CN103300849A - Electroencephalogram signal processing method - Google Patents
Electroencephalogram signal processing method Download PDFInfo
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- CN103300849A CN103300849A CN2013101230523A CN201310123052A CN103300849A CN 103300849 A CN103300849 A CN 103300849A CN 2013101230523 A CN2013101230523 A CN 2013101230523A CN 201310123052 A CN201310123052 A CN 201310123052A CN 103300849 A CN103300849 A CN 103300849A
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Abstract
The invention discloses an electroencephalogram signal processing method, which consists of the following steps of testing p subjects respectively according to the following steps, using a computer to select m analog lead signals of every subject, wherein a digital channel signal is formed by the lead signals of every subject, calculating the energy value of every digital channel signal, calculating the maximum energy of electroencephalogram, calculating the membership function of every digital channel signal, setting the fuzzy eigenvector of the digital channel signals, using the computer to draw the diagram of the signal-to-noise ratio spectrum of every channel signal, and displaying the combined signal-to-noise ratio spectrum differentiation diagram of the channel signals on the display screen of the computer. The method has the advantages that event-related features of electroencephalogram signals can be extracted effectively, and reliable data support can be provided for diagnoses of medical personnel; clinical guidance of electroencephalogram signals can be improved; and clinical promotion of an electroencephalogram analysis method can be facilitated.
Description
Technical field
The present invention relates to technical field of data processing, especially relate to a kind of brain-electrical signal processing method that can effectively extract the event correlated characteristic of EEG signals.
Background technology
Most disease is before morbidity or in the pathogenic process, the variation of brain potential has characteristics, observes the variation of electroencephalogram, can play certain directive function to the differentiation of disease.
But, to read the electroencephalogram waveform that electroencephalograph shows, and make judging it is a very complicated process according to the variation of waveform, the interpretation mistake of electroencephalogram usually can appear owing to the deficiency of doctor's qualification and experience.
The electroencephalogram data is different with imaging data, because the imaging data reaction is that brain anatomy changes, and electronic edition is corresponding with the slice, thin piece of giving patient, there is not the selectivity intercepting of information, therefore it is comparatively easy to carry the consultation of doctors, and the electroencephalogram reaction is that brain function variation (often the instant changes in time) and corresponding clinical behavioristics change, even 4 hours electroencephalogram of a record only shows with a kind of mode of leading (mode of leading commonly used has several) and prints, print calculating in 10 seconds with every paper, then need to print 1440, this almost is impossible accomplish.
Therefore, the electroencephalogram practitioner who has only the high-tech level is by accurately studying the original seriality electroencephalogram figure of noting in the computer of long time journey carefully, and gathers a credible and the report of accurate electroencephalogram just can really be played the clinical guidance effect.
Chinese patent mandate publication number: CN101690659A authorizes open day on April 7th, 2010, discloses a kind of brain wave analysis method, it is characterized in that, comprises following steps: step 1, collection eeg data; Step 2, the eeg data that collects is carried out time-domain analysis; Step 3, the brain computer electrograph is carried out pretreatment; Step 4, E.E.G is carried out frequency-domain analysis; Step 5, extraction E.E.G feature are carried out principal component analysis; Step 6, employing support vector machine are carried out the brainwave patterns classification.This invention can be extracted brain fag, anxiety and characteristic information such as be loosened; But there is the shortcoming of the event correlated characteristic that can not extract EEG signals in this invention.
Summary of the invention
The present invention is difficult to make credible and accurate electroencephalogram report in order to overcome prior art, and can't extract the deficiency of event correlated characteristic, and the brain-electrical signal processing method that can effectively extract the event correlated characteristic of EEG signals is provided.
To achieve these goals, the present invention is by the following technical solutions:
A kind of brain-electrical signal processing method comprises the steps:
(1-1) test p 〉=2 respectively for according to following step p experimenter:
Put on suitable electrode cap, be provided with 10-20 system electrode in the electrode cap, 10-20 system electrode is electrically connected with electroencephalograph;
Place the electrode area scalp with defat with the cotton ball soaked in alcohol wiping, lay 10-20 system electrode at scalp then;
(1-2) by electroencephalograph demonstration and record electroencephalogram, obtain the simulation lead signals of each electrode of each experimenter, store in the simulation lead signals input computer of each electrode of each experimenter; Computer is chosen each experimenter's m simulation lead signals, and experimenter's sequence number i=1 is set in m 〉=3;
(1-3) each simulation lead signals of i experimenter is sampled respectively, calculate the mean sample value of sample value of each simulation lead signals of identical sampling time, constitute a digital channel signal with the mean sample value of N different sampling times
,
(1-4) as i＜p, make the i value increase by 1, repeat the processing procedure of (1-3);
(1-5) computer utilizes formula
,
Calculate the energy value of each digital channel signal
(1-6) computer utilizes formula
Calculate the ceiling capacity of brain electricity
(1-7) computer utilizes formula
Calculate the membership function of each digital channel signal
(1-8) fuzzy eigenvector of computer settings digital channel signal
(1-9) computer is imported fuzzy eigenvector on the stochastic resonance system model that prestores in the computer
In, the draw signal to noise ratio spectrogram of each channel signal of stochastic resonance system model output signal-to-noise ratio SNR, computer;
Most of disease is before morbidity or in the pathogenic process, the variation of brain potential has characteristics, and the combination noise comparison component-bar chart that includes the event correlated characteristic can be diagnosed support more simply and intuitively to the medical worker.
At present, accidental resonance is used widely in fields such as Detection of Weak Signals, and this model comprises three factors: bistable state (or multistable) system, input signal and noise source.Usually in the bistable state potential well, by power-actuated overdamp Brownian movement of cycle particle the accidental resonance characteristic is described with one.
Its neutralization all is real parameter,
Be white Gaussian noise, its auto-correlation function is:
, M is input signal strength,
,
Be frequency modulating signal, D is noise intensity,
It is the EEG signal fuzzy eigenvector;
The signal to noise ratio formula is:
Be signal spectral density,
It is the noise intensity in the signal frequency range.
(1-10) in computer screen, demonstrate the combination noise comparison component-bar chart of channel signal;
(1-11) computer is selected the noise intensity scope of event correlated characteristic in combination noise comparison component-bar chart, the combination noise comparison component-bar chart in this noise intensity scope is amplified show.
The present invention is by extracting 3 or above simulation lead signals respectively to p experimenter, respectively the simulation lead signals is sampled, calculate the mean sample value, obtain p digital channel signal, calculate the energy value of each digital channel signal, calculate the ceiling capacity of brain electricity, calculate the membership function of each digital channel signal, each membership function is constituted fuzzy eigenvector, fuzzy eigenvector is imported in the stochastic resonance system model, adopt the accidental resonance spectral method to handle EEG signals, the noise intensity scope of selection event correlated characteristic is amplified demonstration to the combination noise comparison component-bar chart in this noise intensity scope.
The patient uses above-mentioned steps 100 to 900 test processs to test, obtain this patient's signal to noise ratio spectrogram, record the peak value of signal to noise ratio spectrogram, compare with the peak value of the signal to noise ratio curve of the enlarged drawing of combination noise comparison component-bar chart with this peak value, be this disease of patient type with the disease type of the immediate curve of peak value.
As preferably, described combination noise comparison component-bar chart comprises two dimension combination noise comparison component-bar chart and three-dimensional arrangement noise comparison component-bar chart.
As preferably, described noise intensity scope is 0 to 250 decibel.
As preferably, the simulation lead signals in the described step (1-1) is 20, is respectively left front volume point FP1, right front volume point FP2, volume point F3 and F4, central point C3 and C4, summit P3 and P4, pillow point O1 and O2, left front temporo F7, right front temporo F8, ezh FZ, central center line CZ, left side ear-lobe A1, the auris dextra A2 that hangs down, temporo T3 in the left side, temporo T4 in the right side, left back temporo T5 and the left back temporo T6 data of leading.
As preferably, the simulation lead signals of choosing in the described step (1-4) is 3 to 20.
As preferably, adopt 100 times/second to 1000 times/second sampling rate to sample in the step (1-3).
As preferably, the 10-20 system electrode in the step (1-2) was used saline soak before being placed on the scalp.
Therefore, the present invention has following beneficial effect: (1) can effectively extract the event correlated characteristic of EEG signals; (2) diagnosis for the medical worker provides the infallible data support; (3) reduced requirement to medical worker's professional skill and experience; (4) improved the clinical guidance of EEG signals; (5) be convenient to the clinical expansion of brain electricity analytical method.
Description of drawings
Fig. 1 is a kind of flow chart of the present invention;
Fig. 2 is the topography of 10-20 of the present invention system electrode;
Fig. 3 is the enlarged drawing of combination noise comparison component-bar chart.
The specific embodiment
The present invention will be further described below in conjunction with the drawings and specific embodiments.
Epilepsy is a kind of chronic neurological condition common, that show effect repeatedly, sickness rate is very high, according to the symptom in when morbidity and pass through eeg analysis, epilepsy can the part outbreak, generalized seizures and three big classes of other property outbreak, and partial seizures and generalized seizures be each self-contained many tiny classification again.In general, generalized seizures is than the serious symptom of other two big classes, very harmful to the patient, therefore concerning the epileptic effectively prediction for the patient, have great meaning.
Be a important step to such disease forecasting and diagnosis and treatment to the accurate classification of epilepsy, different classes of epilepsy adopts different classes of medicine to treat, and the therapeutic effect assessment is also all closely related with the classification of epilepsy.If under patient's epilepsy type misjudge situation, carry out pharmaceutical intervention or operation rashly, not only can not solve the problem of patient's epilepsy, and will lose best and optimal diagnosis and treatment opportunitys, even cause the life threat.Therefore, press for a kind of quick, accurate, noninvasive epileptic condition type judgement method.
Embodiment as shown in Figure 1 is a kind of brain-electrical signal processing method, comprises the steps:
Put on suitable electrode cap, be provided with 10-20 system electrode as shown in Figure 2 in the electrode cap, 10-20 system electrode is electrically connected with electroencephalograph;
The measurement of 10-20 system electrode fore-and-aft direction is to be as the criterion to the median line that occipital tuberosity is linked to be with the nasion, equidistant corresponding site is made left and right sides forehead point F41 and F42 about this line, volume point F3 and F4, central point C3 and C4, summit p3 and p4, pillow point O1 and O2, the position of forehead point is equivalent to the nasion to 10% place of occipital tuberosity at the nasion, volume point is equivalent to the nasion to the twice of volume point distance after forehead point, be nasion median line distance 20% place, the interval of central authorities, top, all points of pillow is 20% backward.
Place the electrode area scalp with defat with the cotton ball soaked in alcohol wiping, lay 10-20 system electrode at scalp then;
Please the experimenter sit comfortable, loosening all muscles, hands lies on the lower limb, order is closed in peace and quiet, opens electroencephalogram then and shows or recording system whether the baseline of observing the electroencephalogram waveform steady, whether electrode contacts well.As find that electrocardio is arranged, electromyographic signal disturbs, then mobile once with this electrode position that leads and be connected.Please the experimenter be in the steady joyful state of mood, observe and record the variation of electroencephalogram waveform.
Show and the record electroencephalogram by electroencephalograph, obtain the simulation lead signals of each electrode of each experimenter, store in the simulation lead signals input computer of each electrode of each experimenter;
Step 800, the fuzzy eigenvector of computer settings digital channel signal
Step 1000 demonstrates the combination noise comparison component-bar chart of 4 channel signals in computer screen;
4 channel signal differences in noise intensity [200,2000] zone are not obvious, and main difference concentrates in [0,200] zone.
For example: a patient uses above-mentioned steps 100 to 900 test processs to test, obtain this patient's signal to noise ratio spectrogram, the peak value that records this patient's signal to noise ratio spectrogram is 74dB, then compare with the peak value of the signal to noise ratio curve among this peak value and Fig. 3, immediate curve is the inattentive epilepsy of child, can judge that therefore this disease of patient is the inattentive epilepsy of child.
When using brain-electrical signal processing method of the present invention to detect disease type, as seen from Figure 3, different disease of patient types can clear, clear making a distinction.Therefore, brain-electrical signal processing method of the present invention has important directive significance to the type of accurate difference epileptic condition, be convenient to the big class of different epilepsies and group that the doctor accurately distinguishes different patients, be convenient in time adopt only method and medicament that the patient is treated, allow the smooth rehabilitation of more epileptic person.
Should be understood that present embodiment only to be used for explanation the present invention and be not used in and limit the scope of the invention.Should be understood that in addition those skilled in the art can make various changes or modifications the present invention after the content of having read the present invention's instruction, these equivalent form of values fall within the application's appended claims institute restricted portion equally.
Claims (7)
1. a brain-electrical signal processing method is characterized in that, comprises the steps:
(1-1) test p 〉=2 respectively for according to following step p experimenter:
Put on suitable electrode cap, be provided with 10-20 system electrode in the electrode cap, 10-20 system electrode is electrically connected with electroencephalograph;
Place the electrode area scalp with defat with the cotton ball soaked in alcohol wiping, lay 10-20 system electrode at scalp then;
Show and the record electroencephalogram by electroencephalograph, obtain the simulation lead signals of each electrode of each experimenter, store in the simulation lead signals input computer of each electrode of each experimenter;
(1-2) computer is chosen each experimenter's m simulation lead signals, and experimenter's sequence number i=1 is set in m 〉=3;
(1-3) each simulation lead signals of i experimenter is sampled respectively, calculate the mean sample value of sample value of each simulation lead signals of identical sampling time, constitute a digital channel signal with the mean sample value of N different sampling times
,
(1-4) as i＜p, make the i value increase by 1, repeat the processing procedure of (1-3);
(1-5) computer utilizes formula
,
Calculate the energy value of each digital channel signal
(1-6) computer utilizes formula
Calculate the ceiling capacity of brain electricity
(1-7) computer utilizes formula
Calculate the membership function of each digital channel signal
(1-8) fuzzy eigenvector of computer settings digital channel signal
(1-9) computer is imported fuzzy eigenvector on the stochastic resonance system model that prestores in the computer
In, the draw signal to noise ratio spectrogram of each channel signal of stochastic resonance system model output signal-to-noise ratio SNR, computer;
(1-10) in computer screen, demonstrate the combination noise comparison component-bar chart of channel signal;
(1-11) computer is selected the noise intensity scope of event correlated characteristic in combination noise comparison component-bar chart, the combination noise comparison component-bar chart in this noise intensity scope is amplified show.
2. brain-electrical signal processing method according to claim 1 is characterized in that, described combination noise comparison component-bar chart comprises two dimension combination noise comparison component-bar chart and three-dimensional arrangement noise comparison component-bar chart.
3. brain-electrical signal processing method according to claim 1 is characterized in that, described noise intensity scope is 0 to 250 decibel.
4. brain-electrical signal processing method according to claim 1 is characterized in that, the simulation lead signals in the described step (1-1) is 20, be respectively left front volume point FP1, right front volume point FP2, volume point F3 and F4, central point C3 and C4, summit P3 and P4, pillow point O1 and O2, left front temporo F7, right front temporo F8, ezh FZ, the center line CZ of central authorities, left ear-lobe A1, the auris dextra A2 that hangs down, temporo T3 in the left side, temporo T4 in the right side, left back temporo T5 and the left back temporo T6 data of leading.
5. brain-electrical signal processing method according to claim 1 is characterized in that, the simulation lead signals of choosing in the described step (1-4) is 3 to 20.
6. according to claim 1 or 2 or 3 or 4 or 5 described brain-electrical signal processing methods, it is characterized in that, adopt 100 times/second to 1000 times/second sampling rate to sample in the step (1-3).
7. according to claim 1 or 2 or 3 or 4 or 5 described brain-electrical signal processing methods, it is characterized in that the 10-20 system electrode in the step (1-2) was used saline soak before being placed on the scalp.
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| CN110558977A (en) * | 2019-09-09 | 2019-12-13 | 西北大学 | epileptic seizure electroencephalogram signal classification method based on machine learning fuzzy feature selection |
| CN111867448A (en) * | 2017-12-22 | 2020-10-30 | 波尓瑟兰尼提公司 | Method and system for calculating an indication of brain activity |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111867448A (en) * | 2017-12-22 | 2020-10-30 | 波尓瑟兰尼提公司 | Method and system for calculating an indication of brain activity |
| CN110558977A (en) * | 2019-09-09 | 2019-12-13 | 西北大学 | epileptic seizure electroencephalogram signal classification method based on machine learning fuzzy feature selection |
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