CN109893125A - A kind of brain comatose state recognition methods based on brain area information exchange - Google Patents
A kind of brain comatose state recognition methods based on brain area information exchange Download PDFInfo
- Publication number
- CN109893125A CN109893125A CN201910203614.2A CN201910203614A CN109893125A CN 109893125 A CN109893125 A CN 109893125A CN 201910203614 A CN201910203614 A CN 201910203614A CN 109893125 A CN109893125 A CN 109893125A
- Authority
- CN
- China
- Prior art keywords
- brain
- brain area
- comatose state
- information exchange
- comatose
- 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.)
- Pending
Links
Landscapes
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The brain comatose state recognition methods based on brain area information exchange that the present invention relates to a kind of.The present invention selects symmetrical six electrodes of forehead or so brain to acquire EEG signals according to 10-20 system standard, after obtaining forehead EEG signal, it is pre-processed, then delta is calculated separately, theta, the Phase synchronization value of all interchannels in alpha frequency range, and then left and right brain area is calculated across Phase synchronization index in brain area and brain area, finally brain comatose state is judged across brain area and brain area information interaction relationship according in T time.The present invention provides a kind of more convenient, accurate, reliable robustness neural markers for comatose state judgement, can be used as the important reference of doctor's clinic comatose state diagnosis.
Description
Technical field
The present invention relates to brain stupor deciding degree fields, and in particular to one kind is based on brain electricity Phase synchronization analytical technology from brain
The method that the angle of area's information exchange judges patient's brain comatose state.
Background technique
Medically, stupor degree is divided into light coma, coma in moderate depth, depth stupor and excessive stupor (brain death), wherein
First three stupor is reversible, and brain death is irreversible.
Excessively stupor refers to that full brain function includes the irreversible forfeiture of brainstem function, is to determine most may be used for body life termination
By foundation, received by more than 100 countries in the world including China at present.Clinically, judge the brain stupor of patient
Degree is a very arduous but vital task.Accurately can quickly make a definite diagnosis brain comatose state for patient family,
It is most important for medical team and potential organ recipient, not only facilitate in this way prevent from bringing to patient home it is huge
Sad and economic puzzlement, while the chance for obtaining organ transplant is also provided for some patients.But due to depth comatose patient and
The clinical physiological index of excessive comatose patient is very much like, causes its clinical diagnosis extremely difficult.Although most countries are
Corresponding brain death criterion has been formulated, but there are the urgent problems to be solved such as time-consuming, the big, poor robustness of risk.And work as
Preceding major part decision process requires whether confirmation patient remains autonomous respiration, needs to stop exhaling for patient within the regular hour
Suction machine oxygen supply, in addition the flat test of brain electricity needs duplicate acknowledgment, it is therefore more likely that delaying effective treatment time of patient and relating to
And very big risk, secondary injury is caused to patient.Therefore, how safe and accurate, be rapidly performed by stupor degree diagnosis and
Identification becomes urgent problem to be solved.
Electroencephalogram (EEG) is because it is with the advantages such as at low cost, temporal resolution is high, acquisition is noninvasive, it has also become clinical diagnosis
One of most effective householder method of brain states.Having many researchs at present has shown that, excessive comatose patient and depth stupor are suffered from
The EEG signals of person all have significant difference in numerous indexs: the EEG signals energy and signal such as depth comatose patient are complicated
The single channels indexs such as degree will be significantly higher than excessive comatose patient；In addition either from brain function network and brain effect network analysis knot
From the point of view of fruit, also all there is information exchange more significantly more than excessive comatose patient in depth comatose patient.These results of study show
The feasibility of brain stupor deciding degree is carried out based on EEG signals.But these results of study only indicate the brain of two class patients
There are significant differences for signal characteristics, lack specific threshold value and decision rule, are only doctor and provide basic reference, Wu Fazuo
For clinical endpoint.
Summary of the invention
In view of the deficiencies of the prior art, it is based on Phase synchronization brain function network analysis method, by patient's forehead or so
Half brain is analyzed and is compared across the information exchange relationship in brain area and brain area, and the present invention is proposed in low-frequency range (delta, theta
And alpha) across the brain area Phase synchronization index of depth comatose patient will be significantly higher than index value in brain area, and have comparable stabilization
Property, and excessively comatose patient shows mix then without this phenomenon, this phenomenon provides one for comatose state judgement
Kind more convenient, accurate, reliable robustness neural markers, can be used as the important references of doctor's clinic comatose state diagnosis according to
According to.
In order to achieve the above objectives, the technical solution adopted in the present invention is as follows:
Brain comatose patient scalp EEG signals are acquired first.Since patient lies in a comatose condition, cannot move easily, for
Safety and convenience consider, this programme according to 10-20 system standard select forehead or so symmetrical six electrodes of brain (Fp1,
Fp2, F3, F4, F7 and F8) acquisition EEG signals.After obtaining forehead EEG signal, it is pre-processed, to remove in signal
Noise and interference, then calculate separately delta, theta, the Phase synchronization value of all interchannels, Jin Erji in alpha frequency range
Left and right brain area is calculated across Phase synchronization index in brain area and brain area, to characterize brain in patients information exchange situation.When finally according to T
Interior (T is artificial setting time, generally higher than 30min), the comparative observation across brain area and brain area information interaction relationship provides
Patient's comatose state determines diagnostic recommendations.
Specific implementation step is as follows:
Step 1, eeg signal acquisition.
The scalp EEG signals of six electrodes (Fp1, Fp2, F3, F4, F7 and F8) of patient's forehead are acquired, and by A1 and A2
Two electrodes are respectively placed in left and right ear-lobe as reference electrode.
Step 2, EEG signals pretreatment.
The resulting EEG signals of step 1 are pre-processed.Pretreatment includes removal artefact, baseline correction, bandpass filtering
Deng.
Step 3, signal segment length selection.
It is calculated according to formula (1) and obtains delta (0.5-4Hz), theta (4-8Hz), three frequency ranges of alpha (8-13Hz)
Corresponding signal segment length；
L, C, S and F respectively indicate segment length, periodicity, sample rate and frequency range.If sample rate is 1000Hz, for
Delta (0.5-4Hz) frequency range, the segment length that sync index calculates are taken as 1000ms (non-overlap time window).That is, choosing
Select 3 or 4 periods.Similarly, the segment length of other two frequency band theta (4-8Hz), alpha (8-13Hz) selection is equal are as follows:
800ms。
Step 4, PGC demodulation value calculate.
According to the segment length of the calculated different frequency range of step 3, delta is calculated, every two is logical under theta and alpha frequency range
Phase synchronization relationship between road.
To continuous time signal x (t), an analytic signal Z is defined firstx(t),
Wherein i is the imaginary part of analytic signal；
For the Hilbert transform of x (t),
Wherein, P is Cauchy's principal value, and t is time variable, and τ is time window；
Ax(t) and Φx(t) be respectively signal x (t) instantaneous amplitude and instantaneous phase, calculation formula is as follows:
Similarly, the analytic signal Z of definition signal y (t)y(t), and instantaneous phase Φ is calculatedy(t)。
Specific PLV calculation formula is as follows:
PLV=| < exp (i { Φx(t)-Φy(t)})>|
Wherein<>indicates temporal mean value, the i of the same formula of i (2)；
The intra/inter- Phase synchronization index of step 5, brain area calculates.
Phase synchronization can react the correlation of information integration between brain different zones, therefore the present invention proposes that three is similar
Step index carries out a point brain area Synchronization Analysis, wherein the PLV mean value of 9 electrodes pair is defined as across brain area PS between half brain of left and right
(Inter-hemispheric phase synchronization, IHPS) index, it reflects the information between the brain area of left and right
Interaction；Left half brain, 3 electrodes (Fp1, F3, F7) constitute 3 electrodes pair, and the PLV mean value between them is defined as half brain PS of a left side
(Left hemispheric phase synchronization, LHPS) index, the message that it reflects left half intracerebral portion are handed over
Mutual mechanism；Right half brain, 3 electrodes (Fp2, F4, F8) constitute 3 electrodes pair, and the PLV mean value between them is defined as right half brain
PS (Right Hemispheric phase synchronization, RHPS) index, it reflects the information in right half intracerebral portion
Interaction mechanism.
By taking IHPS as an example, the calculating of IHPS is shown below:
Wherein, N indicates the number in across brain area channel pair.The calculating of LHPS and RHPS is identical with IHPS Computing Principle.
Step 6, the identification of brain comatose state.
In T time, IHPS under tri- low-frequency ranges of delta, theta and alpha is continuously recorded, LHPS and RHPS index,
Wherein T is artificial setting time, generally higher than 30min；If IHPS is all larger than the phenomenon that LHPS and RHPS, then it represents that at patient
In depth comatose state, continual cure is answered；Otherwise mix is shown as, then it represents that patient is likely to be at excessive comatose state,
Subsequent judgement measure should be taken.
The beneficial effects of the present invention are: the present invention provides a kind of based on brain electricity Phase synchronization from the angle of brain area information exchange
The method that degree judges patient's brain comatose state.Based on Phase synchronization brain function network analysis method, by patient's forehead or so
Half brain is analyzed and is compared across the information exchange relationship in brain area and brain area, and the present invention is proposed in low-frequency range (delta, theta
And alpha) across the brain area Phase synchronization index of depth comatose patient will be significantly higher than index value in brain area, and have comparable stabilization
Property, and excessively comatose patient shows mix then without this phenomenon, this phenomenon provides one for comatose state judgement
Kind more convenient, accurate, reliable robustness neural markers, can be used as the important references of doctor's clinic comatose state diagnosis according to
According to.Since this method has certain instantaneity and accuracy, it is expected to develop a kind of new visualization tool and carrys out adjuvant clinical
The diagnosis of brain comatose state.
Detailed description of the invention
Fig. 1 is specific implementation method flow chart of the present invention；
Fig. 2 is specific embodiment of the invention brain electrode position figure；
Fig. 3 is that two class patient's Phase synchronization indexes compare；
Fig. 4 is that depth comatose patient δ frequency range calculates IHPS, LHPS and RHPS index results in real time；
Fig. 5 is that excessive comatose patient δ frequency range calculates IHPS, LHPS and RHPS index results in real time.
Specific embodiment
With reference to the accompanying drawing, a kind of brain comatose state recognition methods based on brain area information exchange of the present invention is done and is retouched in detail
It states.
As shown in Figure 1, a kind of brain comatose state recognition methods based on brain area information exchange, comprising the following steps:
Step 1, brain electric data collecting: in June, 2004 in March, 2006, under the supervision of veteran clinician,
Through patient monitoring, people agrees to, has collected the eeg data of 36 adult patients.Signal acquisition uses NEUROSCAN ESI-64 system
System record EEG data, sample rate 1000Hz, electrode impedance is less than 8000 Ω.In view of the situation of patient, according to standard 10-
Six electrodes (Fp1, Fp2, F3, F4, F7 and F8) are placed on forehead by 20 systems, and put using A1 and A2 as reference electrode
It sets on ear-lobe.As shown in Figure 2.
Step 2, EEG signals pretreatment: it using going averaging method to carry out baseline correction to data, is then filtered using FIR
By signal filtering to 6 frequency bands, delta (0.5-4Hz), theta (4-8Hz), alpha (8-13Hz), low beta (13-
22Hz), highbeta (22-30Hz) and gamma (30-40Hz).
Step 3, signal segment length selection: PLV calculate when, the selection of data length is very crucial, thus calculate PLV it
Before, it first has to select suitable signal segment length.The long calculating of sync index calculation interval is as follows:
L, C, S and F respectively indicate segment length, periodicity, sample rate and frequency range.If sample rate is 1000Hz, for
Delta (0.5-4Hz) frequency range, the segment length that sync index calculates are taken as 1000ms (non-overlap time window).That is, choosing
Select 3 or 4 periods.Similarly, other two frequency band theta (4-8Hz), the segment length of alpha (8-13Hz) are equal are as follows: 800ms.
Step 4, PGC demodulation value calculate: according to the segment length of the calculated different frequency range of step 3, calculating delta, theta
And the Phase synchronization relationship under alpha frequency range between every two channel.
To continuous time signal x (t), an analytic signal Z is defined firstx(t),
Wherein, i is the imaginary part of analytic signal；
For the Hilbert transform of x (t),
Wherein, P is Cauchy's principal value, and t is time variable, and τ is time window；
Ax(t) and Φx(t) be respectively signal x (t) instantaneous amplitude and instantaneous phase, calculation formula is as follows:
Similarly, the analytic signal Z of definition signal y (t)y(t), and instantaneous phase Φ is calculatedy(t)。
Specific PLV calculation formula is as follows:
PLV=| < exp (i { Φx(t)-Φy(t})>|
Wherein<>indicates temporal mean value, the i of the same formula of i (2)；
The intra/inter- Phase synchronization index of step 5, brain area calculates:
5-1: two class patient's Phase synchronization difference analysis.All 15 interchannels of same type patient are counted in same frequency range
PLV mean value.As shown in figure 3, abscissa is 6 frequency ranges, ordinate indicates PLV mean value, while giving the standard deviation of PLV and showing
Work property inspection result, wherein Filled Rectangle indicates the PLV mean value of depth comatose patient, and hollow rectangle indicates excessive comatose patient
Average PLV, wherein symbol * indicate p < 0.05, * * indicate p < 0.01, n.s. indicate be not present significant difference.Pass through observation
It can be seen that the PLV mean value of depth comatose patient is all larger than the PLV mean value of excessive comatose patient in each frequency range.The result can benefit
It is explained with Phase synchronization/locking phase physical significance, i.e. PS can reflect from chaos to orderly process.It goes into a coma for depth
Brain chaotic behavior can be transferred to the ordering behavior of collective's system by the brain of patient, and excessively comatose patient does not have
The ability of cognitive operation is carried out, therefore, PS intensity is very weak, that is, causes PLV value smaller.It is worth noting that, in high frequency γ wave band
Between (p > 0.05) two class patient passageways PLV be not present significant difference, and δ, θ,There is significant difference with low β frequency range
(p<0.05).It is well known that EEG signals are concentrated mainly on slow wave when brain activity becomes relatively weak.Therefore above-mentioned knot
Fruit further illustrates brain area information exchange of the frontal lobe brain area information interaction than excessive comatose patient of depth comatose patient
It is stronger.
5-2: the significant result based on 5-1, respectively δ, θ,Low β, high β frequency range classifying type statistics patient are different
PLV average value, variance and the conspicuousness p value of brain area.As shown in table 5-2.1 and table 5-2.2, " * " expression passes through significance test
(p<0.05), n.s. indicate not pass through significance test (p>=0.05).By observation table 5-2.1 it can be found that in low-frequency range
(δ, θ and) the IHPS index of depth comatose patient is significantly greater than its LHPS and RHPS (IHPS > LHPS&RHPS) index, and exists
Significant difference (p < 0.05).On the one hand, the brain wave for again demonstrating coma patient is mainly low frequency slow wave, another aspect table
It is illustrated stronger for the half brain information interaction of left and right of depth comatose patient.This result is understand depth comatose patient big
Brain treatment mechanism opens new thinking.However, between the sync index of excessively comatose patient without occur above-mentioned phenomenon (p >
0.05).Based on result above, the present invention has robustness using this index phenomenon as clinically judgement brain stupor degree
Neural markers.
The intra/inter- PLV of the brain area of table 5-2.1 depth comatose patient
The intra/inter- PLV of the excessive comatose patient brain area of table 5-2.2
Step 6, the identification of brain comatose state: further potential between verifying index in view of the calculating high efficiency of the above method
Clinical efficacy, this step attempt EEG record during real-time statistics and show three classes coincident indicator.Based on sliding window to adopting
(length of window is set as 20s, and overlapping is 50%) for the eeg data real-time perfoming three classes coincident indicator calculating of collection.Based on 45
Trial carries out real-time synchronization index statistics, as a result (the depth stupor: 23 trial as shown in table 5-3；It excessively goes into a coma: 22
trial).For depth comatose patient in δ, θ andThe result that wave band meets the condition of IHPS > LHPS&RHPS is respectively
78.2%, 87% and 100%.It is worth noting that,The phenomenon that 23 trial of wave band are all satisfied IHPS > LHPS&RHPS.Together
Shi Faxian is for excessive comatose patient, and 22 trial in total are only in the probability that IHPS > LHPS&RHPS occur in three frequency ranges
4.7%.It is above-mentioned significant as a result, make it possible that subsequent design visualizes real-time detection apparatus, and provided more for doctor
The message diagnosis about brain comatose state.
The statistical result (PPS: meeting the probability of condition) of 45 trial of table 5-3
Fig. 4 and Fig. 5 respectively show certain bit depth comatose patient and the excessive comatose patient in certain position δ frequency band brain electricity synchronize refer to
Several real-time detection results.Wherein, black thick line, dotted line and filament respectively indicate IHPS, LHPS and RHPS index, and t indicates sliding
Time window, length 20s, overlapping 50%.Simultaneously it can be found that real-time results are still consistent with the above results.For depth
It spends comatose patient (IHPS > LHPS&RHPS index), i.e., black thick line, which is always positioned at, to be put, and is then occurred for excessive comatose patient
Random confusion phenomena.
For Clinical practicability, real time contrast's analysis of above-mentioned EEG record will provide related brain activity shape for doctor
The valuable clue of state analysis.Therefore, above-mentioned analysis can be diagnosed potentially as patient in clinical practice and prognosis is joined
It examines, and there is very strong timeliness and accuracy, and be expected to develop a kind of new visualization tool and carry out adjuvant clinical brain dusk
It is confused condition diagnosing.
Claims (5)
1. a kind of brain comatose state recognition methods based on brain area information exchange, it is characterised in that method includes the following steps:
Step 1, eeg signal acquisition
The scalp EEG signals of six electrodes (Fp1, Fp2, F3, F4, F7 and F8) of patient's forehead are acquired, and A1 and A2 two is electric
Pole is respectively placed in left and right ear-lobe as reference electrode；
Step 2, EEG signals pretreatment
The resulting EEG signals of step 1 are pre-processed；
Step 3, signal segment length selection
It is calculated according to formula (1) and obtains delta (0.5-4Hz), theta (4-8Hz), the phase of alpha (8-13Hz) three frequency ranges
Induction signal segment length；
L, C, S and F respectively indicate segment length, periodicity, sample rate and frequency range；
Step 4, PGC demodulation value calculate；
According to the segment length of the calculated different frequency range of step 3, delta, theta and alpha are calculated using PGC demodulation value (PLV)
Phase synchronization relationship under three frequency ranges between every two channel, specifically:
To the signal x (t) of continuous time channel x, an analytic signal Z is defined firstx(t):
Wherein i is the imaginary part of analytic signal；
For the Hilbert transform of x (t), it is specifically shown in formula (3):
Wherein P is Cauchy's principal value, and t is time variable, and τ is time window；
Ax(t) and Φx(t) be respectively signal x (t) instantaneous amplitude and instantaneous phase, be specifically shown in formula (4)-(5):
Similarly, the analytic signal Z for obtaining the signal y (t) of channel y is calculated separately according to formula (2), (5)y(t) it and calculates instantaneous
Phase Φy(t).；
Specific PLV calculation formula is as follows:
PLV=| < exp (i { Φx(t)-Φy(t) }) > | formula (6)
Wherein<>indicates temporal mean value, the i of the same formula of i (2)；
The intra/inter- Phase synchronization index of step 5, brain area calculates；
Phase synchronization can react the correlation of information integration between brain different zones, calculate three classes point brain area Synchronization Analysis and refer to
Mark:
I, across brain area PS (Inter-hemispheric phase synchronization, IHPS) index be left and right half brain it
Between 9 channels PLV mean value, reflect the information exchange between the brain area of left and right；
Wherein N indicates the number in across brain area channel pair；
II, a left side half brain PS (Left hemispheric phase synchronization, LHPS) index are 3 electricity of left half brain
The PLV mean value between 3 channels that pole (Fp1, F3, F7) is constituted, calculates reference formula (7), reflects disappearing for left half intracerebral portion
Cease interaction mechanism；
III, the right side half brain PS (Right Hemispheric phase synchronization, RHPS) index are right 3, half brain
The PLV mean value between 3 channels that electrode (Fp2, F4, F8) is constituted, calculates reference formula (7), reflects right half intracerebral portion
Information exchange mechanism；
Step 6, the identification of brain comatose state；
In T time, IHPS under tri- low-frequency ranges of delta, theta and alpha is continuously recorded, LHPS and RHPS index, wherein T
For artificial setting time；If IHPS is all larger than the phenomenon that LHPS and RHPS, then it represents that patient is in depth comatose state, Ying Ji
Continuous treatment；Otherwise mix is shown as, then it represents that patient is likely to be at excessive comatose state, should take subsequent judgement measure.
2. a kind of brain comatose state recognition methods based on brain area information exchange as described in claim 1, it is characterised in that step
Rapid 2 pretreatment includes removal artefact, baseline correction, bandpass filtering etc..
3. a kind of brain comatose state recognition methods based on brain area information exchange as described in claim 1, it is characterised in that step
Rapid 59 channels across brain area PS be Fp1-Fp2, Fp1-F4, Fp1-F8, Fp2-F3, Fp2-F7, F3-F4, F3-F8, F4-F7 and
F7-F8。
4. a kind of brain comatose state recognition methods based on brain area information exchange as described in claim 1, it is characterised in that step
3 channels of rapid 5 left side, half brain PS are Fp1-F3, Fp1-F7 and F3-F7.
5. a kind of brain comatose state recognition methods based on brain area information exchange as described in claim 1, it is characterised in that step
3 channels of rapid 5 right side, half brain PS are Fp2-F4, Fp2-F8 and F4-F8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910203614.2A CN109893125A (en) | 2019-03-18 | 2019-03-18 | A kind of brain comatose state recognition methods based on brain area information exchange |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910203614.2A CN109893125A (en) | 2019-03-18 | 2019-03-18 | A kind of brain comatose state recognition methods based on brain area information exchange |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109893125A true CN109893125A (en) | 2019-06-18 |
Family
ID=66953189
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910203614.2A Pending CN109893125A (en) | 2019-03-18 | 2019-03-18 | A kind of brain comatose state recognition methods based on brain area information exchange |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109893125A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112494053A (en) * | 2020-12-23 | 2021-03-16 | 深圳市德力凯医疗设备股份有限公司 | Method, system, equipment and storage medium for monitoring cerebral anoxia risk degree |
| CN114469090A (en) * | 2021-12-31 | 2022-05-13 | 杭州电子科技大学 | Electroencephalogram emotion recognition method based on cross-brain coupling relation calculation and brain-computer system |
| WO2024040423A1 (en) * | 2022-08-23 | 2024-02-29 | 北京太阳电子科技有限公司 | Electroencephalogram-based prognosis evaluation method for acute consciousness disorder, device, and medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102178514A (en) * | 2011-05-09 | 2011-09-14 | 浙江大学 | Coma degree evaluating method based on multiple indexes of non-linearity and complexity |
| CN103845052A (en) * | 2014-02-20 | 2014-06-11 | 清华大学 | Human body faint early warning method based on acquired electroencephalogram signals |
| CN107468243A (en) * | 2017-09-01 | 2017-12-15 | 北京大智商医疗器械有限公司 | A kind of method and device for assessing stupor degree |
-
2019
- 2019-03-18 CN CN201910203614.2A patent/CN109893125A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102178514A (en) * | 2011-05-09 | 2011-09-14 | 浙江大学 | Coma degree evaluating method based on multiple indexes of non-linearity and complexity |
| CN103845052A (en) * | 2014-02-20 | 2014-06-11 | 清华大学 | Human body faint early warning method based on acquired electroencephalogram signals |
| CN107468243A (en) * | 2017-09-01 | 2017-12-15 | 北京大智商医疗器械有限公司 | A kind of method and device for assessing stupor degree |
Non-Patent Citations (6)
| Title |
|---|
| NA ZHANG, JIANHAI ZHANG, JIANTING CAO, YAO MIAO, LI ZHU: "Analysis of Deep Coma and Brain Death Based on", 《INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING》 * |
| ZHANG, JIANHAI；ZHANG, NA；TANG, JIAJIA；CAO, JIANTING；: "A new method for brain death diagnosis based on phase synchronization analysis with EEG", 《IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE-BIBM》 * |
| 张建保，卢虹冰，徐进等编: "《中国生物医学工程进展 下》", 30 April 2007, 西安交通大学出版社 * |
| 徐飞鹏.: "融合相位同步与CSP算法的运动想象分类研究", 《万方》 * |
| 李雪: "基于固有时间尺度分解的数字调制识别", 《万方》 * |
| 贡平: "运动想象脑电信号特征提取研究", 《万方》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112494053A (en) * | 2020-12-23 | 2021-03-16 | 深圳市德力凯医疗设备股份有限公司 | Method, system, equipment and storage medium for monitoring cerebral anoxia risk degree |
| CN112494053B (en) * | 2020-12-23 | 2023-10-03 | 深圳市德力凯医疗设备股份有限公司 | Method, system, equipment and storage medium for monitoring hypoxia risk degree of brain |
| CN114469090A (en) * | 2021-12-31 | 2022-05-13 | 杭州电子科技大学 | Electroencephalogram emotion recognition method based on cross-brain coupling relation calculation and brain-computer system |
| WO2024040423A1 (en) * | 2022-08-23 | 2024-02-29 | 北京太阳电子科技有限公司 | Electroencephalogram-based prognosis evaluation method for acute consciousness disorder, device, and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Cho et al. | EEG-based prediction of epileptic seizures using phase synchronization elicited from noise-assisted multivariate empirical mode decomposition | |
| Liu et al. | Exploring the time–frequency content of high frequency oscillations for automated identification of seizure onset zone in epilepsy | |
| US8700141B2 (en) | Method and apparatus for automatic evoked potentials assessment | |
| Da Silva et al. | Automatic detection and localization of epileptic foci | |
| US9072482B2 (en) | Method and apparatus for automatic seizure monitoring | |
| CN109893125A (en) | A kind of brain comatose state recognition methods based on brain area information exchange | |
| US8600493B2 (en) | Method, apparatus and computer program product for automatic seizure monitoring | |
| CA2936343C (en) | Systems and methods for diagnosing sleep | |
| Busey et al. | Consistency and variability among latent print examiners as revealed by eye tracking methodologies | |
| Kuo et al. | Automatic stage scoring of single-channel sleep EEG based on multiscale permutation entropy | |
| JP2020512860A (en) | How to identify pathological brain activity from scalp EEG | |
| US20210204883A1 (en) | Systems and Methods for Artifact Reduction in Recordings of Neural Activity | |
| Wei et al. | Epileptic seizure detection in clinical EEGs using an XGBoost-based method | |
| Talatov et al. | Algorithmic and software analysis and processing of ECG signals | |
| CN107616794B (en) | System and method for detecting state of cerebral cortex of disturbance of consciousness | |
| CN116186502B (en) | Multi-mode visual nerve function detection method and system thereof | |
| Li et al. | Recognition of general anesthesia-induced loss of consciousness based on the spatial pattern of the brain networks | |
| Cusenza | Fractal analysis of the EEG and clinical applications | |
| CN113197551B (en) | Multimode physiological nerve signal detection and experimental stimulation time alignment method | |
| CN112690806B (en) | Method and system for evaluating cognitive function of brain injury patient after injury | |
| CN114903445A (en) | Intelligent monitoring and early warning system for cardiovascular and cerebrovascular diseases | |
| Chang et al. | Validation of automated arrhythmia detection for Holter ECG | |
| Yang et al. | A preliminary study of brain connectivity in autism using affection-evoked movie clips | |
| Li et al. | EEG-based brain connectivity analysis of states of unawareness | |
| Gotman et al. | Automatic seizure detection in newborns and infants |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190618 |