete understanding of the effects of anesthesia on patients and operation of the patient's brain over the continuum of “levels” of anesthesia is still lacking. As such, anesthesiologists are trained to recognize the effects of anesthesia and extrapolate an estimate of the “level” of anesthetic influence on a given patient based on the identified effects of the administered anesthesia.
- Unfortunately, there are a great number of variables that can influence the effects, effectiveness, and, associated therewith, the “level” of anesthetic influence on a given patient. Obvious variables include physical attributes of the patient, such as age, state of general health, height, or weight, but also less obvious variables that are extrapolated, for example, based on prior experiences of the patient when under anesthesia. When these variables are compounded with the variables of a given anesthesiologists' practices and the variables presented by a particular anesthetic compound or, more so, combination of anesthetic compounds, the proper and effective administration of anesthesia to a given patient can appear to be an art and a science.
- Therefore, it would be desirable to have a system and method for reducing the unpredictability of administering anesthetic compounds to patients. More particularly, it would be desirable to have systems and methods that aid an anesthesiologists or other clinician in recognizing, reducing, and/or controlling the number of variable presented to the clinician when administering anesthetic compounds to patients.
- The present invention overcomes the aforementioned drawbacks by providing a system and method for determining the state of a patient's brain under anesthesia using readily-available monitoring information, such as from a patient's electroencephalography (EEG). The present invention recognizes that anesthetic compounds induce different signatures in physiological characteristics of the patient under anesthesia and aids interpretation of such information. Using the physiological characteristics and signatures associated with the selected anesthetic compound, the present invention aids the correlation of the physiological characteristics and signatures to a state of the patient's brain.
- In accordance with one aspect of the present invention, a system for monitoring a patient experiencing an administration of at least one drug having anesthetic properties is disclosed. The system includes a plurality of sensors configured to acquire physiological data from the patient and at least one processor. The processor is configured to assemble the physiological data into sets of time-series data associated with an origin location of the patient, transform each set of time-series data into a spectrum information, and determine coherence information with respect to the associated origin locations associated with the time-series of data. The processor is further configured to identify signatures within at least one of the spectrum information and the coherence information indicative of at least one of a current state and a predicted future state of the patient and generate a report using the signatures including information regarding at least one of the current state and the predicted future state of the patient induced by the drug.
- In accordance with another aspect of the present invention, a system for monitoring a patient experiencing an administration of at least one drug having anesthetic properties is disclosed. The system includes a plurality of sensors configured to acquire physiological data from the patient, a user interface configured to receive an indication of at least one of a characteristic of the patient and the at least one drug having anesthetic properties, and at least one processor. The processor is configured to identify signature profiles indicative of at least one of a current state and a predicted future state of the patient based on the indication and assemble the physiological data into sets of time-series data. The processor is further configured to analyze the sets of time-series data using the identified signature profiles and generate a report including information regarding at least one of the current state and the predicted future state of the patient induced by the drug.
- The foregoing and other advantages of the invention will appear from the following description. In the description, reference is made to the accompanying drawings which form a part hereof, and in which there is shown by way of illustration a preferred embodiment of the invention. Such embodiment does not necessarily represent the full scope of the invention, however, and reference is made therefore to the claims and herein for interpreting the scope of the invention.
-
FIG. 1 is a schematic illustration of a system for determining the state of a patients brain under anesthesia in accordance with the present invention. -
FIG. 2 is a flow chart setting forth the steps of a method for determining the state of a patient's brain under anesthesia in accordance with the present invention -
FIG. 3A is a series of spectrograms acquired under different drug or patient characteristics. -
FIG. 3B is a spectrogram and associated EEG waveforms showing the overlapping influence of different drugs administered to a patient. -
FIG. 4 is a series of EEG waveforms collected to illustrate variations therein that can be observed as corresponding with respective patient states. -
FIG. 5 is a collection of data readouts including EEG waveforms, a frequency analysis, and a spectrogram illustrating key markers within the data and reflected in each data readout. -
FIG. 6 is a collection of spectrograms of the radial current density estimated at each of a plurality of electrode sites. -
FIG. 7 is a collection of EEG waveforms and spectrograms illustrating key markers within the data and reflected in each data format. -
FIG. 8 is a set of graphs, phase-amplitude histograms, and EEG waveforms illustrating phase-amplitude analysis as a mechanism for determining and predicting future patient states. -
FIG. 9 is a collection of EEG waveforms and spectrograms illustrating key markers within the data and reflected in each data format. -
FIGS. 10-15 are graphs, each figure corresponding to a different drug, that illustrate the ability to create “spectral templates” for each of a plurality of exemplary drugs, which can be used in accordance with the present invention. - The present invention recognizes that anesthetic compounds induce different signatures in physiological characteristics of the patient under anesthesia and aids interpretation of physiological characteristics and signatures therein based on a selected anesthesia compound. Using the physiological characteristics and signatures associated with the selected anesthesia compound, the present invention aids the correlation of the physiological characteristics and signatures to a state of the patient's brain.
- For example, turning to
FIG. 1 , asystem 10 configured for use in accordance with the present invention includes apatient monitoring device 12, such as a physiological monitoring device, illustrated inFIG. 1 as an electroencephalography (EEG) electrode array. However, it is contemplated that the patient monitoring device may also include mechanisms for monitoring galvanic skin response (GSR), for example, to measure arousal to external stimuli. One specific realization of this design utilizes a frontal Laplacian EEG electrode layout with additional electrodes to measure GSR. Another realization of this design incorporates a frontal array of electrodes that could be combined in post-processing to obtain any combination of electrodes found to optimally detect the EEG signatures described earlier, also with separate GSR electrodes. Another realization of this design utilizes a high-density layout sampling the entire scalp surface using between 64 to 256 sensors for the purpose of source localization, also with separate GSR electrodes. - The
patient monitoring device 12 is connected via acable 14 to communicate with amonitoring system 16. Also,cable 14 and similar connections can be replaced by wireless connections between components. As illustrated, themonitoring system 18 may be further connected to adedicated analysis system 18. Also, themonitoring system 16 andanalysis system 18 may be integrated. - For example, as noted above, it is contemplated that the
patient monitoring device 12 may be an EEG electrode array, for example, a 64-lead EEG electrode array. However, as will be apparent below, greater spatial accuracy can be achieved by increasing the number of electrodes from 64 to 128, 256, or even higher. Similarly, the present invention can be implemented with substantially less electrodes. In any case, themonitoring system 16 may be configured to receive raw signals acquired by the EEG electrode array and assemble, and even display, the raw signals as EEG waveforms. Accordingly, theanalysis system 18 may receive the EEG waveforms from themonitoring system 16 and, as will be described, analyze the EEG waveforms and signatures therein based on a selected anesthesia compound, determine a state of the patient based on the analyzed EEG waveforms and signatures, and generate a report, for example, as a printed report or, preferably, a real-time display of signature information and determined state. However, it is also contemplated that the functions ofmonitoring system 16 andanalysis system 18 may be combined into a common system. - Referring to
FIG. 2 , a method for analysis and reporting in accordance with the present invention begins atprocess block 200 with the selection of a desired drug, such as anesthesia compound or compounds, and/or a particular patient profile, such as a patient's age height, weight, gender, or the like. Such selection may be communicated through auser interface 20 ofFIG. 1 . Furthermore, drug administration information, such as timing, dose, rate, and the like, in conjunction with the above-described EEG data may be acquired and used to estimate and predict future patient states in accordance with the present invention. As will be described, the present invention recognizes that the physiological responses to anesthesia vary based on the specific compound or compounds administered, as well as the patient profile. For example, elderly patients have a tendency to show lower amplitude alpha power under anesthesia, with some showing no visible alpha power in the unconscious state. The present invention accounts for this variation between an elderly patient and a younger patient. Furthermore, the present invention recognizes that analyzing physiological data for signatures particular to a specific anesthetic compound or compounds administered and/or the profile of the patient substantially increases the ability to identify particular indicators of the patient's brain being in a particular state and the accuracy of state indicators and predictions based on those indicators. - For example, the following drugs are examples of drugs or anesthetic compounds that may be used with the present invention: Propofol, Etomidate, Barbiturates, Thiopental, Pentobarbital, Phenobarbital, Methohexital, Benzodiazepines, Midazolam, Diazepam, Lorazepam, Dexmedetomidine, Ketamine, Sevoflurane, Isoflurane, Desflurane, Remifenanil, Fentanyl, Sufentanil, Alfentanil, and the like. However, the present invention recognizes that each of these drugs, induces very different characteristics or signatures, for example, within EEG data or waveforms. For example,
FIG. 15 provides EEG data for one prominent drug, propofol, and associated states. - More particularly, referring to
FIG. 3A , a plurality of spectrograms corresponding, as labeled, to patients having been administered Propofol, Dexmedetomidine, Sevoflurane, and Ketamine are illustrated. In addition,FIG. 3A shows a spectrogram of an elderly patient. When placed in proximity to one another, it is clear that the spectrograms vary substantially, so as to be visually distinct, based on the administered drug and/or patient profile. This is particularly true, for example, when multiple drugs are combined, such as illustrated inFIG. 3B . As will be explained, the present invention recognizes the substantial variation in physiological data acquired from a patient and the signatures contained therein. Based on a selected drug or drugs and/or the patient profile and, by faking this recognition into account, the present invention provides systems and methods for tracking brain states during the administration of anesthesia that is greatly improved over traditional systems. A summary of exemplary “spectral templates” for each of a plurality of exemplary drugs is provided in the “examples” section. These “spectral templates” can be used to automatically identify a current or project a future state of the patient. - With the proper drug or drugs and/or patient profile selected, acquisition of physiological data begins at
process block 204, for example, using a system such as described with respect toFIG. 1 , where the acquired data is EEG data. Referring toFIG. 4 , a series of EEG waveforms in the time domain are illustrated. As is clear in a side-by-side comparison such as illustrated inFIG. 4 , these EEG waveforms vary appreciably. For example, general categories of “awake” 400, “asleep” 402, and under “general anesthesia” 404 can be readily created, in the side-by-side comparison with the associatedcategory titles category - To do so, rather than evaluate physiological data in the abstract, at
process block 206, the physiological data is processed. Processing can be done in the electrode or sensor space or extrapolated to the locations in the brain. As will be described, the present invention enables the tracking of the spatiotemporal dynamics of the brain by combining spectrogram and global coherence analyses. As will be apparent, reference to “spectrogram” in may refer to a visual representation of frequency domain information, such as represented in, for example,FIG. 3A . However, a “spectrogram” within the context of the present invention need not be visually represented or displayed. Rather, within the context, for example, of processing and report generation, the spectrogram may be an intermediate processing step from which reports or visual representations are ultimately created. For example, referring toFIG. 5 , EEG waveforms in thetime domain 500 have aspectrum 502 and can be translated into aspectrogram 504. However, rather than simply analyzing the spectrum information of thespectrogram 504 or a givenspectrum 502 in the abstract, Laplacian referencing can be performed at process block 208 to estimate radial current densities perpendicular to the scalp at each electrode site of, for example, themonitoring device 12 ofFIG. 1 . Accordingly, though “spectrogram” processing is performed, a visual representation of the spectrogram need not be displayed. - Surface Laplacian calculations can be determined by taking a difference between voltages recorded at an electrode site and an average of the voltage recorded at the electrode sites in a local neighborhood. Denoting the voltage recorded at the ith electrode relative to a reference electrode located close to the top of the head as Vi(t), the surface Laplacian of Vi(t) can be estimated as:
-
- where Vi m(t) denotes the voltage recording at the mth closest electrode to electrode i. Thus, the EEG recorded at a particular location was locally referenced to an average of the EEG recorded at the neighbors. The choice of M depended on the nearest electrodes to ith electrode and on their locations' symmetry with respect to electrode. For the electrode on the top of the head, which had six symmetrically distributed nearest electrodes, M=6. For the remaining electrodes it is possible to find four or five neighbors that are arranged in an approximately symmetric configuration, in this case, M can be chosen to be equal to 4 or 5, respectively. For the electrodes at the edge, for which such a symmetric configuration cannot be approximated, surface Laplacian can be uncalculated and radial current density estimates not made. Accordingly, more accurate estimates of radial current density can be computed by increasing the number of electrodes and by accounting for the curvature of the head in the neighborhood of each electrode site.
- From these current density estimates, at
process block 210, spectrograms at each electrode site are computed. Referring toFIG. 6 , spectrograms of the radial current density estimated at each of a plurality of electrode sites are illustrated. As explained above, the spectrograms reflect, as illustrated inFIG. 7 , key signatures commonly identified or tracked in the time domain EEG waveforms. Specifically, two time domain EEG waveforms and associated spectrograms are illustrated, oneset 700 corresponding to light Dexmedetomine sedation and oneset 702 corresponding to deep Dexmedetomine sedation. In theset 700 corresponding to light Dexmedetomine sedation,spindles 704 are visible and in theset 702 corresponding to deep Dexmedetomine sedation, strongslow wave oscillations 706 are visible. - It is noted that the local referencing is preferable so that distinct temporal patterns in the spectrogram at different electrode sites can be identified. This is in contrast to an average or single electrode referencing scheme, which would lead to an erroneous conclusion that approximately the same temporal pattern was present in the spectrogram at each electrode site.
- The spectrum of the surface Laplacian at the location of the ith electrode site is estimated by averaging over K non-overlapping segments:
-
- where
-
- is the mean corrected Fourier transform of the current density estimate at electrode site i of segment k at frequency f, and Xi k(f)* is the complex conjugate.
- To characterize the coordinated activity in the time-series of acquired data, an eigenvalue decomposition can be computed of a cross-spectral matrix at each spectral frequency as a function of time. Specifically, at
process block 212, spectral and eigenvalue-based global coherence analyses can be used to track the spatiatemporal dynamics of the brain's anesthetic state. Generally, for the example of propofol, the global coherence analysis detects strong coordinated a activity in the occipital sites when the patient is awake that shifts to the frontal sites when the patient becomes unconscious. - In particular, method-of-moments estimates of the i*n element of the cross-spectral matrix at a frequency f can be computed as:
-
- where Xi k(f) and Xj k(f) are the tapered Fourier transforms of the current density estimates from electrode sites i and j, respectively, at frequency f. For N locations, Cx(f) is an N×N matrix of cross-spectra.
- An orthogonal basis can be obtained by performing a Karhunen-Loeve transform at each frequency, f:
-
Y k(f)=U(f)H X k(f) Eqn. 4; - where U(f)H is the adjoint of the matrix U(f) (that is, the complex conjugate transpose of U(f), U(f)H=(U(f)*)*)T and a unitary matrix (that is, U(f)U(f)H=1). U(f)H is chosen so that under the Karhunen-Loeve transform the cross-spectral matrix in the new basis:
-
- is diagonal (that is, Cij Y(f)=Cij Y(f)δij. This implies that the diagonal elements of CY(f)=Si Y(f), where
-
- is the ith eigenvector, and the ith column of U(f) is the normalized eigenvector satisfying Σj N=1|Uji(f)|2=1. Therefore, |Uji(f)|2 contains the contribution from the ith electrode to the ith eigenvector. The matrix whose jth and ith element is |Uji(f)|2 is termed the weighting matrix.
- Sorting the eigenvalues, S1 Y(f)≧S2 Y(f)≧ . . . ≧SN Y(f), largest eigenvalue to the sum of eigenvalues is:
-
- This ratio is called the global coherence. When the leading eigenvalue is large compared with the remaining ones, CGlobal(f) is close to 1. In this case, examining the contributions of different sites to the corresponding eigenvector by using the elements of the weight matrix provides a summary of coordinated activity at this frequency. These elements are row weights. The row weights can be obtained by the absolute value square of the elements of the row of U(f)H, which leads to the eigenvector with the highest eigenvalue.
- Estimates of the cross-spectral matrix as described above can be sensitive to noise. To make cross-spectral estimation more robust, the median can be used in place of the mean in
equation 5. The median is a robust estimator of centrality, and is much less sensitive to outliers than the mean. - Thus, process blocks 208-212 yield two pieces of valuable information, namely, the spectrogram and global coherence information, which show different spatiotemporal activity at different states of the patient receiving anesthesia. For example, for propofol, when patients are awake, the spectrograms will show strong occipital α activity. After loss of consciousness, the spectrograms will show a loss of α activity and an increase in δ activity in the occipital sites and strong α and δ activity in the frontal sites, increased power in the α, β, and δ ranges in the frontal sites will occur after loss of consciousness, consistent with the well-known pattern of anteriorization. As patients lose responsiveness, the coordinated activity over the occipital sites in the α range diminish. When patients are unconscious, strong coordinated activity in the α range is observed broadly over the frontal electrode sites at which the spectrograms show the anteriorization pattern. Despite the overall high δ activity in the spectrograms, coordinated activity may only be observed in the α range. The relative power in the occipital α and δ ranges reliably track the patients' behavioral responses. For propofol, the occipital α power is greater than the δ power when the patient is awake, and the reverse is true when the patients are unconscious. The strong global coherence in the α range indicates highly coordinated activity in the frontal electrode sites. Thus, global coherence and weight matrices along with spectrograms provide a first level of data for determining a current state and predicting a future state of a patient's brain under anesthesia. Further details regarding initial testing and validation of such processes are provided in Cimenser A, Purdon P L, Pierce E T, Walsh J L, Salazar-Gomez A F, Harrell P G, Tavares-Stoeckel C, Habeeb K, Brown E N (2011) Tracking brain states under general anesthesia by using global coherence analysis. Proceedings of the National Academy of Sciences of the United States of America 108:8832-8837.
- At
process block 214, phase-amplitude analysis is performed that considers the amplitude of a given signal with respect to the phase of other signals and vice versa. As explained above, spectral analysis of EEG recordings allows the present invention to track systematic changes in the power in specific frequency bands associated with administration of anesthesia, including changes in δ (1-4 Hz), θ (5-8 Hz), α (8-14 Hz), β (12-30 Hz), and γ (30-80 Hz). However, spectral analysis treats oscillations within each frequency band independently, ignoring correlations in either phase or amplitude between rhythms at different frequencies. - Power spectral measures are invariant with respect to changes in the complex phase of a signal's Fourier transform. It is thus natural to extend power spectral analysis by using measures that are sensitive to signal phase. Bispectral analysis can detect the presence of correlation in the phases of oscillation at different frequencies. Bispectrum-based statistics have been used in quantitative clinical depth-of-anesthesia monitors, in a manner that compares the bispectrum across broad low- and high-frequency ranges.
- However, in accordance with the present invention, instead of a traditional “cross-frequency correlation”, phase-amplitude analysis, is used, in phase-amplitude analysis, the amplitude or envelope of activity in one frequency band is consistently largest at a particular phase of a lower frequency rhythm. For example, given two non-overlapping frequency bands, then in phase-amplitude coupling, the amplitude of the activity in the higher frequency band is consistently highest at a particular phase of the lower frequency rhythm. In accordance with the present invention, an analysis can be performed to measure phase-amplitude coupling in a time-resolved fashion to identify at least two distinct modes of phase-amplitude coupling corresponding to shallow and deep planes of anesthesia, respectively.
- Specifically, to characterize coupling between the phase of the slow oscillation (SO; 0.1-1 Hz) and the amplitude of α (8-14 Hz) oscillations, a time-varying phase-amplitude modulogram M(t, φ) can be created that describes the relative α (or other) amplitude at a particular phase at each SO cycle.
- Given an EEG signal, x(t), sampled at rate Fs=250 Hz, ultra-low-frequency drift is removed by subtracting a least-square errors spline fit to the signal with one knot for every 2 minutes (or other selected duration) of data. Next, a band-pass filter may be applied to extract the rhythmic component within each frequency band of interest, xb(t),bε{α,SO}. Symmetric finite impulse response filters designed using a least-squares approach (SO: passband 0.1-1 Hz, transition bands 0.085-0.1 and 1-1.15 Hz, ≧17 dB attenuation in stop bands, order 2207 at 250 Hz; α: passband 8-13.9 Hz, transition bands 5.9-8 and 13.9-16 Hz, ≧60 dB attenuation in stop bands, order 513) can be employed. A discrete Hilbert transform can be used to compute the complex analytic signal, zb(t), satisfying Re[zb(t)]=Xb(t). The analytic signal provides the instantaneous α amplitude A(t)=|zα(t)| and SO phase, Ψ(t)=arg[zSO(t)].
- The modulogram is computed by assigning each temporal sample to one of, for example, 18 equally spaced phase bins based on the instantaneous value of ψ/(t), then averaging the corresponding values of A(t) within, for example, a 2-minute epoch:
-
- where δ(t)=120 s and δφ=2π/18. Note that ∫−π πM(t,φ)dφ=1, so that M(t,φ) is a normalized density of a amplitude over all SO phases.
- For example,
FIG. 8 illustrates two distinct patterns of phase-amplitude modulation. Namely, a first phase-amplitude modulation is similar to slow wave sleep (peak-max—i.e., high-frequency activity is highest at the peak of the low-frequency oscillation, corresponding to a low-frequency phase of 0), and a second phase-amplitude modulation foreshadows the return of consciousness (trough-max—i.e., high-frequency activity is highest at the trough of the low-frequency oscillation, corresponding to a low-frequency phase of +/−π). Slow oscillation phase modulates alpha/beta (8-14 Hz) amplitude, in relation to probability of response, can be studied and is reflected inFIG. 8 . Specifically, a group behavioral curves 802 show the probability of response to click and verbal stimuli during induction in thefirst graph 804 and emergence in thesecond graph 806. A set of phase-amplitude histograms 808 show the relationship between the slow oscillation phase (y-axis, shown with reference sinusoid) and mean-normalized alpha/beta amplitude as a function of time (x-axis) relative to LOG 810 andROC 812. A trough-max phase-amplitude relationship can be observed at the LOC/ROC transition points, where the amplitude of alpha is maximal at the slow oscillation troughs. A peak-max phase-amplitude relationship can be observed during the unconscious state, where the amplitude of alpha is maximal at slow oscillation peaks. These trough-max and peak-max modulation patterns can be observed in raw EEG traces 814, which shows the trough-max and 816, which shows the peak-max, respectively. The trough-max pattern re-appears during emergence prior to ROC, illustrating that it can be used to predict when patients are able to regain consciousness during anesthesia. - For example, because the transition to the trough-max pattern occurs reliably before return of consciousness, the trough-max relationship to predict when patients are likely to recover consciousness while emerging from anesthesia, in cases where trough-max modulation is absent, due to pathology that impairs alpha waves, or drug choice (e.g., sevoflurane), or where electrode placement makes detection of trough-max modulation difficult, the absence or loss of peak-max modulation could also be used to predict recovery of consciousness during emergence. More particularly, during emergence from propofol anesthesia, the peak-max modulation relationship between the phase of the slow oscillation and higher frequencies changes to the trough-max modulation relationship, and does so prior to the return of consciousness, with little change to the underlying power spectrum. The trough-max modulation has a frontal distribution, whereas the peak-max modulation is distributed approximately uniformly across the scalp.
- Thus, such phase-amplitude information can provide a reliable indicator of a current or probable future patients state. For example, since the peak-max modulation pattern represents a state of unconsciousness that is more profound than that observed during trough-max modulation, but less profound than burst-suppression, the peak-max modulation pattern could be used as a target for maintenance of a surgical level of anesthesia. Because the trough-max modulation represents a state of unconsciousness that is less profound than the peak-max modulation, one where patients can respond to external stimuli, the trough-max modulation pattern could be used as a target for maintenance of sedation. Furthermore, as described above, elderly patients often exhibit diminished alpha waves or a lack of alpha waves. Hence, in elderly patients who show diminished alpha waves, or who lack alpha waves, monitoring can also be performed by calculating slow oscillation phase-amplitude modulation across a broad-band frequency range including theta, alpha, beta, and gamma bands.
- To quantify the strength of modulation, a modulation index, MI(t) can be defined, as the Kullback-Leibler divergence, in bits, between M (t, φ) and a uniform phase distribution over the interval (−π, π):
-
- It is noted that the power spectrum and phase-amplitude coupling may be complementary sources of information about brain dynamics. Thus, a combination of both measures may reveal greater structure than either analysis alone.
- For example, the EEG power spectrum during gradual administration of anesthesia shows a broad-band peak that begins in gamma frequencies, and decreases in frequency and bandwidth into the low-beta and alpha bands with increasing doses of anesthesia resulting in loss of consciousness. The gamma and beta range effects are associated with a reduced probability of response to external stimuli. Power within this traveling peak is strongest in frontal EEG channels. This reverses after recovery of consciousness. The traveling peak frequency can be quantified, for example, as the median between 2 and 40 Hz, and calculate the bandwidth using the interquartile range between the same limits.
- It is further noted that the above-described phase-amplitude modulation effect and systems and methods for monitoring thereof is best observed using a local average of several electrodes, such as the surface Laplacian. Otherwise, phase-amplitude modulation effect can be poorly resolved or not observable.
- In accordance with one configuration of the present invention, a beamforming procedure may be used to improve estimation of phase-amplitude modulation. Let x(t):=[x1(t), x2(t), . . . , xN(t)]T denote the EEG time-series corresponding to N EEG channels for
time 0≦t≦T. Let α(t):=[α1(t), α2(t), . . . , αN(t)]T and s(t):=[s1(t), s2(t), . . . , sN(t)]T denote the alpha rhythm and slow oscillation time-series which are obtained by band-pass filtering x(t) in the frequency bands of 8-14 Hz and 0.1-1 Hz, respectively. Let αH(t):=α(t)+iH(α(t)) and sH(t):=s(t)+iH(s(t)), where H(•) is the discrete-time Hilbert transform. The amplitude of the alpha rhythm is modulated by the phase of the slow oscillation during anesthesia, based on an analysis of single-channel Laplacian-derived EEG. - Assuming that the phase-amplitude modulation arises from a unified and possibly spatially localized mechanism in the brain, the problem reduces to reconstructing a single phase-amplitude modulation relationship based on the observation through the multi-channel array of EEG sensors.
- A viable solution is given by beamforming. The idea of beamforming is to form a scalar signal based on the array observations in order to minimize an appropriate cost function representing the underlying system model. Let w:=[w1, w2, . . . , wN]T denote a weight vector (beamforming vector) and consider the corresponding projection of the alpha rhythm and slow oscillation time series given by αW(t):=wT αH(t), and sW(t):=wT sH(t), respectively. The amplitude of αW(t) and the phase (argument) of sW(t) are given by:
-
A W(t):=√{square root over ((Σk=1 N w kαk(t))2+(Σk=1 N w k H k(αk(t)))2)}{square root over ((Σk=1 N w kαk(t))2+(Σk=1 N w k H k(αk(t)))2)} Eqn. 9; -
and -
θw(t):=arg(Σk=1 N w k s k(t)+Σk=1 N w k H(s k(t))) Eqn. 10; - respectively. Suppose that for a given value of the phase of sW(t), denoted by θ, the amplitude of the alpha rhythm AW(t) has a distribution given by the density pw(A; θ). Then, the phase-amplitude modulation relation is defined as:
-
A W(θ;t):=E pw {A w(t)|θ} Eqn. 11; - where the ensemble averaging Epw is with respect to the density pw(A; θ). The function A(θ;t) is clearly periodic with the full period defined as [−π, π]. Assume that A(θ:t) is stationary during the observation period [0, T] and hence drop the dependence on t. Assuming that the function AW(θ) has sufficient smoothness properties, it can be represented in the Fourier basis as follows:
-
A W(θ)=ω+Σk=1 ∞αk sin(kθ)+b k cos(kθ) Eqn. 12; - where μ, αk, and bk denote the expansion coefficients. A suitable model for estimating AW(θ) is given by its truncated Fourier expansion to the first L terms, with L≦3. This reduced-order model enforces a smooth phase-amplitude modulation relation, which is consistent with empirical observations. A suitable cost function for estimating AW(θ) is given by the following quadratic form:
-
R(μ{αk ,b k };w):=∫k=1 L {A w(θ)−μ−ΣK=1 L(αk sin(kθ)+b k cos(kθ))}2 p(θ)dθ Eqn. 13. - Since the densities pw(A; θ) are unknown, it is not possible to compute Aw(θ;t):=Ep
w {Aw (t)|θ}. Hence, an empirical quadratic cost function can be used for estimating AW(θ) by substituting the ensemble averaging operator Epw by the corresponding temporal averaging as follows: -
R(μ{αk ,b k };w):=∫ . . . π π {E T {A w(t w(θ))}−μ−ΣK=1 L(αk sin(kθ)+b k cos(kθ))}2 p(θ)dθ Eqn. 14; - where tw(θ) denotes the inverse function of θw(t), p(θ) is the prior distribution of the slow oscillation phase and Et denotes temporal averaging.
- Note, replacing ensemble averaging by temporal averaging implicitly assumes the ergodicity of the underlying processes during the observation period [0, T]. Since the prior p(θ) is unknown, the cost function can be further approximated by substituting the ensemble averaging over θ by the corresponding temporal averaging as follows:
-
- For a given beamformer w, it is possible to minimize the cost function over the parameters μ, αk, and bk. Then, the best such beamformer can be chosen by minimizing the resulting cost function over w. This, in fact, corresponds to a cost minimization formulation for estimating the reduced-order phase-amplitude modulation relation that is most consistent with the data (in the sense of the above quadratic cost function). Assuming that the beamformer elements are bounded as w≦wk≦
w , for some constants w andw , the overall optimization procedure can be expressed as: -
minwminμ,αk ,bk {circumflex over (R)} T(μ{αk ,b k }′w)s.t.w≦w k≦w ,∀k Eqn. 16. - The inner minimization can be carried out by linear regression and the resulting solution can be expressed explicitly in terms of Aw(t) and θw(t). The outer minimization can be performed using standard optimization routines. In particular, since the constraints on wk form a convex set, the interior point method for the outer minimization stage can be employed.
- Under the above-described process and with respect to, for example, propofol, it can be shown that the phase-amplitude modulation of frontal EEG under anesthesia undergoes two different patterns of modulation, corresponding to depth of anesthesia. The first pattern, occurring before and after the loss of consciousness, consists of maximum alpha amplitude occurring at the trough (surface-negative) of the slow oscillation, which can be referred to as the “trough-max” pattern. At deeper levels of anesthesia, the relationship reverses and maximum alpha amplitude occurs at the peak (surfacepositive) of the slow oscillation, which can be referred to as the “peak-max” pattern. In order to compute the electrode weights that would show both modes of the phase-amplitude modulation most clearly, equal-length segments of data from both modes were chosen and used to compute the optimal weights for each mode. These trough-max and peak-max data for the two patients were used to perform the averaging described in
equation 15. The data used in the optimization consisted of four-minute segments, chosen as periods during which the phase-amplitude modulation was relatively constant, based on phase-amplitude histograms computed using Lapiacian-referenced data. Table I illustrates modulation depths for different methodologies. -
Patient 1Patient 2Trough-Max Peak-Max Trough-Max Peak-Max Bipolar 0.26 0.39 0.22 0.76 Laplacian 1.08 0.73 0.98 1.00 Optimized 1.23 0.91 1.33 1.67 - The beamforming method produced the largest modulation depth, followed by the Laplacian method, with bipolar referencing showing the lowest modulation depth in both regimes. Thus, The beamforming method provides a means to obtain electrode weights that minimize the least-squares error in a parametric sinusoidal model of the phase-amplitude relationship. This optimal weighting of EEG electrodes allows for improved detection of phase-amplitude modulation across time and patients. This method could be useful in studies of phase-amplitude modulation in the EEG under anesthesia, as well as other conditions where this phenomenon might arise.
- The above-described selection of an appropriate analysis context based on a selected drug or drugs (process block 200), the acquisition of data (process block 204), and the analysis of the acquired data (process blocks 206-214) set the stage for the new and substantially improved real-time analysis and reporting on the state of a patient's brain as an anesthetic or combination of anesthetics is being administered and the recovery from the administered anesthetic or combination of anesthetics occurs. That is, although, as explained above, particular indications or signatures related to the states of effectiveness of an administered anesthetic compound or anesthetic compounds can be determined from each of the above-described analyses (particularly, when adjusted for a particular selected drug or drugs), the present invention provides a mechanism for considering each of these separate pieces of data and more to accurately indicate and/or report on a state of the patient under anesthesia and/or the indicators or signatures that indicate the state of the patient under anesthesia.
- Specifically, referring to process block 216, any and ail of the above-described analysis and/or results can be reported and, in addition, can be coupled with a precise statistical characterizations of behavioral dynamics. That is, behavioral dynamics, such as the points of loss-of-consciousness and recovery-of-consciousness can be precisely, and statistically calculated and indicated in accordance with the present invention. To do so, the present invention may use dynamic Bayesian methods that allow accurate alignment of the spectral and global coherence analyses relative to behavioral markers.
- To build the information needed to achieve this, a study was performed to correlate the behavior marks with EEG information. With respect to the points of loss of consciousness and recovery of consciousness and any other desired points, three possible behavioral outcomes may be defined: correct responses, incorrect responses, and no response. A state-space model with two state variables representing a probability of response and a conditional probability of correct response can be used to correlate outcomes with a predicted future state. Probability densities of model parameters and the response probability are computed within a Bayesian framework to provide precise statistical characterizations of behavioral dynamics.
- For example, assume the experiment consists of K stimulus trials. On any trial these are three possible outcomes for the response to the verbal stimulus; the subject may respond correctly, respond incorrectly or not respond. Let mk=1 if the subject responds on trial k and 0 otherwise. If there is a response on trial k, let nk=1 if it is correct and 0 otherwise. Let pk denote the probability of a response on trial k, i.e. that nk=1, and let qk denote the probability of a response of a correct response, i.e. mk=1. The observed data at trail k is the pair (mk,nk) which can assume the values {(1,1), (1,0), (0,0)}. The observation model at trail k is therefore:
-
Pr(m k ,n k |p k ,q k)=[p k(q k)1-nk ]mk (1−p k)1-mk Eqn. 17; - Where we define pk and qk in terms of the cognitive state variables by the logistic relations:
-
p k=[1+exp(−x k)]−1 Eqn. 18; -
q k=[1+exp(−z k)]−1 Eqn. 19. - We define state model for the unobservable cognitive state variables as the random walk equations:
-
X k =X k-1+εk Eqn. 20; -
Z k =Z k-1+ηk Eqn. 21; - Where εk and ηk are zero-mean Gaussian random variables with variances σε 2 Δk and σn 2 Δk where Δk is the time elapsed between verbal stimulus trials k−1 and k.
- Formulating the probability of response and the conditional probability of a correct response on each trial as a logistic function of the cognitive state variable ensures that these probabilities are properly defined between 0 and 1. The state model provides a continuity constraint so that the current cognitive state and hence, the probability of a response and the conditional probability of a correct response depend on the previous cognitive state and experience. We let θ={σε 2, σn 2, x0, z0} denote the unknown parameters to be estimated.
- We can use the same logic presented for the verbal stimuli to develop a state-space model for the clicks. The only exception is we rewrite the observation equation to follow for more than one click stimulus presentation per trial, in fact, for each verbal stimulus, there are four click stimuli. Hence the observation model for the clicks is:
-
- Where mk=0,1,2,3,4 is the number of responses and nk=0,1,2, . . . , mk is the number of correct responses to the click stimuli, otherwise pk,qk,xk and zk are defined exactly as they were defined for the verbal state-space model. These are six possible outcomes in any click trial block. The unknown parameters are again θ={σε 2,σn 2,x0,z0}. Using both verbal and click stimuli allow us to determine the relevance of stimulus saliency in defining loss and recovery of consciousness.
- Our objective is to develop a Bayesian procedure for state and model parameter estimation. We assume that there is a 2-dimensional state-space model for the verbal responses and a separate 2-dimensional state-space model for the verbal responses and a separate 2-dimensional state-space model for the click stimuli. We denote the unobserved state as X=(xv,1, . . . , xv,K, zv,1, . . . , zv,K, xc,1, . . . , xc,K, zc,1, . . . , zc,K), the model parameters as Θ=(θv,θc) and the observed data as M=(mv,1, . . . , mv,K, nv,1, . . . , nv,K, mc,1, . . . , mc,K, nc,1, . . . , nc, K), where the subscripts v and c have been added to denote the verbal and click components of the model respectively. If we assume that f (θ) is a prior distribution for (θ), then by Bayes' rule the posterior distribution for the parameters and the state is:
-
- The observation models (Eqs. 17-19, 22) define f (M|X,Θ) and the state-space models (Eqs. 20-21) define f(X|,Θ) and f (M) is the normalizing constant. To specify f(Θ) we chose the independent prior distributions for xv,0, zv,0, xc,0 and zc0 to be uniform distributions each on the interval [0,100]. For each of the variance parameters σε,v 2, Zv,0, Xc,0 and σn,c 2 we take as the prior distribution independent inverse gamma distributions with parameters α=5 and λ=1.
- We need the WinBUGS software to compute by Bayesian Monte Carlo methods the posterior densities f(X,Θ|M) and the marginal posterior densities of the form:
-
f(x c,k|M)∫∫Θ f(X,Θ|M)dΘdX [c.k]. Eqn. 24 - where the inner integral is over the components of all values of Θ and the outer integral is over ail components of X excluding Xc,k. We computed the comparable marginal posterior densities for Zc,k, Xv,k, Zv,k and for each component of Θ. We report the median of each marginal posterior density as the estimate of a given state at a particular trial and a given parameter. We report that the uncertainty in any state or parameter estimate as the 95% or 90% credibility interval based on the Monte Carlo samples. The posterior densities were computed using 100,000 iterations after a 20,000 iteration burn-in period. A Bayesian analysis implementation such as described in A. C. Smith, S Wirth, W. A. Suzuki and E. N. Brown, Bayesian analysis of interleaved learning and response bias in behavioral experiments,” Journal of Neurophysiology, vol 97, pp. 2516-2524, and incorporated herein by reference, can be utilized.
- The set of free parameters includes all the x0 and ail the σ2. These are computed using the Bayesian approach, which assumes that prior information about the parameters improves the parameter estimates. For all the x0, uniform prior distribution, uniform (a, b), can be chosen. For all the σ2, the conjugate inverse gamma prior distribution, inverse gamma (α,λ) can be chosen. Assuming values of 0 and 100 for a and b respectively to reflect the fact that the patient's behavior markers correlates perfectly at the beginning of tracking, α and λ can be chosen to be 5 and 1, making the inverse gamma prior distribution non-informative. A Bayesian analysis implementation, such as described in A. C. Smith, S. Wirth, W. A. Suzuki, and E. N. Brown, “Bayesian analysis of interleaved learning and response bias in behavioral experiments,” Journal of Neurophysiology, vol. 97, pp. 2518-2524, 2007, and incorporated herein by reference, can be utilized.
- The result is a report that can be coupled with a precise statistical characterization of behavioral dynamics. That is, behavioral dynamics, such as the points of loss-of-consciousness and recovery-of-consciousness (or other selected states) can be precisely, and statistically calculated and indicated in accordance with the present invention. Specifically, this report can aid clinicians by allowing information that was previously unknown or incapable of being discerned from traditional monitoring systems to be identified and communicated and/or used by the clinician and/or monitoring system to identify particular states of a given patient. The report may serve as part of a “human in-the-loop” operational strategy, whereby the above-described systems automatically detects spectral features of interest and the report serves as a mechanism by which to highlight or communicate the spectral features and the information extrapolated therefrom to inform clinicians of a given or predicted future state and the reasoning therebehind. Also, the report may include topographic maps of the patient's scalp or source localization maps on a brain image rendering to provide information relating to the location within the brain from which the EEG activity is being received.
- For example,
FIG. 9 provides two sets of time domain EEG waveforms and associated spectrograms acquired during clinical use of Propofol. Specifically, afirst set 900 illustrates two time domain EEG waveforms, a first EEG waveform demonstratingdelta oscillation 902 and the second EEG waveform demonstrating delta-alpha oscillation 904. While simultaneously reading and interpreting theseparate EEG waveforms spectrogram 906 displays the same delta oscillation and delta-alpha oscillation, but in form that is able to be more readily interpreted, particularly in real time. Similarly, asecond set 908 illustrates two time domain EEG waveforms, a first EEG waveform demonstrating delta-alpha oscillation 910 and the second EEG waveform demonstratingburst suppression 912. While simultaneously reading and interpreting theseparate EEG waveforms spectrogram 914 displays the same delta-alpha oscillation and burst suppression, but in form that is able to be more readily interpreted in real time. - Accordingly, using the present invention, a substantial amount of new and important signatures that were previously difficult to consider or understand and/or were previously unknown or not understood as reliable without considering drug and/or patient-specific signature information, can be reliably determined and used to track a current state of a patient. For example, in the unconscious state, sevoflurane shows increased slow, delta, theta, and alpha power. Compared to propofol, the increase in theta power is pronounced and visible in the power spectrum. Slow oscillation phase-amplitude coupling shows that high frequency activity is greatest on the rising phase (−π/2) of the slow oscillation. When ketamine is administered, power in beta and gamma bands increase. When it is administered alongside propofol, the two drugs act on the EEG in an antagonistic fashion. If enough ketamine is administered, it reduces or abolishes both slow and alpha power, and increases gamma power. Dexmedetomidine shows increased slow, delta, and sigma (12-16 Hz) power at lower doses consistent with sedation. At higher doses the EEG is dominated by slow oscillations.
- The above-referenced report, as an example with respect to propofol, can indicate, predict, and/or track onset of loss of consciousness and recovery of consciousness based on increased gamma (25 to 40 Hz) and beta (12 to 25 Hz) activity; transition to unconsciousness and in the unconscious state, and recovery of consciousness based on increased/decreased slow (0 to 1 Hz), delta (1 to 4 Hz), theta (4 to 8 Hz), and alpha (8 to 12 Hz) activity; anesthetic drug administration and loss of consciousness/recovery of consciousness based on reduced theta (4 to 8 Hz) power; loss of consciousness and recovery of consciousness associated with changes in the ratio of alpha and delta (1 to 4 Hz) power in the occipital region of the scalp; states of profound unconsciousness by identifying strong global coherence in the alpha band; states of profound anesthesia based on strong association between global coherence and the state of anteriorization; profound unconsciousness based on strong modulation of the theta (4 to 8 Hz), alpha (8 to 12 Hz), beta (12 to 25 Hz), and gamma (25 to 40 Hz) activity by phase of slow and delta oscillations; transition to unconsciousness based on trough-max relationship between phase of slow oscillation and amplitude of alpha rhythm, where alpha amplitude is highest at the troughs (phase=+/−π) of the slow oscillation; profound unconsciousness based on peak-max relationship between phase of slow oscillation and amplitude of alpha rhythm, where alpha amplitude is highest at the peaks (phase=0) of the slow oscillation; and profound states of loss of consciousness based on distinct behavior in the slow oscillation (0.1 to 1 Hz) compared with the delta band (1 to 4 Hz). Of course, many and different metrics, indicators, and signatures can be identified, tracked, and used to determine a patient's state and/or predict a future patient's state.
- As mentioned above, “spectral templates” for each of a plurality of exemplary drugs can be provided and used in accordance with the present invention. Specifically, to create the following “spectral templates,” EEG data was acquired from operating room surgical cases during the administration of anesthesia. The data was separated into three age demographics: young (>35), middle-aged (36-59), and elderly (<60); and by drug: propofol, sevoflurane, isoflurane, dexmedetomidine, and ketamine. Spectrograms from each of the cases were analyzed to identify temporal intervals containing recurring spectral motifs correlated with putative unconscious and deep levels of anesthesia. These intervals were used to compute median spectra for each of the drug/demographic pairings.
- The following relates the results of this analysis. For each of the reported median power spectra from the drug/demographic pairings, the prominent spectral peaks were identified. For each set of spectral peaks, the peak frequency, peak power, and bandwidth were estimated as a function of putative unconscious (darker) and deep (lighter) states of anesthesia. Summary schematics of the state/demographic dynamics were generated along with tables relating the defining features of each spectral peak.
- Referring to
FIG. 10 , propofol has studied. The spectral motifs for propofol included three salient spectral peaks: a low frequency (<1 Hz) oscillation, a traveling peak (spanning gamma through alpha), and a broadband gamma. - Overall dynamics during the transition from putative unconscious to deep states of anesthesia include:
-
- Low Frequency Oscillation:
- The peak frequency remains roughly constant. In some instances, a slight decrease in peak frequency may be seen.
- The peak power increases.
- The bandwidth remains roughly constant.
- Traveling Peak:
- The peak frequency decreases.
- The peak power decreases.
- The bandwidth remains roughly constant.
- Gamma:
- Broadband gamma with no clear spectral peak.
- The power decreases
- Low Frequency Oscillation:
- The effect of increasing age on the spectral motif dynamics during the transition from putative unconscious to deep states of anesthesia:
-
- Low Frequency Oscillation:
- Peak frequency and bandwidth remain roughly unchanged.
- The power of both unconscious and deep peaks are reduced and the difference between them increased with age.
- Traveling Peak:
- The peak frequency, power, and bandwidths of both unconscious and deep peaks are reduced with age.
- Gamma:
- The power of the broadband peaks is reduced such that there is no difference in power between them at ˜40 Hz.
- Low Frequency Oscillation:
-
FIG. 11 provides information similar to that ofFIG. 10 , in this case, with respect to sevoflurane. The spectral motifs for sevoflurane included two salient spectral peaks: a broad low frequency oscillation (spanning <1 Hz, delta, and theta bands), and a traveling peak. - Overall dynamics during the transition from putative unconscious to deep states of anesthesia:
-
- Broad Low Frequency Oscillation:
- The peak frequency remains roughly constant.
- The peak power increases.
- The bandwidth decreases slightly.
- Traveling Peak:
- The peak frequency, power, and bandwidths decrease.
- Broad Low Frequency Oscillation:
- The effect of increasing age on the spectral motif dynamics during the transition from putative unconscious to deep states of anesthesia:
-
- Broad Low Frequency Oscillation:
- Peak frequency is roughly unchanged.
- The bandwidth is slightly reduced.
- The power of both unconscious and deep peaks are reduced, and the difference between is significantly reduced such that they have the same power across unconscious and deep states.
- Traveling Peak:
- The peak frequency, power, and bandwidth of both unconscious and deep peaks are reduced, and the differences between their values are increased.
- Broad Low Frequency Oscillation:
-
FIG. 12 provides information similar to that ofFIGS. 10 and 11 , in this case, with respect to isoflurane. The spectral motifs for isoflurane included two salient spectral peaks: a broad low frequency oscillation (spanning <1 Hz, delta and theta bands), and a traveling peak. - Overall dynamics during the transition from putative unconscious to deep states of anesthesia:
-
- Broad Low Frequency Oscillation:
- The peak frequency remains roughly constant.
- The peak power increases.
- The bandwidth decreases slightly.
- Traveling Peak:
- The peak frequency, power, and bandwidths decrease.
- Broad Low Frequency Oscillation:
- The effect of increasing age on the spectral motif dynamics during the transition from putative unconscious to deep states of anesthesia:
-
- Broad Low Oscillation:
- Peak frequency is roughly unchanged.
- The bandwidth is slightly reduced.
- The power of both unconscious and deep peaks are reduced, and the difference between is significantly reduced such that they have the same power across unconscious and deep states.
- Traveling Peak:
- The peak frequency is reduced, and the differences between their values are increased.
- The power of both unconscious and deep peaks are reduced, and the difference between is significantly reduced such that they have the same power across unconscious and deep states.
- The bandwidth is slightly reduced.
- Broad Low Oscillation:
-
FIG. 13 provides information similar to that ofFIGS. 10-12 , in this case, with respect to dexmedetomidine. The data for young and elderly patients were limited and showed spectral heterogeneity. Thus, we report only the findings for the middle aged demographic. With respect to identified peaks, the spectral motifs for dexmedetomidine included three salient spectral peaks: low frequency oscillation, a non-stationary “spindle” peak spanning alpha and sigma bands, and broadband gamma. - Overall dynamics during the transition from putative unconscious to deep states of anesthesia:
-
- Slow Oscillation:
- The peak frequency remains roughly constant.
- The peak power increases.
- The bandwidth remains roughly constant.
- Spindle Peak:
- The peak frequency decreases though remains in high alpha/low sigma (spindle) range.
- The peak power decreases.
- The bandwidth remains roughly constant.
- Gamma:
- Broadband gamma with no clear spectral peak.
- The power decreases.
- Slow Oscillation:
-
FIG. 14 provides information similar to that ofFIGS. 10-13 , in this case, with respect to ketamine. Data was acquired from one middle aged patient. The effects, however, seem marked between putative unconscious and deep states of anesthesia. With respect to identified peaks, the spectral motifs for ketamine included three salient spectral peaks: the low frequency oscillation, and a non-stationary low gamma peak, and broadband high gamma (up to 150 Hz). Overall dynamics during the transition from putative unconscious to deep states of anesthesia: -
- Low Frequency Oscillation:
- The peak frequency remains roughly constant.
- The peak power decreases.
- The bandwidth remains roughly constant.
- Low Gamma Peak:
- The peak frequency and power decrease.
- The bandwidth remains roughly constant.
- High Gamma:
- Broadband gamma with no clear spectral peak.
- The power increases.
- Low Frequency Oscillation:
- The present invention has been described in terms of one or more preferred embodiments, and it should be appreciated that many equivalents, alternatives, variations, and modifications, aside from those expressly stated, are possible and within the scope of the invention. Therefore, the invention should not be limited to a particular described embodiment.
- For example, the present invention has ready use and clinical need in fields of medicine other than anesthesiology. For example, other medical specialties either use or have interest in the use of drugs such as those described above and other similar drugs. The above-described systems and methods are useful for managing drugs in a wide variety of situations. For example, the present invention can be used during pharmacological therapies to induce and maintain sedation. Also, the present invention can be particularly useful in the intensive care unit where intense therapies are administered and clinicians can benefit from additional monitoring and feedback. Further still, the present invention can be of use in outpatient settings, including outpatient treatments involving pharmacologically-induced sleep, involving sedation, for example, using dexmedetomidine. Also, the present invention can be used in psychiatry settings to aid in the treatment of depression with ketamine, for example. These are but a few of the wide-variety of clinical and non-clinical settings where the present invention can be readily applied.
Claims (21)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US14/115,682 US20140187973A1 (en) | 2011-05-06 | 2012-05-07 | System and method for tracking brain states during administration of anesthesia |
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201161483483P | 2011-05-06 | 2011-05-06 | |
PCT/US2012/036854 WO2012154701A1 (en) | 2011-05-06 | 2012-05-07 | System and method for tracking brain states during administration of anesthesia |
US14/115,682 US20140187973A1 (en) | 2011-05-06 | 2012-05-07 | System and method for tracking brain states during administration of anesthesia |
Related Parent Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/US2012/036854 A-371-Of-International WO2012154701A1 (en) | 2011-05-06 | 2012-05-07 | System and method for tracking brain states during administration of anesthesia |
Related Child Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US16/165,580 Continuation US11751770B2 (en) | 2011-05-06 | 2018-10-19 | System and method for tracking brain states during administration of anesthesia |
Publications (1)
Publication Number | Publication Date |
---|---|
US20140187973A1 true US20140187973A1 (en) | 2014-07-03 |
Family
ID=47139581
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US14/115,682 Abandoned US20140187973A1 (en) | 2011-05-06 | 2012-05-07 | System and method for tracking brain states during administration of anesthesia |
US16/165,580 Active 2035-07-18 US11751770B2 (en) | 2011-05-06 | 2018-10-19 | System and method for tracking brain states during administration of anesthesia |
Family Applications After (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US16/165,580 Active 2035-07-18 US11751770B2 (en) | 2011-05-06 | 2018-10-19 | System and method for tracking brain states during administration of anesthesia |
Country Status (5)
Country | Link |
---|---|
US (2) | US20140187973A1 (en) |
EP (1) | EP2704630B1 (en) |
JP (1) | JP6109155B2 (en) |
MX (1) | MX2013012933A (en) |
WO (1) | WO2012154701A1 (en) |
Cited By (207)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20150148700A1 (en) * | 2012-05-30 | 2015-05-28 | Isis Innovation Limited | Perception loss detection |
US20160081617A1 (en) * | 2014-09-22 | 2016-03-24 | Covidien Lp | Systems and methods for eeg monitoring |
US20160287169A1 (en) * | 2015-03-31 | 2016-10-06 | Oulun Yliopisto | Apparatus and method for electroencephalographic examination |
US9579039B2 (en) | 2011-01-10 | 2017-02-28 | Masimo Corporation | Non-invasive intravascular volume index monitor |
US9717458B2 (en) | 2012-10-20 | 2017-08-01 | Masimo Corporation | Magnetic-flap optical sensor |
US9750443B2 (en) | 2005-03-01 | 2017-09-05 | Cercacor Laboratories, Inc. | Multiple wavelength sensor emitters |
US9775570B2 (en) | 2010-03-01 | 2017-10-03 | Masimo Corporation | Adaptive alarm system |
US9788735B2 (en) | 2002-03-25 | 2017-10-17 | Masimo Corporation | Body worn mobile medical patient monitor |
US9795739B2 (en) | 2009-05-20 | 2017-10-24 | Masimo Corporation | Hemoglobin display and patient treatment |
US9801588B2 (en) | 2003-07-08 | 2017-10-31 | Cercacor Laboratories, Inc. | Method and apparatus for reducing coupling between signals in a measurement system |
US9814418B2 (en) | 2001-06-29 | 2017-11-14 | Masimo Corporation | Sine saturation transform |
US9839379B2 (en) | 2013-10-07 | 2017-12-12 | Masimo Corporation | Regional oximetry pod |
US9839381B1 (en) | 2009-11-24 | 2017-12-12 | Cercacor Laboratories, Inc. | Physiological measurement system with automatic wavelength adjustment |
US9847002B2 (en) | 2009-12-21 | 2017-12-19 | Masimo Corporation | Modular patient monitor |
US9848806B2 (en) | 2001-07-02 | 2017-12-26 | Masimo Corporation | Low power pulse oximeter |
US9849241B2 (en) | 2013-04-24 | 2017-12-26 | Fresenius Kabi Deutschland Gmbh | Method of operating a control device for controlling an infusion device |
US9848807B2 (en) | 2007-04-21 | 2017-12-26 | Masimo Corporation | Tissue profile wellness monitor |
US9861305B1 (en) | 2006-10-12 | 2018-01-09 | Masimo Corporation | Method and apparatus for calibration to reduce coupling between signals in a measurement system |
US9891079B2 (en) | 2013-07-17 | 2018-02-13 | Masimo Corporation | Pulser with double-bearing position encoder for non-invasive physiological monitoring |
US9913617B2 (en) | 2011-10-13 | 2018-03-13 | Masimo Corporation | Medical monitoring hub |
US9936917B2 (en) | 2013-03-14 | 2018-04-10 | Masimo Laboratories, Inc. | Patient monitor placement indicator |
US9943269B2 (en) | 2011-10-13 | 2018-04-17 | Masimo Corporation | System for displaying medical monitoring data |
US9949676B2 (en) | 2006-10-12 | 2018-04-24 | Masimo Corporation | Patient monitor capable of monitoring the quality of attached probes and accessories |
US10007758B2 (en) | 2009-03-04 | 2018-06-26 | Masimo Corporation | Medical monitoring system |
US10032002B2 (en) | 2009-03-04 | 2018-07-24 | Masimo Corporation | Medical monitoring system |
US10052037B2 (en) | 2010-07-22 | 2018-08-21 | Masimo Corporation | Non-invasive blood pressure measurement system |
US10058275B2 (en) | 2003-07-25 | 2018-08-28 | Masimo Corporation | Multipurpose sensor port |
US10086138B1 (en) | 2014-01-28 | 2018-10-02 | Masimo Corporation | Autonomous drug delivery system |
US10092249B2 (en) | 2005-10-14 | 2018-10-09 | Masimo Corporation | Robust alarm system |
US10098591B2 (en) | 2004-03-08 | 2018-10-16 | Masimo Corporation | Physiological parameter system |
US10130289B2 (en) | 1999-01-07 | 2018-11-20 | Masimo Corporation | Pulse and confidence indicator displayed proximate plethysmograph |
US10130291B2 (en) | 2004-08-11 | 2018-11-20 | Masimo Corporation | Method for data reduction and calibration of an OCT-based physiological monitor |
USD835282S1 (en) | 2017-04-28 | 2018-12-04 | Masimo Corporation | Medical monitoring device |
USD835283S1 (en) | 2017-04-28 | 2018-12-04 | Masimo Corporation | Medical monitoring device |
USD835285S1 (en) | 2017-04-28 | 2018-12-04 | Masimo Corporation | Medical monitoring device |
USD835284S1 (en) | 2017-04-28 | 2018-12-04 | Masimo Corporation | Medical monitoring device |
US10149616B2 (en) | 2012-02-09 | 2018-12-11 | Masimo Corporation | Wireless patient monitoring device |
US10159412B2 (en) | 2010-12-01 | 2018-12-25 | Cercacor Laboratories, Inc. | Handheld processing device including medical applications for minimally and non invasive glucose measurements |
US10188331B1 (en) | 2009-07-29 | 2019-01-29 | Masimo Corporation | Non-invasive physiological sensor cover |
US10194847B2 (en) | 2006-10-12 | 2019-02-05 | Masimo Corporation | Perfusion index smoother |
US10205272B2 (en) | 2009-03-11 | 2019-02-12 | Masimo Corporation | Magnetic connector |
US10201298B2 (en) | 2003-01-24 | 2019-02-12 | Masimo Corporation | Noninvasive oximetry optical sensor including disposable and reusable elements |
US10205291B2 (en) | 2015-02-06 | 2019-02-12 | Masimo Corporation | Pogo pin connector |
USRE47249E1 (en) | 2008-07-29 | 2019-02-19 | Masimo Corporation | Alarm suspend system |
US10219746B2 (en) | 2006-10-12 | 2019-03-05 | Masimo Corporation | Oximeter probe off indicator defining probe off space |
US10226576B2 (en) | 2006-05-15 | 2019-03-12 | Masimo Corporation | Sepsis monitor |
US10226187B2 (en) | 2015-08-31 | 2019-03-12 | Masimo Corporation | Patient-worn wireless physiological sensor |
US10231670B2 (en) | 2014-06-19 | 2019-03-19 | Masimo Corporation | Proximity sensor in pulse oximeter |
US10255994B2 (en) | 2009-03-04 | 2019-04-09 | Masimo Corporation | Physiological parameter alarm delay |
US10271749B2 (en) | 2011-02-25 | 2019-04-30 | Masimo Corporation | Patient monitor for monitoring microcirculation |
US10271748B2 (en) | 2010-05-06 | 2019-04-30 | Masimo Corporation | Patient monitor for determining microcirculation state |
US10278648B2 (en) | 2012-01-04 | 2019-05-07 | Masimo Corporation | Automated CCHD screening and detection |
US10278626B2 (en) | 2006-03-17 | 2019-05-07 | Masimo Corporation | Apparatus and method for creating a stable optical interface |
US10279247B2 (en) | 2013-12-13 | 2019-05-07 | Masimo Corporation | Avatar-incentive healthcare therapy |
US10292664B2 (en) | 2008-05-02 | 2019-05-21 | Masimo Corporation | Monitor configuration system |
US10292657B2 (en) | 2009-02-16 | 2019-05-21 | Masimo Corporation | Ear sensor |
US10299720B2 (en) | 2010-09-01 | 2019-05-28 | The General Hospital Corporation | Reversal of general anesthesia by administration of methylphenidate, amphetamine, modafinil, amantadine, and/or caffeine |
US10314503B2 (en) | 2013-06-27 | 2019-06-11 | The General Hospital Corporation | Systems and methods for tracking non-stationary spectral structure and dynamics in physiological data |
US10327337B2 (en) | 2015-02-06 | 2019-06-18 | Masimo Corporation | Fold flex circuit for LNOP |
US10327713B2 (en) | 2017-02-24 | 2019-06-25 | Masimo Corporation | Modular multi-parameter patient monitoring device |
US10335072B2 (en) | 1998-06-03 | 2019-07-02 | Masimo Corporation | Physiological monitor |
US10342497B2 (en) | 2009-10-15 | 2019-07-09 | Masimo Corporation | Physiological acoustic monitoring system |
US10342487B2 (en) | 2009-05-19 | 2019-07-09 | Masimo Corporation | Disposable components for reusable physiological sensor |
US10342470B2 (en) | 2006-10-12 | 2019-07-09 | Masimo Corporation | System and method for monitoring the life of a physiological sensor |
US10349895B2 (en) | 2009-10-15 | 2019-07-16 | Masimo Corporation | Acoustic respiratory monitoring sensor having multiple sensing elements |
US10357209B2 (en) | 2009-10-15 | 2019-07-23 | Masimo Corporation | Bidirectional physiological information display |
US10368787B2 (en) | 2008-03-04 | 2019-08-06 | Masimo Corporation | Flowometry in optical coherence tomography for analyte level estimation |
US10383574B2 (en) | 2013-06-28 | 2019-08-20 | The General Hospital Corporation | Systems and methods to infer brain state during burst suppression |
US10383520B2 (en) | 2014-09-18 | 2019-08-20 | Masimo Semiconductor, Inc. | Enhanced visible near-infrared photodiode and non-invasive physiological sensor |
US10388120B2 (en) | 2017-02-24 | 2019-08-20 | Masimo Corporation | Localized projection of audible noises in medical settings |
US10398320B2 (en) | 2009-09-17 | 2019-09-03 | Masimo Corporation | Optical-based physiological monitoring system |
US10441196B2 (en) | 2015-01-23 | 2019-10-15 | Masimo Corporation | Nasal/oral cannula system and manufacturing |
US10448871B2 (en) | 2015-07-02 | 2019-10-22 | Masimo Corporation | Advanced pulse oximetry sensor |
US10463340B2 (en) | 2009-10-15 | 2019-11-05 | Masimo Corporation | Acoustic respiratory monitoring systems and methods |
US10463284B2 (en) | 2006-11-29 | 2019-11-05 | Cercacor Laboratories, Inc. | Optical sensor including disposable and reusable elements |
US10505311B2 (en) | 2017-08-15 | 2019-12-10 | Masimo Corporation | Water resistant connector for noninvasive patient monitor |
US10524706B2 (en) | 2008-05-05 | 2020-01-07 | Masimo Corporation | Pulse oximetry system with electrical decoupling circuitry |
US10524738B2 (en) | 2015-05-04 | 2020-01-07 | Cercacor Laboratories, Inc. | Noninvasive sensor system with visual infographic display |
US10537285B2 (en) | 2016-03-04 | 2020-01-21 | Masimo Corporation | Nose sensor |
WO2020018595A1 (en) * | 2018-07-16 | 2020-01-23 | The General Hospital Corporation | System and method for monitoring neural signals |
US10542903B2 (en) | 2012-06-07 | 2020-01-28 | Masimo Corporation | Depth of consciousness monitor |
US10548561B2 (en) | 2008-12-30 | 2020-02-04 | Masimo Corporation | Acoustic sensor assembly |
US10555678B2 (en) | 2013-08-05 | 2020-02-11 | Masimo Corporation | Blood pressure monitor with valve-chamber assembly |
US10568553B2 (en) | 2015-02-06 | 2020-02-25 | Masimo Corporation | Soft boot pulse oximetry sensor |
US10582886B2 (en) | 2008-07-03 | 2020-03-10 | Masimo Corporation | Multi-stream data collection system for noninvasive measurement of blood constituents |
US10588518B2 (en) | 2006-09-20 | 2020-03-17 | Masimo Corporation | Congenital heart disease monitor |
US10602978B2 (en) | 2013-09-13 | 2020-03-31 | The General Hospital Corporation | Systems and methods for improved brain monitoring during general anesthesia and sedation |
US10610139B2 (en) | 2013-01-16 | 2020-04-07 | Masimo Corporation | Active-pulse blood analysis system |
US10617302B2 (en) | 2016-07-07 | 2020-04-14 | Masimo Corporation | Wearable pulse oximeter and respiration monitor |
US10667764B2 (en) | 2018-04-19 | 2020-06-02 | Masimo Corporation | Mobile patient alarm display |
US10672260B2 (en) | 2013-03-13 | 2020-06-02 | Masimo Corporation | Systems and methods for monitoring a patient health network |
USD890708S1 (en) | 2017-08-15 | 2020-07-21 | Masimo Corporation | Connector |
US10721785B2 (en) | 2017-01-18 | 2020-07-21 | Masimo Corporation | Patient-worn wireless physiological sensor with pairing functionality |
US10729402B2 (en) | 2009-12-04 | 2020-08-04 | Masimo Corporation | Calibration for multi-stage physiological monitors |
US10729362B2 (en) | 2010-03-08 | 2020-08-04 | Masimo Corporation | Reprocessing of a physiological sensor |
US10750984B2 (en) | 2016-12-22 | 2020-08-25 | Cercacor Laboratories, Inc. | Methods and devices for detecting intensity of light with translucent detector |
US10765367B2 (en) | 2014-10-07 | 2020-09-08 | Masimo Corporation | Modular physiological sensors |
US10779098B2 (en) | 2018-07-10 | 2020-09-15 | Masimo Corporation | Patient monitor alarm speaker analyzer |
US10786168B2 (en) | 2016-11-29 | 2020-09-29 | The General Hospital Corporation | Systems and methods for analyzing electrophysiological data from patients undergoing medical treatments |
USD897098S1 (en) | 2018-10-12 | 2020-09-29 | Masimo Corporation | Card holder set |
US10825568B2 (en) | 2013-10-11 | 2020-11-03 | Masimo Corporation | Alarm notification system |
US10828007B1 (en) | 2013-10-11 | 2020-11-10 | Masimo Corporation | Acoustic sensor with attachment portion |
US10833983B2 (en) | 2012-09-20 | 2020-11-10 | Masimo Corporation | Intelligent medical escalation process |
US10849554B2 (en) | 2017-04-18 | 2020-12-01 | Masimo Corporation | Nose sensor |
US10856750B2 (en) | 2017-04-28 | 2020-12-08 | Masimo Corporation | Spot check measurement system |
US10874797B2 (en) | 2006-01-17 | 2020-12-29 | Masimo Corporation | Drug administration controller |
USD906970S1 (en) | 2017-08-15 | 2021-01-05 | Masimo Corporation | Connector |
US10912524B2 (en) | 2006-09-22 | 2021-02-09 | Masimo Corporation | Modular patient monitor |
US10918281B2 (en) | 2017-04-26 | 2021-02-16 | Masimo Corporation | Medical monitoring device having multiple configurations |
US10918341B2 (en) | 2006-12-22 | 2021-02-16 | Masimo Corporation | Physiological parameter system |
US10932729B2 (en) | 2018-06-06 | 2021-03-02 | Masimo Corporation | Opioid overdose monitoring |
US10932705B2 (en) | 2017-05-08 | 2021-03-02 | Masimo Corporation | System for displaying and controlling medical monitoring data |
US10956950B2 (en) | 2017-02-24 | 2021-03-23 | Masimo Corporation | Managing dynamic licenses for physiological parameters in a patient monitoring environment |
US10952641B2 (en) | 2008-09-15 | 2021-03-23 | Masimo Corporation | Gas sampling line |
US10955270B2 (en) | 2011-10-27 | 2021-03-23 | Masimo Corporation | Physiological monitor gauge panel |
USD916135S1 (en) | 2018-10-11 | 2021-04-13 | Masimo Corporation | Display screen or portion thereof with a graphical user interface |
US10980507B2 (en) | 2009-10-15 | 2021-04-20 | Masimo Corporation | Physiological acoustic monitoring system |
US10991135B2 (en) | 2015-08-11 | 2021-04-27 | Masimo Corporation | Medical monitoring analysis and replay including indicia responsive to light attenuated by body tissue |
USD917704S1 (en) | 2019-08-16 | 2021-04-27 | Masimo Corporation | Patient monitor |
US10987066B2 (en) | 2017-10-31 | 2021-04-27 | Masimo Corporation | System for displaying oxygen state indications |
USD917550S1 (en) | 2018-10-11 | 2021-04-27 | Masimo Corporation | Display screen or portion thereof with a graphical user interface |
USD917564S1 (en) | 2018-10-11 | 2021-04-27 | Masimo Corporation | Display screen or portion thereof with graphical user interface |
US10993662B2 (en) | 2016-03-04 | 2021-05-04 | Masimo Corporation | Nose sensor |
USD919100S1 (en) | 2019-08-16 | 2021-05-11 | Masimo Corporation | Holder for a patient monitor |
USD919094S1 (en) | 2019-08-16 | 2021-05-11 | Masimo Corporation | Blood pressure device |
US11006841B2 (en) | 2017-06-07 | 2021-05-18 | Covidien Lp | Systems and methods for detecting strokes |
US11024064B2 (en) | 2017-02-24 | 2021-06-01 | Masimo Corporation | Augmented reality system for displaying patient data |
USD921202S1 (en) | 2019-08-16 | 2021-06-01 | Masimo Corporation | Holder for a blood pressure device |
US11020084B2 (en) | 2012-09-20 | 2021-06-01 | Masimo Corporation | Acoustic patient sensor coupler |
US11026604B2 (en) | 2017-07-13 | 2021-06-08 | Cercacor Laboratories, Inc. | Medical monitoring device for harmonizing physiological measurements |
US11069461B2 (en) | 2012-08-01 | 2021-07-20 | Masimo Corporation | Automated assembly sensor cable |
USD925597S1 (en) | 2017-10-31 | 2021-07-20 | Masimo Corporation | Display screen or portion thereof with graphical user interface |
US11071480B2 (en) | 2012-04-17 | 2021-07-27 | Masimo Corporation | Hypersaturation index |
US11076777B2 (en) | 2016-10-13 | 2021-08-03 | Masimo Corporation | Systems and methods for monitoring orientation to reduce pressure ulcer formation |
US11086609B2 (en) | 2017-02-24 | 2021-08-10 | Masimo Corporation | Medical monitoring hub |
USD927699S1 (en) | 2019-10-18 | 2021-08-10 | Masimo Corporation | Electrode pad |
US11089982B2 (en) | 2011-10-13 | 2021-08-17 | Masimo Corporation | Robust fractional saturation determination |
US11109770B2 (en) | 2011-06-21 | 2021-09-07 | Masimo Corporation | Patient monitoring system |
US11114188B2 (en) | 2009-10-06 | 2021-09-07 | Cercacor Laboratories, Inc. | System for monitoring a physiological parameter of a user |
US11132117B2 (en) | 2012-03-25 | 2021-09-28 | Masimo Corporation | Physiological monitor touchscreen interface |
USD933232S1 (en) | 2020-05-11 | 2021-10-12 | Masimo Corporation | Blood pressure monitor |
US11147518B1 (en) | 2013-10-07 | 2021-10-19 | Masimo Corporation | Regional oximetry signal processor |
US11153089B2 (en) | 2016-07-06 | 2021-10-19 | Masimo Corporation | Secure and zero knowledge data sharing for cloud applications |
US11172890B2 (en) | 2012-01-04 | 2021-11-16 | Masimo Corporation | Automated condition screening and detection |
US11176801B2 (en) | 2011-08-19 | 2021-11-16 | Masimo Corporation | Health care sanitation monitoring system |
US11185262B2 (en) | 2017-03-10 | 2021-11-30 | Masimo Corporation | Pneumonia screener |
US11191485B2 (en) | 2006-06-05 | 2021-12-07 | Masimo Corporation | Parameter upgrade system |
US11191484B2 (en) | 2016-04-29 | 2021-12-07 | Masimo Corporation | Optical sensor tape |
US11229374B2 (en) | 2006-12-09 | 2022-01-25 | Masimo Corporation | Plethysmograph variability processor |
US11234655B2 (en) | 2007-01-20 | 2022-02-01 | Masimo Corporation | Perfusion trend indicator |
US11259745B2 (en) | 2014-01-28 | 2022-03-01 | Masimo Corporation | Autonomous drug delivery system |
US11272852B2 (en) | 2011-06-21 | 2022-03-15 | Masimo Corporation | Patient monitoring system |
US11272839B2 (en) | 2018-10-12 | 2022-03-15 | Ma Simo Corporation | System for transmission of sensor data using dual communication protocol |
US11289199B2 (en) | 2010-01-19 | 2022-03-29 | Masimo Corporation | Wellness analysis system |
US11298021B2 (en) | 2017-10-19 | 2022-04-12 | Masimo Corporation | Medical monitoring system |
USRE49034E1 (en) | 2002-01-24 | 2022-04-19 | Masimo Corporation | Physiological trend monitor |
US11331013B2 (en) | 2014-09-04 | 2022-05-17 | Masimo Corporation | Total hemoglobin screening sensor |
US11367529B2 (en) | 2012-11-05 | 2022-06-21 | Cercacor Laboratories, Inc. | Physiological test credit method |
US11389093B2 (en) | 2018-10-11 | 2022-07-19 | Masimo Corporation | Low noise oximetry cable |
US11399774B2 (en) | 2010-10-13 | 2022-08-02 | Masimo Corporation | Physiological measurement logic engine |
US11399722B2 (en) | 2010-03-30 | 2022-08-02 | Masimo Corporation | Plethysmographic respiration rate detection |
US11406286B2 (en) | 2018-10-11 | 2022-08-09 | Masimo Corporation | Patient monitoring device with improved user interface |
US11417426B2 (en) | 2017-02-24 | 2022-08-16 | Masimo Corporation | System for displaying medical monitoring data |
US11439329B2 (en) | 2011-07-13 | 2022-09-13 | Masimo Corporation | Multiple measurement mode in a physiological sensor |
US11445948B2 (en) | 2018-10-11 | 2022-09-20 | Masimo Corporation | Patient connector assembly with vertical detents |
US11445960B2 (en) * | 2019-10-09 | 2022-09-20 | Trustees Of Boston University | Electrography system employing layered electrodes for improved spatial resolution |
US11452449B2 (en) | 2012-10-30 | 2022-09-27 | Masimo Corporation | Universal medical system |
US11464410B2 (en) | 2018-10-12 | 2022-10-11 | Masimo Corporation | Medical systems and methods |
US11488715B2 (en) | 2011-02-13 | 2022-11-01 | Masimo Corporation | Medical characterization system |
US11504002B2 (en) | 2012-09-20 | 2022-11-22 | Masimo Corporation | Physiological monitoring system |
US11504066B1 (en) | 2015-09-04 | 2022-11-22 | Cercacor Laboratories, Inc. | Low-noise sensor system |
US11504058B1 (en) | 2016-12-02 | 2022-11-22 | Masimo Corporation | Multi-site noninvasive measurement of a physiological parameter |
USD973072S1 (en) | 2020-09-30 | 2022-12-20 | Masimo Corporation | Display screen or portion thereof with graphical user interface |
USD973686S1 (en) | 2020-09-30 | 2022-12-27 | Masimo Corporation | Display screen or portion thereof with graphical user interface |
USD973685S1 (en) | 2020-09-30 | 2022-12-27 | Masimo Corporation | Display screen or portion thereof with graphical user interface |
USD974193S1 (en) | 2020-07-27 | 2023-01-03 | Masimo Corporation | Wearable temperature measurement device |
US11581091B2 (en) | 2014-08-26 | 2023-02-14 | Vccb Holdings, Inc. | Real-time monitoring systems and methods in a healthcare environment |
USD979516S1 (en) | 2020-05-11 | 2023-02-28 | Masimo Corporation | Connector |
USD980091S1 (en) | 2020-07-27 | 2023-03-07 | Masimo Corporation | Wearable temperature measurement device |
US11596363B2 (en) | 2013-09-12 | 2023-03-07 | Cercacor Laboratories, Inc. | Medical device management system |
US11637437B2 (en) | 2019-04-17 | 2023-04-25 | Masimo Corporation | Charging station for physiological monitoring device |
US11638532B2 (en) | 2008-07-03 | 2023-05-02 | Masimo Corporation | User-worn device for noninvasively measuring a physiological parameter of a user |
USD985498S1 (en) | 2019-08-16 | 2023-05-09 | Masimo Corporation | Connector |
US11653862B2 (en) | 2015-05-22 | 2023-05-23 | Cercacor Laboratories, Inc. | Non-invasive optical physiological differential pathlength sensor |
US11679579B2 (en) | 2015-12-17 | 2023-06-20 | Masimo Corporation | Varnish-coated release liner |
US11684296B2 (en) | 2018-12-21 | 2023-06-27 | Cercacor Laboratories, Inc. | Noninvasive physiological sensor |
US11690574B2 (en) | 2003-11-05 | 2023-07-04 | Masimo Corporation | Pulse oximeter access apparatus and method |
US11696712B2 (en) | 2014-06-13 | 2023-07-11 | Vccb Holdings, Inc. | Alarm fatigue management systems and methods |
US11717210B2 (en) | 2010-09-28 | 2023-08-08 | Masimo Corporation | Depth of consciousness monitor including oximeter |
US11721105B2 (en) | 2020-02-13 | 2023-08-08 | Masimo Corporation | System and method for monitoring clinical activities |
US11730960B2 (en) * | 2020-05-27 | 2023-08-22 | Attune Neurosciences, Inc. | Ultrasound systems and associated devices and methods for modulating brain activity |
US11730379B2 (en) | 2020-03-20 | 2023-08-22 | Masimo Corporation | Remote patient management and monitoring systems and methods |
USD997365S1 (en) | 2021-06-24 | 2023-08-29 | Masimo Corporation | Physiological nose sensor |
USD998630S1 (en) | 2018-10-11 | 2023-09-12 | Masimo Corporation | Display screen or portion thereof with a graphical user interface |
USD998631S1 (en) | 2018-10-11 | 2023-09-12 | Masimo Corporation | Display screen or portion thereof with a graphical user interface |
USD999246S1 (en) | 2018-10-11 | 2023-09-19 | Masimo Corporation | Display screen or portion thereof with a graphical user interface |
US11766198B2 (en) | 2018-02-02 | 2023-09-26 | Cercacor Laboratories, Inc. | Limb-worn patient monitoring device |
EP4041067A4 (en) * | 2019-10-11 | 2023-10-04 | The Trustees of Columbia University in the City of New York | System, method and computer-accessible medium for anesthesia monitoring using electroencephalographic monitoring |
USD1000975S1 (en) | 2021-09-22 | 2023-10-10 | Masimo Corporation | Wearable temperature measurement device |
US11803623B2 (en) | 2019-10-18 | 2023-10-31 | Masimo Corporation | Display layout and interactive objects for patient monitoring |
US11832940B2 (en) | 2019-08-27 | 2023-12-05 | Cercacor Laboratories, Inc. | Non-invasive medical monitoring device for blood analyte measurements |
EP4048343A4 (en) * | 2019-10-24 | 2023-12-06 | The Trustees of Columbia University in the City of New York | System, method, and computer-accessible medium for visualization and analysis of electroencephalogram oscillations in the alpha band |
US11872156B2 (en) | 2018-08-22 | 2024-01-16 | Masimo Corporation | Core body temperature measurement |
US11877824B2 (en) | 2011-08-17 | 2024-01-23 | Masimo Corporation | Modulated physiological sensor |
US11879960B2 (en) | 2020-02-13 | 2024-01-23 | Masimo Corporation | System and method for monitoring clinical activities |
US11883129B2 (en) | 2018-04-24 | 2024-01-30 | Cercacor Laboratories, Inc. | Easy insert finger sensor for transmission based spectroscopy sensor |
US11951186B2 (en) | 2020-10-23 | 2024-04-09 | Willow Laboratories, Inc. | Indicator compounds, devices comprising indicator compounds, and methods of making and using the same |
Families Citing this family (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11786132B2 (en) | 2011-05-06 | 2023-10-17 | The General Hospital Corporation | Systems and methods for predicting arousal to consciousness during general anesthesia and sedation |
BR112015026933A2 (en) * | 2013-04-23 | 2017-07-25 | Massachusetts Gen Hospital | system and method for monitoring anesthesia and sedation using brain coherence and synchrony measurements |
WO2014176441A1 (en) * | 2013-04-24 | 2014-10-30 | The General Hospital Corporation | System and method for monitoring level of dexmedatomidine-induced sedation |
WO2014176444A1 (en) * | 2013-04-24 | 2014-10-30 | The General Hospital Corporation | System and method for estimating high time-frequency resolution eeg spectrograms to monitor patient state |
WO2015009877A1 (en) | 2013-07-17 | 2015-01-22 | The Regents Of The University Of California | Method for focused recording and stimulation electrode array |
WO2015108908A2 (en) * | 2014-01-14 | 2015-07-23 | The General Hospital Corporation | System and method for characterizing brain states during general anesthesia and sedation using phase-amplitude modulation |
CN107811619B (en) * | 2017-12-08 | 2021-10-19 | 西安科技大学 | Portable pulse diagnosis instrument and analysis method thereof |
US11406316B2 (en) | 2018-02-14 | 2022-08-09 | Cerenion Oy | Apparatus and method for electroencephalographic measurement |
KR102502880B1 (en) * | 2019-10-11 | 2023-02-23 | 연세대학교 산학협력단 | Method for bayesian maximum entropy model estimation and brain function dynamic characteristics evaluation |
US11769595B2 (en) * | 2020-10-01 | 2023-09-26 | Agama-X Co., Ltd. | Information processing apparatus and non-transitory computer readable medium |
JP2022059140A (en) | 2020-10-01 | 2022-04-13 | 株式会社Agama-X | Information processing device and program |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6016444A (en) * | 1997-12-10 | 2000-01-18 | New York University | Automatic control of anesthesia using quantitative EEG |
US20040193068A1 (en) * | 2001-06-13 | 2004-09-30 | David Burton | Methods and apparatus for monitoring consciousness |
US20060009733A1 (en) * | 2004-07-07 | 2006-01-12 | Martin James F | Bis closed loop anesthetic delivery |
US20070060831A1 (en) * | 2005-09-12 | 2007-03-15 | Le Tan T T | Method and system for detecting and classifyng the mental state of a subject |
US20110015538A1 (en) * | 2009-07-17 | 2011-01-20 | Matthews Jr Thomas Virgil | System and method for analyzing electroencephalography data |
US20110119212A1 (en) * | 2008-02-20 | 2011-05-19 | Hubert De Bruin | Expert system for determining patient treatment response |
US20110295096A1 (en) * | 2010-05-25 | 2011-12-01 | Neurowave Systems Inc. | Method and system for electrode impedance measurement |
Family Cites Families (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6067467A (en) * | 1994-02-07 | 2000-05-23 | New York University | EEG operative and post-operative patient monitoring method |
US6631291B2 (en) | 2001-05-18 | 2003-10-07 | Instrumentarium Corp. | Closed loop drug administration method and apparatus using EEG complexity for control purposes |
US7089927B2 (en) * | 2002-10-23 | 2006-08-15 | New York University | System and method for guidance of anesthesia, analgesia and amnesia |
WO2008003049A2 (en) * | 2006-06-28 | 2008-01-03 | The University Of Utah Research Foundation | Distinguishing different drug effects from the electroencephalogram |
JP4978860B2 (en) | 2007-01-24 | 2012-07-18 | 株式会社国際電気通信基礎技術研究所 | Action prediction method and action prediction apparatus |
US20110118620A1 (en) * | 2008-04-15 | 2011-05-19 | Christopher Scheib | Method and system for monitoring and displaying physiological conditions |
RU95243U1 (en) * | 2010-02-12 | 2010-06-27 | Российская Федерация, от имени которой выступает Федеральное Агентство по науке и инновациям | DEVICE FOR ANESTHESIA LEVEL CONTROL |
-
2012
- 2012-05-07 US US14/115,682 patent/US20140187973A1/en not_active Abandoned
- 2012-05-07 WO PCT/US2012/036854 patent/WO2012154701A1/en active Application Filing
- 2012-05-07 EP EP12781958.9A patent/EP2704630B1/en active Active
- 2012-05-07 MX MX2013012933A patent/MX2013012933A/en active IP Right Grant
- 2012-05-07 JP JP2014510399A patent/JP6109155B2/en active Active
-
2018
- 2018-10-19 US US16/165,580 patent/US11751770B2/en active Active
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6016444A (en) * | 1997-12-10 | 2000-01-18 | New York University | Automatic control of anesthesia using quantitative EEG |
US20040193068A1 (en) * | 2001-06-13 | 2004-09-30 | David Burton | Methods and apparatus for monitoring consciousness |
US20060009733A1 (en) * | 2004-07-07 | 2006-01-12 | Martin James F | Bis closed loop anesthetic delivery |
US20070060831A1 (en) * | 2005-09-12 | 2007-03-15 | Le Tan T T | Method and system for detecting and classifyng the mental state of a subject |
US20110119212A1 (en) * | 2008-02-20 | 2011-05-19 | Hubert De Bruin | Expert system for determining patient treatment response |
US20110015538A1 (en) * | 2009-07-17 | 2011-01-20 | Matthews Jr Thomas Virgil | System and method for analyzing electroencephalography data |
US20110295096A1 (en) * | 2010-05-25 | 2011-12-01 | Neurowave Systems Inc. | Method and system for electrode impedance measurement |
Non-Patent Citations (5)
Title |
---|
Ching et al. Thalamocortical model for a propofol-induced alpha-reythm associated with loss of consciousness, 12/28/2010, PNAS, vol. 107, no. 52, pp. 22665-22670 * |
Cimenser et al. Development of new neurophysiological signatures of general anesthesia induced loss of consciousness, 7/13/2009, BMC Neuroscience, 10(Suppl I):P79 * |
Mitra et al., "Time Series Analysis" Chapter 7, 2007, Observed Brain Dynamics, Oxford University Press, UK. p. 184-216 * |
Molaee-Ardekani et al. Delta waves differently modulate high frequency components of EEG oscillations ion various unconsciousness levels, 2007, Proceedings of the 29th Annual International Conference of the IEE EMBS, 1294-1297 * |
Wong et al., Robust Time-Varying Multivariate Coherence Estimation: Application to Electroencephalogram Recordings During General Anesthesia, 8-9/2011, IEE, 4725-4728 * |
Cited By (438)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10335072B2 (en) | 1998-06-03 | 2019-07-02 | Masimo Corporation | Physiological monitor |
US10130289B2 (en) | 1999-01-07 | 2018-11-20 | Masimo Corporation | Pulse and confidence indicator displayed proximate plethysmograph |
US9814418B2 (en) | 2001-06-29 | 2017-11-14 | Masimo Corporation | Sine saturation transform |
US10959652B2 (en) | 2001-07-02 | 2021-03-30 | Masimo Corporation | Low power pulse oximeter |
US10433776B2 (en) | 2001-07-02 | 2019-10-08 | Masimo Corporation | Low power pulse oximeter |
US9848806B2 (en) | 2001-07-02 | 2017-12-26 | Masimo Corporation | Low power pulse oximeter |
US11219391B2 (en) | 2001-07-02 | 2022-01-11 | Masimo Corporation | Low power pulse oximeter |
US10980455B2 (en) | 2001-07-02 | 2021-04-20 | Masimo Corporation | Low power pulse oximeter |
USRE49034E1 (en) | 2002-01-24 | 2022-04-19 | Masimo Corporation | Physiological trend monitor |
US9795300B2 (en) | 2002-03-25 | 2017-10-24 | Masimo Corporation | Wearable portable patient monitor |
US10213108B2 (en) | 2002-03-25 | 2019-02-26 | Masimo Corporation | Arm mountable portable patient monitor |
US10869602B2 (en) | 2002-03-25 | 2020-12-22 | Masimo Corporation | Physiological measurement communications adapter |
US9788735B2 (en) | 2002-03-25 | 2017-10-17 | Masimo Corporation | Body worn mobile medical patient monitor |
US11484205B2 (en) | 2002-03-25 | 2022-11-01 | Masimo Corporation | Physiological measurement device |
US10219706B2 (en) | 2002-03-25 | 2019-03-05 | Masimo Corporation | Physiological measurement device |
US10335033B2 (en) | 2002-03-25 | 2019-07-02 | Masimo Corporation | Physiological measurement device |
US9872623B2 (en) | 2002-03-25 | 2018-01-23 | Masimo Corporation | Arm mountable portable patient monitor |
US10201298B2 (en) | 2003-01-24 | 2019-02-12 | Masimo Corporation | Noninvasive oximetry optical sensor including disposable and reusable elements |
US10973447B2 (en) | 2003-01-24 | 2021-04-13 | Masimo Corporation | Noninvasive oximetry optical sensor including disposable and reusable elements |
US9801588B2 (en) | 2003-07-08 | 2017-10-31 | Cercacor Laboratories, Inc. | Method and apparatus for reducing coupling between signals in a measurement system |
US11020029B2 (en) | 2003-07-25 | 2021-06-01 | Masimo Corporation | Multipurpose sensor port |
US10058275B2 (en) | 2003-07-25 | 2018-08-28 | Masimo Corporation | Multipurpose sensor port |
US11690574B2 (en) | 2003-11-05 | 2023-07-04 | Masimo Corporation | Pulse oximeter access apparatus and method |
US11937949B2 (en) | 2004-03-08 | 2024-03-26 | Masimo Corporation | Physiological parameter system |
US10098591B2 (en) | 2004-03-08 | 2018-10-16 | Masimo Corporation | Physiological parameter system |
US11109814B2 (en) | 2004-03-08 | 2021-09-07 | Masimo Corporation | Physiological parameter system |
US10130291B2 (en) | 2004-08-11 | 2018-11-20 | Masimo Corporation | Method for data reduction and calibration of an OCT-based physiological monitor |
US10791971B2 (en) | 2004-08-11 | 2020-10-06 | Masimo Corporation | Method for data reduction and calibration of an OCT-based physiological monitor |
US11426104B2 (en) | 2004-08-11 | 2022-08-30 | Masimo Corporation | Method for data reduction and calibration of an OCT-based physiological monitor |
US10984911B2 (en) | 2005-03-01 | 2021-04-20 | Cercacor Laboratories, Inc. | Multiple wavelength sensor emitters |
US10327683B2 (en) | 2005-03-01 | 2019-06-25 | Cercacor Laboratories, Inc. | Multiple wavelength sensor emitters |
US11430572B2 (en) | 2005-03-01 | 2022-08-30 | Cercacor Laboratories, Inc. | Multiple wavelength sensor emitters |
US11545263B2 (en) | 2005-03-01 | 2023-01-03 | Cercacor Laboratories, Inc. | Multiple wavelength sensor emitters |
US9750443B2 (en) | 2005-03-01 | 2017-09-05 | Cercacor Laboratories, Inc. | Multiple wavelength sensor emitters |
US10251585B2 (en) | 2005-03-01 | 2019-04-09 | Cercacor Laboratories, Inc. | Noninvasive multi-parameter patient monitor |
US10123726B2 (en) | 2005-03-01 | 2018-11-13 | Cercacor Laboratories, Inc. | Configurable physiological measurement system |
US10856788B2 (en) | 2005-03-01 | 2020-12-08 | Cercacor Laboratories, Inc. | Noninvasive multi-parameter patient monitor |
US10092249B2 (en) | 2005-10-14 | 2018-10-09 | Masimo Corporation | Robust alarm system |
US10939877B2 (en) | 2005-10-14 | 2021-03-09 | Masimo Corporation | Robust alarm system |
US11839498B2 (en) | 2005-10-14 | 2023-12-12 | Masimo Corporation | Robust alarm system |
US11724031B2 (en) | 2006-01-17 | 2023-08-15 | Masimo Corporation | Drug administration controller |
US10874797B2 (en) | 2006-01-17 | 2020-12-29 | Masimo Corporation | Drug administration controller |
US11207007B2 (en) | 2006-03-17 | 2021-12-28 | Masimo Corporation | Apparatus and method for creating a stable optical interface |
US11944431B2 (en) | 2006-03-17 | 2024-04-02 | Masimo Corportation | Apparatus and method for creating a stable optical interface |
US10278626B2 (en) | 2006-03-17 | 2019-05-07 | Masimo Corporation | Apparatus and method for creating a stable optical interface |
US10226576B2 (en) | 2006-05-15 | 2019-03-12 | Masimo Corporation | Sepsis monitor |
US11191485B2 (en) | 2006-06-05 | 2021-12-07 | Masimo Corporation | Parameter upgrade system |
US11607139B2 (en) | 2006-09-20 | 2023-03-21 | Masimo Corporation | Congenital heart disease monitor |
US10588518B2 (en) | 2006-09-20 | 2020-03-17 | Masimo Corporation | Congenital heart disease monitor |
US10912524B2 (en) | 2006-09-22 | 2021-02-09 | Masimo Corporation | Modular patient monitor |
US10219746B2 (en) | 2006-10-12 | 2019-03-05 | Masimo Corporation | Oximeter probe off indicator defining probe off space |
US10993643B2 (en) | 2006-10-12 | 2021-05-04 | Masimo Corporation | Patient monitor capable of monitoring the quality of attached probes and accessories |
US10194847B2 (en) | 2006-10-12 | 2019-02-05 | Masimo Corporation | Perfusion index smoother |
US11006867B2 (en) | 2006-10-12 | 2021-05-18 | Masimo Corporation | Perfusion index smoother |
US11672447B2 (en) | 2006-10-12 | 2023-06-13 | Masimo Corporation | Method and apparatus for calibration to reduce coupling between signals in a measurement system |
US11857319B2 (en) | 2006-10-12 | 2024-01-02 | Masimo Corporation | System and method for monitoring the life of a physiological sensor |
US11224381B2 (en) | 2006-10-12 | 2022-01-18 | Masimo Corporation | Oximeter probe off indicator defining probe off space |
US11317837B2 (en) | 2006-10-12 | 2022-05-03 | Masimo Corporation | System and method for monitoring the life of a physiological sensor |
US11857315B2 (en) | 2006-10-12 | 2024-01-02 | Masimo Corporation | Patient monitor capable of monitoring the quality of attached probes and accessories |
US11759130B2 (en) | 2006-10-12 | 2023-09-19 | Masimo Corporation | Perfusion index smoother |
US9861305B1 (en) | 2006-10-12 | 2018-01-09 | Masimo Corporation | Method and apparatus for calibration to reduce coupling between signals in a measurement system |
US10772542B2 (en) | 2006-10-12 | 2020-09-15 | Masimo Corporation | Method and apparatus for calibration to reduce coupling between signals in a measurement system |
US10863938B2 (en) | 2006-10-12 | 2020-12-15 | Masimo Corporation | System and method for monitoring the life of a physiological sensor |
US10342470B2 (en) | 2006-10-12 | 2019-07-09 | Masimo Corporation | System and method for monitoring the life of a physiological sensor |
US9949676B2 (en) | 2006-10-12 | 2018-04-24 | Masimo Corporation | Patient monitor capable of monitoring the quality of attached probes and accessories |
US10799163B2 (en) | 2006-10-12 | 2020-10-13 | Masimo Corporation | Perfusion index smoother |
US10463284B2 (en) | 2006-11-29 | 2019-11-05 | Cercacor Laboratories, Inc. | Optical sensor including disposable and reusable elements |
US11229374B2 (en) | 2006-12-09 | 2022-01-25 | Masimo Corporation | Plethysmograph variability processor |
US10918341B2 (en) | 2006-12-22 | 2021-02-16 | Masimo Corporation | Physiological parameter system |
US11229408B2 (en) | 2006-12-22 | 2022-01-25 | Masimo Corporation | Optical patient monitor |
US11234655B2 (en) | 2007-01-20 | 2022-02-01 | Masimo Corporation | Perfusion trend indicator |
US10980457B2 (en) | 2007-04-21 | 2021-04-20 | Masimo Corporation | Tissue profile wellness monitor |
US9848807B2 (en) | 2007-04-21 | 2017-12-26 | Masimo Corporation | Tissue profile wellness monitor |
US11647923B2 (en) | 2007-04-21 | 2023-05-16 | Masimo Corporation | Tissue profile wellness monitor |
US10251586B2 (en) | 2007-04-21 | 2019-04-09 | Masimo Corporation | Tissue profile wellness monitor |
US11660028B2 (en) | 2008-03-04 | 2023-05-30 | Masimo Corporation | Multispot monitoring for use in optical coherence tomography |
US10368787B2 (en) | 2008-03-04 | 2019-08-06 | Masimo Corporation | Flowometry in optical coherence tomography for analyte level estimation |
US11033210B2 (en) | 2008-03-04 | 2021-06-15 | Masimo Corporation | Multispot monitoring for use in optical coherence tomography |
US11426105B2 (en) | 2008-03-04 | 2022-08-30 | Masimo Corporation | Flowometry in optical coherence tomography for analyte level estimation |
US10292664B2 (en) | 2008-05-02 | 2019-05-21 | Masimo Corporation | Monitor configuration system |
US11622733B2 (en) | 2008-05-02 | 2023-04-11 | Masimo Corporation | Monitor configuration system |
US10524706B2 (en) | 2008-05-05 | 2020-01-07 | Masimo Corporation | Pulse oximetry system with electrical decoupling circuitry |
US11412964B2 (en) | 2008-05-05 | 2022-08-16 | Masimo Corporation | Pulse oximetry system with electrical decoupling circuitry |
US11642037B2 (en) | 2008-07-03 | 2023-05-09 | Masimo Corporation | User-worn device for noninvasively measuring a physiological parameter of a user |
US11647914B2 (en) | 2008-07-03 | 2023-05-16 | Masimo Corporation | User-worn device for noninvasively measuring a physiological parameter of a user |
US11484229B2 (en) | 2008-07-03 | 2022-11-01 | Masimo Corporation | User-worn device for noninvasively measuring a physiological parameter of a user |
US10702194B1 (en) | 2008-07-03 | 2020-07-07 | Masimo Corporation | Multi-stream data collection system for noninvasive measurement of blood constituents |
US11751773B2 (en) | 2008-07-03 | 2023-09-12 | Masimo Corporation | Emitter arrangement for physiological measurements |
US11426103B2 (en) | 2008-07-03 | 2022-08-30 | Masimo Corporation | Multi-stream data collection system for noninvasive measurement of blood constituents |
US10582886B2 (en) | 2008-07-03 | 2020-03-10 | Masimo Corporation | Multi-stream data collection system for noninvasive measurement of blood constituents |
US10588554B2 (en) | 2008-07-03 | 2020-03-17 | Masimo Corporation | Multi-stream data collection system for noninvasive measurement of blood constituents |
US11484230B2 (en) | 2008-07-03 | 2022-11-01 | Masimo Corporation | User-worn device for noninvasively measuring a physiological parameter of a user |
US10758166B2 (en) | 2008-07-03 | 2020-09-01 | Masimo Corporation | Multi-stream data collection system for noninvasive measurement of blood constituents |
US10709366B1 (en) | 2008-07-03 | 2020-07-14 | Masimo Corporation | Multi-stream data collection system for noninvasive measurement of blood constituents |
US10945648B2 (en) | 2008-07-03 | 2021-03-16 | Masimo Corporation | User-worn device for noninvasively measuring a physiological parameter of a user |
US11642036B2 (en) | 2008-07-03 | 2023-05-09 | Masimo Corporation | User-worn device for noninvasively measuring a physiological parameter of a user |
US10912502B2 (en) | 2008-07-03 | 2021-02-09 | Masimo Corporation | User-worn device for noninvasively measuring a physiological parameter of a user |
US11638532B2 (en) | 2008-07-03 | 2023-05-02 | Masimo Corporation | User-worn device for noninvasively measuring a physiological parameter of a user |
US10702195B1 (en) | 2008-07-03 | 2020-07-07 | Masimo Corporation | Multi-stream data collection system for noninvasive measurement of blood constituents |
US10912500B2 (en) | 2008-07-03 | 2021-02-09 | Masimo Corporation | Multi-stream data collection system for noninvasive measurement of blood constituents |
US10631765B1 (en) | 2008-07-03 | 2020-04-28 | Masimo Corporation | Multi-stream data collection system for noninvasive measurement of blood constituents |
US10624564B1 (en) | 2008-07-03 | 2020-04-21 | Masimo Corporation | Multi-stream data collection system for noninvasive measurement of blood constituents |
US10624563B2 (en) | 2008-07-03 | 2020-04-21 | Masimo Corporation | Multi-stream data collection system for noninvasive measurement of blood constituents |
US10912501B2 (en) | 2008-07-03 | 2021-02-09 | Masimo Corporation | User-worn device for noninvasively measuring a physiological parameter of a user |
US10588553B2 (en) | 2008-07-03 | 2020-03-17 | Masimo Corporation | Multi-stream data collection system for noninvasive measurement of blood constituents |
US10617338B2 (en) | 2008-07-03 | 2020-04-14 | Masimo Corporation | Multi-stream data collection system for noninvasive measurement of blood constituents |
US10743803B2 (en) | 2008-07-03 | 2020-08-18 | Masimo Corporation | Multi-stream data collection system for noninvasive measurement of blood constituents |
US10610138B2 (en) | 2008-07-03 | 2020-04-07 | Masimo Corporation | Multi-stream data collection system for noninvasive measurement of blood constituents |
USRE47249E1 (en) | 2008-07-29 | 2019-02-19 | Masimo Corporation | Alarm suspend system |
USRE47244E1 (en) | 2008-07-29 | 2019-02-19 | Masimo Corporation | Alarm suspend system |
USRE47353E1 (en) | 2008-07-29 | 2019-04-16 | Masimo Corporation | Alarm suspend system |
US11564593B2 (en) | 2008-09-15 | 2023-01-31 | Masimo Corporation | Gas sampling line |
US10952641B2 (en) | 2008-09-15 | 2021-03-23 | Masimo Corporation | Gas sampling line |
US10548561B2 (en) | 2008-12-30 | 2020-02-04 | Masimo Corporation | Acoustic sensor assembly |
US11559275B2 (en) | 2008-12-30 | 2023-01-24 | Masimo Corporation | Acoustic sensor assembly |
US11426125B2 (en) | 2009-02-16 | 2022-08-30 | Masimo Corporation | Physiological measurement device |
US11432771B2 (en) | 2009-02-16 | 2022-09-06 | Masimo Corporation | Physiological measurement device |
US10292657B2 (en) | 2009-02-16 | 2019-05-21 | Masimo Corporation | Ear sensor |
US11877867B2 (en) | 2009-02-16 | 2024-01-23 | Masimo Corporation | Physiological measurement device |
US10007758B2 (en) | 2009-03-04 | 2018-06-26 | Masimo Corporation | Medical monitoring system |
US11145408B2 (en) | 2009-03-04 | 2021-10-12 | Masimo Corporation | Medical communication protocol translator |
US10366787B2 (en) | 2009-03-04 | 2019-07-30 | Masimo Corporation | Physiological alarm threshold determination |
US10255994B2 (en) | 2009-03-04 | 2019-04-09 | Masimo Corporation | Physiological parameter alarm delay |
US10325681B2 (en) | 2009-03-04 | 2019-06-18 | Masimo Corporation | Physiological alarm threshold determination |
US11158421B2 (en) | 2009-03-04 | 2021-10-26 | Masimo Corporation | Physiological parameter alarm delay |
US10032002B2 (en) | 2009-03-04 | 2018-07-24 | Masimo Corporation | Medical monitoring system |
US11133105B2 (en) | 2009-03-04 | 2021-09-28 | Masimo Corporation | Medical monitoring system |
US11923080B2 (en) | 2009-03-04 | 2024-03-05 | Masimo Corporation | Medical monitoring system |
US11087875B2 (en) | 2009-03-04 | 2021-08-10 | Masimo Corporation | Medical monitoring system |
US11515664B2 (en) | 2009-03-11 | 2022-11-29 | Masimo Corporation | Magnetic connector |
US11848515B1 (en) | 2009-03-11 | 2023-12-19 | Masimo Corporation | Magnetic connector |
US10205272B2 (en) | 2009-03-11 | 2019-02-12 | Masimo Corporation | Magnetic connector |
US10855023B2 (en) | 2009-03-11 | 2020-12-01 | Masimo Corporation | Magnetic connector for a data communications cable |
US10342487B2 (en) | 2009-05-19 | 2019-07-09 | Masimo Corporation | Disposable components for reusable physiological sensor |
US11331042B2 (en) | 2009-05-19 | 2022-05-17 | Masimo Corporation | Disposable components for reusable physiological sensor |
US11752262B2 (en) | 2009-05-20 | 2023-09-12 | Masimo Corporation | Hemoglobin display and patient treatment |
US10413666B2 (en) | 2009-05-20 | 2019-09-17 | Masimo Corporation | Hemoglobin display and patient treatment |
US10953156B2 (en) | 2009-05-20 | 2021-03-23 | Masimo Corporation | Hemoglobin display and patient treatment |
US9795739B2 (en) | 2009-05-20 | 2017-10-24 | Masimo Corporation | Hemoglobin display and patient treatment |
US11779247B2 (en) | 2009-07-29 | 2023-10-10 | Masimo Corporation | Non-invasive physiological sensor cover |
US10194848B1 (en) | 2009-07-29 | 2019-02-05 | Masimo Corporation | Non-invasive physiological sensor cover |
US10188331B1 (en) | 2009-07-29 | 2019-01-29 | Masimo Corporation | Non-invasive physiological sensor cover |
US11559227B2 (en) | 2009-07-29 | 2023-01-24 | Masimo Corporation | Non-invasive physiological sensor cover |
US10478107B2 (en) | 2009-07-29 | 2019-11-19 | Masimo Corporation | Non-invasive physiological sensor cover |
US11369293B2 (en) | 2009-07-29 | 2022-06-28 | Masimo Corporation | Non-invasive physiological sensor cover |
US10588556B2 (en) | 2009-07-29 | 2020-03-17 | Masimo Corporation | Non-invasive physiological sensor cover |
US10687715B2 (en) | 2009-09-15 | 2020-06-23 | Masimo Corporation | Non-invasive intravascular volume index monitor |
US10398320B2 (en) | 2009-09-17 | 2019-09-03 | Masimo Corporation | Optical-based physiological monitoring system |
US11103143B2 (en) | 2009-09-17 | 2021-08-31 | Masimo Corporation | Optical-based physiological monitoring system |
US11744471B2 (en) | 2009-09-17 | 2023-09-05 | Masimo Corporation | Optical-based physiological monitoring system |
US11342072B2 (en) | 2009-10-06 | 2022-05-24 | Cercacor Laboratories, Inc. | Optical sensing systems and methods for detecting a physiological condition of a patient |
US11114188B2 (en) | 2009-10-06 | 2021-09-07 | Cercacor Laboratories, Inc. | System for monitoring a physiological parameter of a user |
US10357209B2 (en) | 2009-10-15 | 2019-07-23 | Masimo Corporation | Bidirectional physiological information display |
US10463340B2 (en) | 2009-10-15 | 2019-11-05 | Masimo Corporation | Acoustic respiratory monitoring systems and methods |
US10349895B2 (en) | 2009-10-15 | 2019-07-16 | Masimo Corporation | Acoustic respiratory monitoring sensor having multiple sensing elements |
US10925544B2 (en) | 2009-10-15 | 2021-02-23 | Masimo Corporation | Acoustic respiratory monitoring sensor having multiple sensing elements |
US10980507B2 (en) | 2009-10-15 | 2021-04-20 | Masimo Corporation | Physiological acoustic monitoring system |
US10342497B2 (en) | 2009-10-15 | 2019-07-09 | Masimo Corporation | Physiological acoustic monitoring system |
US10750983B2 (en) | 2009-11-24 | 2020-08-25 | Cercacor Laboratories, Inc. | Physiological measurement system with automatic wavelength adjustment |
US11534087B2 (en) | 2009-11-24 | 2022-12-27 | Cercacor Laboratories, Inc. | Physiological measurement system with automatic wavelength adjustment |
US9839381B1 (en) | 2009-11-24 | 2017-12-12 | Cercacor Laboratories, Inc. | Physiological measurement system with automatic wavelength adjustment |
US10729402B2 (en) | 2009-12-04 | 2020-08-04 | Masimo Corporation | Calibration for multi-stage physiological monitors |
US11571152B2 (en) | 2009-12-04 | 2023-02-07 | Masimo Corporation | Calibration for multi-stage physiological monitors |
US11900775B2 (en) | 2009-12-21 | 2024-02-13 | Masimo Corporation | Modular patient monitor |
US10943450B2 (en) | 2009-12-21 | 2021-03-09 | Masimo Corporation | Modular patient monitor |
US10354504B2 (en) | 2009-12-21 | 2019-07-16 | Masimo Corporation | Modular patient monitor |
US9847002B2 (en) | 2009-12-21 | 2017-12-19 | Masimo Corporation | Modular patient monitor |
US11289199B2 (en) | 2010-01-19 | 2022-03-29 | Masimo Corporation | Wellness analysis system |
USRE47882E1 (en) | 2010-03-01 | 2020-03-03 | Masimo Corporation | Adaptive alarm system |
USRE49007E1 (en) | 2010-03-01 | 2022-04-05 | Masimo Corporation | Adaptive alarm system |
US9775570B2 (en) | 2010-03-01 | 2017-10-03 | Masimo Corporation | Adaptive alarm system |
USRE47218E1 (en) | 2010-03-01 | 2019-02-05 | Masimo Corporation | Adaptive alarm system |
US10729362B2 (en) | 2010-03-08 | 2020-08-04 | Masimo Corporation | Reprocessing of a physiological sensor |
US11484231B2 (en) | 2010-03-08 | 2022-11-01 | Masimo Corporation | Reprocessing of a physiological sensor |
US11399722B2 (en) | 2010-03-30 | 2022-08-02 | Masimo Corporation | Plethysmographic respiration rate detection |
US10271748B2 (en) | 2010-05-06 | 2019-04-30 | Masimo Corporation | Patient monitor for determining microcirculation state |
US11330996B2 (en) | 2010-05-06 | 2022-05-17 | Masimo Corporation | Patient monitor for determining microcirculation state |
US11234602B2 (en) | 2010-07-22 | 2022-02-01 | Masimo Corporation | Non-invasive blood pressure measurement system |
US10052037B2 (en) | 2010-07-22 | 2018-08-21 | Masimo Corporation | Non-invasive blood pressure measurement system |
US10299720B2 (en) | 2010-09-01 | 2019-05-28 | The General Hospital Corporation | Reversal of general anesthesia by administration of methylphenidate, amphetamine, modafinil, amantadine, and/or caffeine |
US11553876B2 (en) | 2010-09-01 | 2023-01-17 | The General Hospital Corporation | Reversal of general anesthesia by administration of methylphenidate, amphetamine, modafinil, amantadine, and/or caffeine |
US11717210B2 (en) | 2010-09-28 | 2023-08-08 | Masimo Corporation | Depth of consciousness monitor including oximeter |
US11399774B2 (en) | 2010-10-13 | 2022-08-02 | Masimo Corporation | Physiological measurement logic engine |
US10159412B2 (en) | 2010-12-01 | 2018-12-25 | Cercacor Laboratories, Inc. | Handheld processing device including medical applications for minimally and non invasive glucose measurements |
US10729335B2 (en) | 2010-12-01 | 2020-08-04 | Cercacor Laboratories, Inc. | Handheld processing device including medical applications for minimally and non invasive glucose measurements |
US9579039B2 (en) | 2011-01-10 | 2017-02-28 | Masimo Corporation | Non-invasive intravascular volume index monitor |
US11488715B2 (en) | 2011-02-13 | 2022-11-01 | Masimo Corporation | Medical characterization system |
US10271749B2 (en) | 2011-02-25 | 2019-04-30 | Masimo Corporation | Patient monitor for monitoring microcirculation |
US11363960B2 (en) | 2011-02-25 | 2022-06-21 | Masimo Corporation | Patient monitor for monitoring microcirculation |
US11272852B2 (en) | 2011-06-21 | 2022-03-15 | Masimo Corporation | Patient monitoring system |
US11925445B2 (en) | 2011-06-21 | 2024-03-12 | Masimo Corporation | Patient monitoring system |
US11109770B2 (en) | 2011-06-21 | 2021-09-07 | Masimo Corporation | Patient monitoring system |
US11439329B2 (en) | 2011-07-13 | 2022-09-13 | Masimo Corporation | Multiple measurement mode in a physiological sensor |
US11877824B2 (en) | 2011-08-17 | 2024-01-23 | Masimo Corporation | Modulated physiological sensor |
US11176801B2 (en) | 2011-08-19 | 2021-11-16 | Masimo Corporation | Health care sanitation monitoring system |
US11816973B2 (en) | 2011-08-19 | 2023-11-14 | Masimo Corporation | Health care sanitation monitoring system |
US9993207B2 (en) | 2011-10-13 | 2018-06-12 | Masimo Corporation | Medical monitoring hub |
US11241199B2 (en) | 2011-10-13 | 2022-02-08 | Masimo Corporation | System for displaying medical monitoring data |
US9913617B2 (en) | 2011-10-13 | 2018-03-13 | Masimo Corporation | Medical monitoring hub |
US9943269B2 (en) | 2011-10-13 | 2018-04-17 | Masimo Corporation | System for displaying medical monitoring data |
US10512436B2 (en) | 2011-10-13 | 2019-12-24 | Masimo Corporation | System for displaying medical monitoring data |
US10925550B2 (en) | 2011-10-13 | 2021-02-23 | Masimo Corporation | Medical monitoring hub |
US11179114B2 (en) | 2011-10-13 | 2021-11-23 | Masimo Corporation | Medical monitoring hub |
US11089982B2 (en) | 2011-10-13 | 2021-08-17 | Masimo Corporation | Robust fractional saturation determination |
US11786183B2 (en) | 2011-10-13 | 2023-10-17 | Masimo Corporation | Medical monitoring hub |
US11747178B2 (en) | 2011-10-27 | 2023-09-05 | Masimo Corporation | Physiological monitor gauge panel |
US10955270B2 (en) | 2011-10-27 | 2021-03-23 | Masimo Corporation | Physiological monitor gauge panel |
US11179111B2 (en) | 2012-01-04 | 2021-11-23 | Masimo Corporation | Automated CCHD screening and detection |
US10729384B2 (en) | 2012-01-04 | 2020-08-04 | Masimo Corporation | Automated condition screening and detection |
US11172890B2 (en) | 2012-01-04 | 2021-11-16 | Masimo Corporation | Automated condition screening and detection |
US10349898B2 (en) | 2012-01-04 | 2019-07-16 | Masimo Corporation | Automated CCHD screening and detection |
US10278648B2 (en) | 2012-01-04 | 2019-05-07 | Masimo Corporation | Automated CCHD screening and detection |
US11083397B2 (en) | 2012-02-09 | 2021-08-10 | Masimo Corporation | Wireless patient monitoring device |
US11918353B2 (en) | 2012-02-09 | 2024-03-05 | Masimo Corporation | Wireless patient monitoring device |
US10188296B2 (en) | 2012-02-09 | 2019-01-29 | Masimo Corporation | Wireless patient monitoring device |
US10149616B2 (en) | 2012-02-09 | 2018-12-11 | Masimo Corporation | Wireless patient monitoring device |
US11132117B2 (en) | 2012-03-25 | 2021-09-28 | Masimo Corporation | Physiological monitor touchscreen interface |
US11071480B2 (en) | 2012-04-17 | 2021-07-27 | Masimo Corporation | Hypersaturation index |
US10542905B2 (en) * | 2012-05-30 | 2020-01-28 | Oxford University Innovation Limited | Perception loss detection |
US20150148700A1 (en) * | 2012-05-30 | 2015-05-28 | Isis Innovation Limited | Perception loss detection |
US10542903B2 (en) | 2012-06-07 | 2020-01-28 | Masimo Corporation | Depth of consciousness monitor |
US11069461B2 (en) | 2012-08-01 | 2021-07-20 | Masimo Corporation | Automated assembly sensor cable |
US11557407B2 (en) | 2012-08-01 | 2023-01-17 | Masimo Corporation | Automated assembly sensor cable |
USD989112S1 (en) | 2012-09-20 | 2023-06-13 | Masimo Corporation | Display screen or portion thereof with a graphical user interface for physiological monitoring |
US11504002B2 (en) | 2012-09-20 | 2022-11-22 | Masimo Corporation | Physiological monitoring system |
US11020084B2 (en) | 2012-09-20 | 2021-06-01 | Masimo Corporation | Acoustic patient sensor coupler |
US10833983B2 (en) | 2012-09-20 | 2020-11-10 | Masimo Corporation | Intelligent medical escalation process |
US11887728B2 (en) | 2012-09-20 | 2024-01-30 | Masimo Corporation | Intelligent medical escalation process |
US9717458B2 (en) | 2012-10-20 | 2017-08-01 | Masimo Corporation | Magnetic-flap optical sensor |
US11452449B2 (en) | 2012-10-30 | 2022-09-27 | Masimo Corporation | Universal medical system |
US11367529B2 (en) | 2012-11-05 | 2022-06-21 | Cercacor Laboratories, Inc. | Physiological test credit method |
US11839470B2 (en) | 2013-01-16 | 2023-12-12 | Masimo Corporation | Active-pulse blood analysis system |
US10610139B2 (en) | 2013-01-16 | 2020-04-07 | Masimo Corporation | Active-pulse blood analysis system |
US11224363B2 (en) | 2013-01-16 | 2022-01-18 | Masimo Corporation | Active-pulse blood analysis system |
US10672260B2 (en) | 2013-03-13 | 2020-06-02 | Masimo Corporation | Systems and methods for monitoring a patient health network |
US11645905B2 (en) | 2013-03-13 | 2023-05-09 | Masimo Corporation | Systems and methods for monitoring a patient health network |
US9936917B2 (en) | 2013-03-14 | 2018-04-10 | Masimo Laboratories, Inc. | Patient monitor placement indicator |
US11504062B2 (en) | 2013-03-14 | 2022-11-22 | Masimo Corporation | Patient monitor placement indicator |
US10575779B2 (en) | 2013-03-14 | 2020-03-03 | Masimo Corporation | Patient monitor placement indicator |
US9849241B2 (en) | 2013-04-24 | 2017-12-26 | Fresenius Kabi Deutschland Gmbh | Method of operating a control device for controlling an infusion device |
US10314503B2 (en) | 2013-06-27 | 2019-06-11 | The General Hospital Corporation | Systems and methods for tracking non-stationary spectral structure and dynamics in physiological data |
US10383574B2 (en) | 2013-06-28 | 2019-08-20 | The General Hospital Corporation | Systems and methods to infer brain state during burst suppression |
US9891079B2 (en) | 2013-07-17 | 2018-02-13 | Masimo Corporation | Pulser with double-bearing position encoder for non-invasive physiological monitoring |
US11022466B2 (en) | 2013-07-17 | 2021-06-01 | Masimo Corporation | Pulser with double-bearing position encoder for non-invasive physiological monitoring |
US10980432B2 (en) | 2013-08-05 | 2021-04-20 | Masimo Corporation | Systems and methods for measuring blood pressure |
US10555678B2 (en) | 2013-08-05 | 2020-02-11 | Masimo Corporation | Blood pressure monitor with valve-chamber assembly |
US11944415B2 (en) | 2013-08-05 | 2024-04-02 | Masimo Corporation | Systems and methods for measuring blood pressure |
US11596363B2 (en) | 2013-09-12 | 2023-03-07 | Cercacor Laboratories, Inc. | Medical device management system |
US10602978B2 (en) | 2013-09-13 | 2020-03-31 | The General Hospital Corporation | Systems and methods for improved brain monitoring during general anesthesia and sedation |
US11717194B2 (en) | 2013-10-07 | 2023-08-08 | Masimo Corporation | Regional oximetry pod |
US11147518B1 (en) | 2013-10-07 | 2021-10-19 | Masimo Corporation | Regional oximetry signal processor |
US10799160B2 (en) | 2013-10-07 | 2020-10-13 | Masimo Corporation | Regional oximetry pod |
US10010276B2 (en) | 2013-10-07 | 2018-07-03 | Masimo Corporation | Regional oximetry user interface |
US11751780B2 (en) | 2013-10-07 | 2023-09-12 | Masimo Corporation | Regional oximetry sensor |
US10617335B2 (en) | 2013-10-07 | 2020-04-14 | Masimo Corporation | Regional oximetry sensor |
US11076782B2 (en) | 2013-10-07 | 2021-08-03 | Masimo Corporation | Regional oximetry user interface |
US9839379B2 (en) | 2013-10-07 | 2017-12-12 | Masimo Corporation | Regional oximetry pod |
US10832818B2 (en) | 2013-10-11 | 2020-11-10 | Masimo Corporation | Alarm notification system |
US11488711B2 (en) | 2013-10-11 | 2022-11-01 | Masimo Corporation | Alarm notification system |
US10828007B1 (en) | 2013-10-11 | 2020-11-10 | Masimo Corporation | Acoustic sensor with attachment portion |
US10825568B2 (en) | 2013-10-11 | 2020-11-03 | Masimo Corporation | Alarm notification system |
US11699526B2 (en) | 2013-10-11 | 2023-07-11 | Masimo Corporation | Alarm notification system |
US10279247B2 (en) | 2013-12-13 | 2019-05-07 | Masimo Corporation | Avatar-incentive healthcare therapy |
US10881951B2 (en) | 2013-12-13 | 2021-01-05 | Masimo Corporation | Avatar-incentive healthcare therapy |
US11673041B2 (en) | 2013-12-13 | 2023-06-13 | Masimo Corporation | Avatar-incentive healthcare therapy |
US11259745B2 (en) | 2014-01-28 | 2022-03-01 | Masimo Corporation | Autonomous drug delivery system |
US10086138B1 (en) | 2014-01-28 | 2018-10-02 | Masimo Corporation | Autonomous drug delivery system |
US11883190B2 (en) | 2014-01-28 | 2024-01-30 | Masimo Corporation | Autonomous drug delivery system |
US11696712B2 (en) | 2014-06-13 | 2023-07-11 | Vccb Holdings, Inc. | Alarm fatigue management systems and methods |
US10231670B2 (en) | 2014-06-19 | 2019-03-19 | Masimo Corporation | Proximity sensor in pulse oximeter |
US11000232B2 (en) | 2014-06-19 | 2021-05-11 | Masimo Corporation | Proximity sensor in pulse oximeter |
US11581091B2 (en) | 2014-08-26 | 2023-02-14 | Vccb Holdings, Inc. | Real-time monitoring systems and methods in a healthcare environment |
US11331013B2 (en) | 2014-09-04 | 2022-05-17 | Masimo Corporation | Total hemoglobin screening sensor |
US11850024B2 (en) | 2014-09-18 | 2023-12-26 | Masimo Semiconductor, Inc. | Enhanced visible near-infrared photodiode and non-invasive physiological sensor |
US10568514B2 (en) | 2014-09-18 | 2020-02-25 | Masimo Semiconductor, Inc. | Enhanced visible near-infrared photodiode and non-invasive physiological sensor |
US11103134B2 (en) | 2014-09-18 | 2021-08-31 | Masimo Semiconductor, Inc. | Enhanced visible near-infrared photodiode and non-invasive physiological sensor |
US10383520B2 (en) | 2014-09-18 | 2019-08-20 | Masimo Semiconductor, Inc. | Enhanced visible near-infrared photodiode and non-invasive physiological sensor |
US11020050B2 (en) | 2014-09-22 | 2021-06-01 | Covidien Lp | Systems and methods for EEG monitoring |
US20160081617A1 (en) * | 2014-09-22 | 2016-03-24 | Covidien Lp | Systems and methods for eeg monitoring |
US10111617B2 (en) * | 2014-09-22 | 2018-10-30 | Covidien Lp | Systems and methods for EEG monitoring |
US10765367B2 (en) | 2014-10-07 | 2020-09-08 | Masimo Corporation | Modular physiological sensors |
US11717218B2 (en) | 2014-10-07 | 2023-08-08 | Masimo Corporation | Modular physiological sensor |
US10441196B2 (en) | 2015-01-23 | 2019-10-15 | Masimo Corporation | Nasal/oral cannula system and manufacturing |
US11437768B2 (en) | 2015-02-06 | 2022-09-06 | Masimo Corporation | Pogo pin connector |
US10568553B2 (en) | 2015-02-06 | 2020-02-25 | Masimo Corporation | Soft boot pulse oximetry sensor |
US10327337B2 (en) | 2015-02-06 | 2019-06-18 | Masimo Corporation | Fold flex circuit for LNOP |
US10784634B2 (en) | 2015-02-06 | 2020-09-22 | Masimo Corporation | Pogo pin connector |
US11178776B2 (en) | 2015-02-06 | 2021-11-16 | Masimo Corporation | Fold flex circuit for LNOP |
US11894640B2 (en) | 2015-02-06 | 2024-02-06 | Masimo Corporation | Pogo pin connector |
US11602289B2 (en) | 2015-02-06 | 2023-03-14 | Masimo Corporation | Soft boot pulse oximetry sensor |
US11903140B2 (en) | 2015-02-06 | 2024-02-13 | Masimo Corporation | Fold flex circuit for LNOP |
US10205291B2 (en) | 2015-02-06 | 2019-02-12 | Masimo Corporation | Pogo pin connector |
US20160287169A1 (en) * | 2015-03-31 | 2016-10-06 | Oulun Yliopisto | Apparatus and method for electroencephalographic examination |
US10702208B2 (en) * | 2015-03-31 | 2020-07-07 | Cerenion Oy | Apparatus and method for electroencephalographic examination |
US11291415B2 (en) | 2015-05-04 | 2022-04-05 | Cercacor Laboratories, Inc. | Noninvasive sensor system with visual infographic display |
US10524738B2 (en) | 2015-05-04 | 2020-01-07 | Cercacor Laboratories, Inc. | Noninvasive sensor system with visual infographic display |
US11653862B2 (en) | 2015-05-22 | 2023-05-23 | Cercacor Laboratories, Inc. | Non-invasive optical physiological differential pathlength sensor |
US10687743B1 (en) | 2015-07-02 | 2020-06-23 | Masimo Corporation | Physiological measurement devices, systems, and methods |
US10448871B2 (en) | 2015-07-02 | 2019-10-22 | Masimo Corporation | Advanced pulse oximetry sensor |
US10470695B2 (en) | 2015-07-02 | 2019-11-12 | Masimo Corporation | Advanced pulse oximetry sensor |
US10687744B1 (en) | 2015-07-02 | 2020-06-23 | Masimo Corporation | Physiological measurement devices, systems, and methods |
US10638961B2 (en) | 2015-07-02 | 2020-05-05 | Masimo Corporation | Physiological measurement devices, systems, and methods |
US10646146B2 (en) | 2015-07-02 | 2020-05-12 | Masimo Corporation | Physiological monitoring devices, systems, and methods |
US10722159B2 (en) | 2015-07-02 | 2020-07-28 | Masimo Corporation | Physiological monitoring devices, systems, and methods |
US10687745B1 (en) | 2015-07-02 | 2020-06-23 | Masimo Corporation | Physiological monitoring devices, systems, and methods |
US10991135B2 (en) | 2015-08-11 | 2021-04-27 | Masimo Corporation | Medical monitoring analysis and replay including indicia responsive to light attenuated by body tissue |
US11605188B2 (en) | 2015-08-11 | 2023-03-14 | Masimo Corporation | Medical monitoring analysis and replay including indicia responsive to light attenuated by body tissue |
US10448844B2 (en) | 2015-08-31 | 2019-10-22 | Masimo Corporation | Systems and methods for patient fall detection |
US11576582B2 (en) | 2015-08-31 | 2023-02-14 | Masimo Corporation | Patient-worn wireless physiological sensor |
US10383527B2 (en) | 2015-08-31 | 2019-08-20 | Masimo Corporation | Wireless patient monitoring systems and methods |
US10736518B2 (en) | 2015-08-31 | 2020-08-11 | Masimo Corporation | Systems and methods to monitor repositioning of a patient |
US11089963B2 (en) | 2015-08-31 | 2021-08-17 | Masimo Corporation | Systems and methods for patient fall detection |
US10226187B2 (en) | 2015-08-31 | 2019-03-12 | Masimo Corporation | Patient-worn wireless physiological sensor |
US11504066B1 (en) | 2015-09-04 | 2022-11-22 | Cercacor Laboratories, Inc. | Low-noise sensor system |
US11864922B2 (en) | 2015-09-04 | 2024-01-09 | Cercacor Laboratories, Inc. | Low-noise sensor system |
US11679579B2 (en) | 2015-12-17 | 2023-06-20 | Masimo Corporation | Varnish-coated release liner |
US10993662B2 (en) | 2016-03-04 | 2021-05-04 | Masimo Corporation | Nose sensor |
US10537285B2 (en) | 2016-03-04 | 2020-01-21 | Masimo Corporation | Nose sensor |
US11931176B2 (en) | 2016-03-04 | 2024-03-19 | Masimo Corporation | Nose sensor |
US11272883B2 (en) | 2016-03-04 | 2022-03-15 | Masimo Corporation | Physiological sensor |
US11191484B2 (en) | 2016-04-29 | 2021-12-07 | Masimo Corporation | Optical sensor tape |
US11153089B2 (en) | 2016-07-06 | 2021-10-19 | Masimo Corporation | Secure and zero knowledge data sharing for cloud applications |
US11706029B2 (en) | 2016-07-06 | 2023-07-18 | Masimo Corporation | Secure and zero knowledge data sharing for cloud applications |
US10617302B2 (en) | 2016-07-07 | 2020-04-14 | Masimo Corporation | Wearable pulse oximeter and respiration monitor |
US11202571B2 (en) | 2016-07-07 | 2021-12-21 | Masimo Corporation | Wearable pulse oximeter and respiration monitor |
US11076777B2 (en) | 2016-10-13 | 2021-08-03 | Masimo Corporation | Systems and methods for monitoring orientation to reduce pressure ulcer formation |
US10786168B2 (en) | 2016-11-29 | 2020-09-29 | The General Hospital Corporation | Systems and methods for analyzing electrophysiological data from patients undergoing medical treatments |
US11504058B1 (en) | 2016-12-02 | 2022-11-22 | Masimo Corporation | Multi-site noninvasive measurement of a physiological parameter |
US11864890B2 (en) | 2016-12-22 | 2024-01-09 | Cercacor Laboratories, Inc. | Methods and devices for detecting intensity of light with translucent detector |
US10750984B2 (en) | 2016-12-22 | 2020-08-25 | Cercacor Laboratories, Inc. | Methods and devices for detecting intensity of light with translucent detector |
US11825536B2 (en) | 2017-01-18 | 2023-11-21 | Masimo Corporation | Patient-worn wireless physiological sensor with pairing functionality |
US11291061B2 (en) | 2017-01-18 | 2022-03-29 | Masimo Corporation | Patient-worn wireless physiological sensor with pairing functionality |
US10721785B2 (en) | 2017-01-18 | 2020-07-21 | Masimo Corporation | Patient-worn wireless physiological sensor with pairing functionality |
US11024064B2 (en) | 2017-02-24 | 2021-06-01 | Masimo Corporation | Augmented reality system for displaying patient data |
US11410507B2 (en) | 2017-02-24 | 2022-08-09 | Masimo Corporation | Localized projection of audible noises in medical settings |
US11096631B2 (en) | 2017-02-24 | 2021-08-24 | Masimo Corporation | Modular multi-parameter patient monitoring device |
US11086609B2 (en) | 2017-02-24 | 2021-08-10 | Masimo Corporation | Medical monitoring hub |
US10667762B2 (en) | 2017-02-24 | 2020-06-02 | Masimo Corporation | Modular multi-parameter patient monitoring device |
US11886858B2 (en) | 2017-02-24 | 2024-01-30 | Masimo Corporation | Medical monitoring hub |
US11417426B2 (en) | 2017-02-24 | 2022-08-16 | Masimo Corporation | System for displaying medical monitoring data |
US10956950B2 (en) | 2017-02-24 | 2021-03-23 | Masimo Corporation | Managing dynamic licenses for physiological parameters in a patient monitoring environment |
US11596365B2 (en) | 2017-02-24 | 2023-03-07 | Masimo Corporation | Modular multi-parameter patient monitoring device |
US10327713B2 (en) | 2017-02-24 | 2019-06-25 | Masimo Corporation | Modular multi-parameter patient monitoring device |
US10388120B2 (en) | 2017-02-24 | 2019-08-20 | Masimo Corporation | Localized projection of audible noises in medical settings |
US11901070B2 (en) | 2017-02-24 | 2024-02-13 | Masimo Corporation | System for displaying medical monitoring data |
US11816771B2 (en) | 2017-02-24 | 2023-11-14 | Masimo Corporation | Augmented reality system for displaying patient data |
US11830349B2 (en) | 2017-02-24 | 2023-11-28 | Masimo Corporation | Localized projection of audible noises in medical settings |
US11185262B2 (en) | 2017-03-10 | 2021-11-30 | Masimo Corporation | Pneumonia screener |
US10849554B2 (en) | 2017-04-18 | 2020-12-01 | Masimo Corporation | Nose sensor |
US11534110B2 (en) | 2017-04-18 | 2022-12-27 | Masimo Corporation | Nose sensor |
US10918281B2 (en) | 2017-04-26 | 2021-02-16 | Masimo Corporation | Medical monitoring device having multiple configurations |
US11813036B2 (en) | 2017-04-26 | 2023-11-14 | Masimo Corporation | Medical monitoring device having multiple configurations |
USD835285S1 (en) | 2017-04-28 | 2018-12-04 | Masimo Corporation | Medical monitoring device |
USD835283S1 (en) | 2017-04-28 | 2018-12-04 | Masimo Corporation | Medical monitoring device |
USD835282S1 (en) | 2017-04-28 | 2018-12-04 | Masimo Corporation | Medical monitoring device |
US10856750B2 (en) | 2017-04-28 | 2020-12-08 | Masimo Corporation | Spot check measurement system |
USD835284S1 (en) | 2017-04-28 | 2018-12-04 | Masimo Corporation | Medical monitoring device |
US10932705B2 (en) | 2017-05-08 | 2021-03-02 | Masimo Corporation | System for displaying and controlling medical monitoring data |
US11006841B2 (en) | 2017-06-07 | 2021-05-18 | Covidien Lp | Systems and methods for detecting strokes |
US11026604B2 (en) | 2017-07-13 | 2021-06-08 | Cercacor Laboratories, Inc. | Medical monitoring device for harmonizing physiological measurements |
US11705666B2 (en) | 2017-08-15 | 2023-07-18 | Masimo Corporation | Water resistant connector for noninvasive patient monitor |
US10637181B2 (en) | 2017-08-15 | 2020-04-28 | Masimo Corporation | Water resistant connector for noninvasive patient monitor |
USD906970S1 (en) | 2017-08-15 | 2021-01-05 | Masimo Corporation | Connector |
US10505311B2 (en) | 2017-08-15 | 2019-12-10 | Masimo Corporation | Water resistant connector for noninvasive patient monitor |
USD890708S1 (en) | 2017-08-15 | 2020-07-21 | Masimo Corporation | Connector |
US11095068B2 (en) | 2017-08-15 | 2021-08-17 | Masimo Corporation | Water resistant connector for noninvasive patient monitor |
US11298021B2 (en) | 2017-10-19 | 2022-04-12 | Masimo Corporation | Medical monitoring system |
US10987066B2 (en) | 2017-10-31 | 2021-04-27 | Masimo Corporation | System for displaying oxygen state indications |
USD925597S1 (en) | 2017-10-31 | 2021-07-20 | Masimo Corporation | Display screen or portion thereof with graphical user interface |
US11766198B2 (en) | 2018-02-02 | 2023-09-26 | Cercacor Laboratories, Inc. | Limb-worn patient monitoring device |
US11844634B2 (en) | 2018-04-19 | 2023-12-19 | Masimo Corporation | Mobile patient alarm display |
US11109818B2 (en) | 2018-04-19 | 2021-09-07 | Masimo Corporation | Mobile patient alarm display |
US10667764B2 (en) | 2018-04-19 | 2020-06-02 | Masimo Corporation | Mobile patient alarm display |
US11883129B2 (en) | 2018-04-24 | 2024-01-30 | Cercacor Laboratories, Inc. | Easy insert finger sensor for transmission based spectroscopy sensor |
US11564642B2 (en) | 2018-06-06 | 2023-01-31 | Masimo Corporation | Opioid overdose monitoring |
US10939878B2 (en) | 2018-06-06 | 2021-03-09 | Masimo Corporation | Opioid overdose monitoring |
US10932729B2 (en) | 2018-06-06 | 2021-03-02 | Masimo Corporation | Opioid overdose monitoring |
US11627919B2 (en) | 2018-06-06 | 2023-04-18 | Masimo Corporation | Opioid overdose monitoring |
US11082786B2 (en) | 2018-07-10 | 2021-08-03 | Masimo Corporation | Patient monitor alarm speaker analyzer |
US11812229B2 (en) | 2018-07-10 | 2023-11-07 | Masimo Corporation | Patient monitor alarm speaker analyzer |
US10779098B2 (en) | 2018-07-10 | 2020-09-15 | Masimo Corporation | Patient monitor alarm speaker analyzer |
WO2020018595A1 (en) * | 2018-07-16 | 2020-01-23 | The General Hospital Corporation | System and method for monitoring neural signals |
CN112867441A (en) * | 2018-07-16 | 2021-05-28 | 通用医疗公司 | System and method for monitoring neural signals |
US20220142554A1 (en) * | 2018-07-16 | 2022-05-12 | The General Hospital Corporation | System and method for monitoring neural signals |
US11872156B2 (en) | 2018-08-22 | 2024-01-16 | Masimo Corporation | Core body temperature measurement |
USD998625S1 (en) | 2018-10-11 | 2023-09-12 | Masimo Corporation | Display screen or portion thereof with a graphical user interface |
USD917550S1 (en) | 2018-10-11 | 2021-04-27 | Masimo Corporation | Display screen or portion thereof with a graphical user interface |
USD916135S1 (en) | 2018-10-11 | 2021-04-13 | Masimo Corporation | Display screen or portion thereof with a graphical user interface |
USD917564S1 (en) | 2018-10-11 | 2021-04-27 | Masimo Corporation | Display screen or portion thereof with graphical user interface |
USD999245S1 (en) | 2018-10-11 | 2023-09-19 | Masimo Corporation | Display screen or portion thereof with graphical user interface |
USD999244S1 (en) | 2018-10-11 | 2023-09-19 | Masimo Corporation | Display screen or portion thereof with a graphical user interface |
USD999246S1 (en) | 2018-10-11 | 2023-09-19 | Masimo Corporation | Display screen or portion thereof with a graphical user interface |
US11445948B2 (en) | 2018-10-11 | 2022-09-20 | Masimo Corporation | Patient connector assembly with vertical detents |
USD998631S1 (en) | 2018-10-11 | 2023-09-12 | Masimo Corporation | Display screen or portion thereof with a graphical user interface |
USD998630S1 (en) | 2018-10-11 | 2023-09-12 | Masimo Corporation | Display screen or portion thereof with a graphical user interface |
US11406286B2 (en) | 2018-10-11 | 2022-08-09 | Masimo Corporation | Patient monitoring device with improved user interface |
US11389093B2 (en) | 2018-10-11 | 2022-07-19 | Masimo Corporation | Low noise oximetry cable |
US11272839B2 (en) | 2018-10-12 | 2022-03-15 | Ma Simo Corporation | System for transmission of sensor data using dual communication protocol |
USD897098S1 (en) | 2018-10-12 | 2020-09-29 | Masimo Corporation | Card holder set |
USD989327S1 (en) | 2018-10-12 | 2023-06-13 | Masimo Corporation | Holder |
US11464410B2 (en) | 2018-10-12 | 2022-10-11 | Masimo Corporation | Medical systems and methods |
US11684296B2 (en) | 2018-12-21 | 2023-06-27 | Cercacor Laboratories, Inc. | Noninvasive physiological sensor |
US11701043B2 (en) | 2019-04-17 | 2023-07-18 | Masimo Corporation | Blood pressure monitor attachment assembly |
US11637437B2 (en) | 2019-04-17 | 2023-04-25 | Masimo Corporation | Charging station for physiological monitoring device |
US11678829B2 (en) | 2019-04-17 | 2023-06-20 | Masimo Corporation | Physiological monitoring device attachment assembly |
USD933234S1 (en) | 2019-08-16 | 2021-10-12 | Masimo Corporation | Patient monitor |
USD917704S1 (en) | 2019-08-16 | 2021-04-27 | Masimo Corporation | Patient monitor |
USD919094S1 (en) | 2019-08-16 | 2021-05-11 | Masimo Corporation | Blood pressure device |
USD985498S1 (en) | 2019-08-16 | 2023-05-09 | Masimo Corporation | Connector |
USD921202S1 (en) | 2019-08-16 | 2021-06-01 | Masimo Corporation | Holder for a blood pressure device |
USD919100S1 (en) | 2019-08-16 | 2021-05-11 | Masimo Corporation | Holder for a patient monitor |
USD967433S1 (en) | 2019-08-16 | 2022-10-18 | Masimo Corporation | Patient monitor |
USD933233S1 (en) | 2019-08-16 | 2021-10-12 | Masimo Corporation | Blood pressure device |
US11832940B2 (en) | 2019-08-27 | 2023-12-05 | Cercacor Laboratories, Inc. | Non-invasive medical monitoring device for blood analyte measurements |
US11445960B2 (en) * | 2019-10-09 | 2022-09-20 | Trustees Of Boston University | Electrography system employing layered electrodes for improved spatial resolution |
EP4041067A4 (en) * | 2019-10-11 | 2023-10-04 | The Trustees of Columbia University in the City of New York | System, method and computer-accessible medium for anesthesia monitoring using electroencephalographic monitoring |
US11803623B2 (en) | 2019-10-18 | 2023-10-31 | Masimo Corporation | Display layout and interactive objects for patient monitoring |
USD950738S1 (en) | 2019-10-18 | 2022-05-03 | Masimo Corporation | Electrode pad |
USD927699S1 (en) | 2019-10-18 | 2021-08-10 | Masimo Corporation | Electrode pad |
EP4048343A4 (en) * | 2019-10-24 | 2023-12-06 | The Trustees of Columbia University in the City of New York | System, method, and computer-accessible medium for visualization and analysis of electroencephalogram oscillations in the alpha band |
US11879960B2 (en) | 2020-02-13 | 2024-01-23 | Masimo Corporation | System and method for monitoring clinical activities |
US11721105B2 (en) | 2020-02-13 | 2023-08-08 | Masimo Corporation | System and method for monitoring clinical activities |
US11730379B2 (en) | 2020-03-20 | 2023-08-22 | Masimo Corporation | Remote patient management and monitoring systems and methods |
US11957474B2 (en) | 2020-04-16 | 2024-04-16 | Masimo Corporation | Electrocardiogram device |
USD979516S1 (en) | 2020-05-11 | 2023-02-28 | Masimo Corporation | Connector |
USD965789S1 (en) | 2020-05-11 | 2022-10-04 | Masimo Corporation | Blood pressure monitor |
USD933232S1 (en) | 2020-05-11 | 2021-10-12 | Masimo Corporation | Blood pressure monitor |
US11730960B2 (en) * | 2020-05-27 | 2023-08-22 | Attune Neurosciences, Inc. | Ultrasound systems and associated devices and methods for modulating brain activity |
USD974193S1 (en) | 2020-07-27 | 2023-01-03 | Masimo Corporation | Wearable temperature measurement device |
USD980091S1 (en) | 2020-07-27 | 2023-03-07 | Masimo Corporation | Wearable temperature measurement device |
USD973685S1 (en) | 2020-09-30 | 2022-12-27 | Masimo Corporation | Display screen or portion thereof with graphical user interface |
USD973072S1 (en) | 2020-09-30 | 2022-12-20 | Masimo Corporation | Display screen or portion thereof with graphical user interface |
USD973686S1 (en) | 2020-09-30 | 2022-12-27 | Masimo Corporation | Display screen or portion thereof with graphical user interface |
US11951186B2 (en) | 2020-10-23 | 2024-04-09 | Willow Laboratories, Inc. | Indicator compounds, devices comprising indicator compounds, and methods of making and using the same |
USD997365S1 (en) | 2021-06-24 | 2023-08-29 | Masimo Corporation | Physiological nose sensor |
USD1000975S1 (en) | 2021-09-22 | 2023-10-10 | Masimo Corporation | Wearable temperature measurement device |
USD1022729S1 (en) | 2022-12-20 | 2024-04-16 | Masimo Corporation | Wearable temperature measurement device |
US11961616B2 (en) | 2023-01-20 | 2024-04-16 | Vccb Holdings, Inc. | Real-time monitoring systems and methods in a healthcare environment |
Also Published As
Publication number | Publication date |
---|---|
MX2013012933A (en) | 2014-02-27 |
EP2704630A4 (en) | 2014-10-15 |
WO2012154701A1 (en) | 2012-11-15 |
EP2704630A1 (en) | 2014-03-12 |
US11751770B2 (en) | 2023-09-12 |
EP2704630B1 (en) | 2023-07-26 |
JP2014515954A (en) | 2014-07-07 |
US20190117085A1 (en) | 2019-04-25 |
JP6109155B2 (en) | 2017-04-05 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US11751770B2 (en) | System and method for tracking brain states during administration of anesthesia | |
US20140316217A1 (en) | System and method for monitoring anesthesia and sedation using measures of brain coherence and synchrony | |
US20190374158A1 (en) | System and method for monitoring and controlling a state of a patient during and after administration of anesthetic compound | |
EP2498676B1 (en) | Brain activity as a marker of disease | |
US20200170575A1 (en) | Systems and methods to infer brain state during burst suppression | |
Höller et al. | High-frequency oscillations in epilepsy and surgical outcome. A meta-analysis | |
Ku et al. | Preferential inhibition of frontal-to-parietal feedback connectivity is a neurophysiologic correlate of general anesthesia in surgical patients | |
US20140323898A1 (en) | System and Method for Monitoring Level of Dexmedatomidine-Induced Sedation | |
US6338713B1 (en) | System and method for facilitating clinical decision making | |
EP2906112B1 (en) | System and method for monitoring and controlling a state of a patient during and after administration of anesthetic compound | |
US20160324446A1 (en) | System and method for determining neural states from physiological measurements | |
US20170231556A1 (en) | Systems and methods for predicting arousal to consciousness during general anesthesia and sedation | |
Bernabei et al. | Quantitative approaches to guide epilepsy surgery from intracranial EEG | |
Liparas et al. | Incorporating resting state dynamics in the analysis of encephalographic responses by means of the Mahalanobis–Taguchi strategy | |
Meesters et al. | Modeling of intracerebral interictal epileptic discharges: evidence for network interactions | |
Bandarabadi et al. | Robust and low complexity algorithms for seizure detection | |
US20220142554A1 (en) | System and method for monitoring neural signals | |
US20190142336A1 (en) | Systems and methods for determining response to anesthetic and sedative drugs using markers of brain function | |
Sakkalis et al. | Parametric and nonparametric EEG analysis for the evaluation of EEG activity in young children with controlled epilepsy | |
US11786132B2 (en) | Systems and methods for predicting arousal to consciousness during general anesthesia and sedation | |
Demuru et al. | To resect or not to resect? Unbiased performances of single and combined biomarkers in intra-operative corticography for tailoring during epilepsy surgery | |
Dai | Partial correlation and partial cross-correlation as multivariate measures of EEG connectivity | |
Lioi | EEG connectivity measures and their application to assess the depth of anaesthesia and sleep | |
Avramov | The use of bispectral analysis in patients undergoing intravenous sedation for third molar extractions |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: THE GENERAL HOSPITAL CORPORATION, MASSACHUSETTS Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:BROWN, EMERY N.;PURDON, PATRICK L.;CIMENSER, AYLIN;AND OTHERS;SIGNING DATES FROM 20140103 TO 20140114;REEL/FRAME:032243/0986 |
|
AS | Assignment |
Owner name: NATIONAL INSTITUTES OF HEALTH (NIH), U.S. DEPT. OF Free format text: CONFIRMATORY LICENSE;ASSIGNOR:MASSACHUSETTS GENERAL HOSPITAL;REEL/FRAME:036131/0392 Effective date: 20150717 |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |