CN113558640A - Minimum consciousness state degree evaluation method based on electroencephalogram characteristics - Google Patents
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Abstract
The invention belongs to the technical field of medical diagnosis and evaluation, and particularly relates to a minimum consciousness state degree evaluation method based on electroencephalogram characteristics. The method comprises the following steps: s1, acquiring electroencephalogram signals; s2, preprocessing the electroencephalogram signals; s3, extracting electroencephalogram signal features; s4, analyzing the characteristics of the electroencephalogram signals; and S5, visualizing the evaluation result. The invention has the characteristics of high detection and evaluation accuracy, capability of carrying out multi-feature analysis and contribution to the state evaluation of patients with different brain area damage conditions.
Description
Technical Field
The invention belongs to the technical field of medical diagnosis and evaluation, and particularly relates to a minimum consciousness state degree evaluation method based on electroencephalogram characteristics.
Background
Relevant studies suggest that the Minimal Consciousness State (MCS) with complex and various disease conditions can be further subdivided according to a behavioral scale, and specifically the MCS + and MCS-two states. The assessment of the behavioural scale needs to be carried out by a professional trained medical practitioner, and a certain misdiagnosis rate exists. Therefore, the development of auxiliary diagnosis and treatment means, such as neuroelectrophysiological technology (electroencephalogram EEG), neuroimaging technology (functional magnetic resonance imaging fMRI, positron emission tomography PET), etc., has been widely applied in the assessment of the cognitive impairment. Although the neural imaging technology has higher resolution in a spatial level, the problems of high cost, inconvenience and the like exist, so that the auxiliary diagnosis and treatment technology which has lower cost and is beneficial to continuous tracking has great application significance to the assessment of the consciousness state of a patient. EEG is used as a sensitive index for reflecting cerebral cortex function change, and by virtue of objectivity, non-invasiveness, continuous tracking and higher time resolution, consciousness state evaluation of low-cost and continuous bedside tracking detection can be realized. Meanwhile, the electroencephalogram signal is used as a high-dimensional chaotic signal, and the nonlinear dynamics characteristics of the chaotic signal can reflect whether neurons on the surface of the brain scalp are excited or inhibited, so that the functional change of the brain area is further reflected.
At present, aiming at the treatment of electroencephalogram characteristics of a patient with disturbance of consciousness, single nonlinear kinetic parameters such as approximate entropy (ApEn), sample entropy and permutation entropy are mostly selected for evaluation, and most of evaluation results are overall evaluation in whole brain areas, and no further subdivision analysis is carried out on different brain areas. Such detection and evaluation accuracy is low, a single characteristic parameter has certain limitation, and the state evaluation of patients with different impaired conditions in the face of brain areas is not facilitated, and great help cannot be provided for clinical auxiliary diagnosis and treatment.
Therefore, it is necessary to design an evaluation method of the minimum consciousness state degree based on the electroencephalogram characteristics, which has high detection and evaluation accuracy, can perform multi-feature analysis, and is beneficial to evaluating the states of patients with different brain areas damaged.
For example, the method for diagnosing disturbance of consciousness based on electroencephalogram signals, which is disclosed in application No. CN201910150296.8, specifically includes the following steps: s1, acquiring electroencephalogram signals: firstly, medical staff can install the electroencephalogram signal acquisition unit at each position of the head of a diagnostician, then the electroencephalogram signal acquisition unit is controlled by the central processing module to acquire the electroencephalogram signal of the head of the diagnostician, and S2 is used for denoising and filtering the electroencephalogram signal. Although the accuracy and the analysis processing speed of detection and evaluation are greatly improved, the filtering and denoising processing of the detected brain waves is realized, the interference of ocular artifacts and other signal sources is avoided, the purpose of respectively and simultaneously analyzing and processing the extracted four characteristic values is well achieved, and the automatic generation and automatic printing of a diagnosis and analysis table through an analysis algorithm after the completion of the electroencephalogram examination is realized, so that the diagnosis work of medical personnel is greatly facilitated, the defects are that the brain area division processing process is lacked, the pertinence evaluation of consciousness conditions of different types of patients is not facilitated, and meanwhile, the change of an analysis result cannot be intuitively known due to the lack of the result output of a brain topographic map.
Disclosure of Invention
The invention provides an electroencephalogram characteristic-based minimum consciousness state degree assessment method which is high in detection and assessment accuracy, capable of performing multi-feature analysis and beneficial to assessing states of patients with different brain areas damaged and is based on electroencephalogram characteristics, and aims to solve the problems that in the prior art, the accuracy is low, a single characteristic parameter has certain limitation, state assessment of the patients with different brain area damaged conditions is not facilitated, and great help cannot be provided for clinical auxiliary diagnosis and treatment.
In order to achieve the purpose, the invention adopts the following technical scheme:
the minimum consciousness state degree evaluation method based on the electroencephalogram features comprises the following steps:
s1, acquiring electroencephalogram signals: the medical staff installs the electroencephalogram signal acquisition module at the head of the patient and then controls the electroencephalogram signal acquisition module to acquire the electroencephalogram signal through the signal analysis module;
s2, preprocessing the electroencephalogram signals: the electroencephalogram signal acquisition module is used for transmitting the acquired electroencephalogram signals to the signal analysis module, an electroencephalogram signal preprocessing system in the signal analysis module is used for filtering and denoising the acquired electroencephalogram signals, and then electro-oculogram, myoelectricity and eye drift are removed through an independent component analysis method to obtain the required electroencephalogram signals;
s3, extraction of electroencephalogram signal features: the electroencephalogram signal data after being preprocessed in the step S2 are transmitted to a signal feature extraction system in a signal analysis module, the signal feature extraction system respectively calculates nonlinear parameters of each channel of the electroencephalogram signal acquisition module according to the acquired electroencephalogram signal data, and transmits the result to a signal feature analysis system in the signal analysis module;
s4, analyzing the characteristics of the electroencephalogram signals: the signal characteristic analysis system of the signal analysis module integrates and analyzes the electroencephalogram signal data obtained in the step S3, and performs targeted analysis on data of different brain areas by using an optimization algorithm according to the distribution condition of the electroencephalogram signal acquisition module;
s5, visualization of evaluation results: and obtaining a nonlinear parameter integration analysis result from the signal characteristic analysis system, displaying the result by an evaluation display module in combination with a clinical evaluation result of a patient, and further visually outputting the result by utilizing a brain topographic map.
Preferably, the non-linear parameters include approximate entropy, sample entropy and permutation entropy.
Preferably, the electroencephalogram acquisition module is in bidirectional connection with the signal analysis module, and the signal analysis module is in bidirectional connection with the evaluation display module.
Preferably, the signal analysis module comprises an electroencephalogram signal preprocessing system, a signal feature extraction system and a signal feature analysis system; the electroencephalogram signal preprocessing system is in bidirectional connection with the signal feature extraction system, and the signal feature extraction system is in bidirectional connection with the signal feature analysis system.
Preferably, the optimization algorithm in step S4 includes the following steps:
s41, obtaining an entropy result obtained by calculation of the signal feature extraction system;
s42, calculating the entropy result of the whole brain area according to an average algorithm;
s43, calculating the entropy result of each brain area according to the brain area position of the EEG signal acquisition module channel and the weight ratio occupied by each channel;
s44, the result of brain area analysis of the patient case is output.
Preferably, the brain region includes the frontal, temporal and parietal lobes.
Preferably, in step S5, the evaluation display module displays the results including different results for the patient' S whole brain region and the specific brain region.
Compared with the prior art, the invention has the beneficial effects that: (1) the method adopts multi-feature joint analysis, which is beneficial to avoiding the limitation of single feature analysis; (2) the method adopts the targeted analysis of the brain area data, which is beneficial to the targeted evaluation of consciousness conditions of different types of patients; (3) the invention adopts the result output of the brain-map, which is favorable for intuitively understanding the change of the analysis result.
Drawings
FIG. 1 is a flow chart of a method for evaluating a minimum state of consciousness based on electroencephalogram characteristics according to the present invention;
FIG. 2 is a schematic block diagram of a system structure of the method for evaluating the minimum consciousness state degree based on electroencephalogram characteristics according to the present invention;
FIG. 3 is a schematic block diagram of a signal analysis module according to the present invention;
FIG. 4 is a flow chart of an optimization algorithm of the present invention;
FIG. 5 is a diagram of EEG topographic results of approximate entropies ApEn, sample entropies SampEn and permutation entropies PerEn of MCS + patients and MCS-patients in a resting state output by the evaluation method of minimum consciousness state degree based on EEG features.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention, the following description will explain the embodiments of the present invention with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
Example 1:
the method for evaluating the minimum consciousness state degree based on the electroencephalogram characteristics, as shown in fig. 1, comprises the following steps:
s1, acquiring electroencephalogram signals: the medical staff installs the electroencephalogram signal acquisition module at the head of the patient and then controls the electroencephalogram signal acquisition module to acquire the electroencephalogram signal through the signal analysis module;
s2, preprocessing the electroencephalogram signals: the electroencephalogram signal acquisition module is used for transmitting the acquired electroencephalogram signals to the signal analysis module, an electroencephalogram signal preprocessing system in the signal analysis module is used for filtering and denoising the acquired electroencephalogram signals, and then electro-oculogram, myoelectricity and eye drift are removed through an independent component analysis method to obtain the required electroencephalogram signals;
s3, extraction of electroencephalogram signal features: the electroencephalogram signal data after being preprocessed in the step S2 are transmitted to a signal feature extraction system in a signal analysis module, the signal feature extraction system respectively calculates nonlinear parameters of each channel of the electroencephalogram signal acquisition module according to the acquired electroencephalogram signal data, and transmits the result to a signal feature analysis system in the signal analysis module;
s4, analyzing the characteristics of the electroencephalogram signals: the signal characteristic analysis system of the signal analysis module integrates and analyzes the electroencephalogram signal data obtained in the step S3, and performs targeted analysis on data of different brain areas by using an optimization algorithm according to the distribution condition of the electroencephalogram signal acquisition module;
s5, visualization of evaluation results: and obtaining a nonlinear parameter integration analysis result from the signal characteristic analysis system, displaying the result by an evaluation display module in combination with a clinical evaluation result of a patient, and further visually outputting the result by utilizing a brain topographic map.
Wherein the non-linear parameters in step S3 include approximate entropy, sample entropy, and permutation entropy.
The approximate entropy calculation mode is as follows:
1. determining a time series { X (i) }, i ═ 1,2, …, n }
2. Constructing a window with the length of m, dividing a time sequence into k-n + m-1 subsequences, and constructing a corresponding vector space:
Xm(i)＝[x(i),x(i+1),…,x(i+m-1)],i＝1,…,n-m+1
3. in vector space, each vector X is definedm(i) To vectors X other than themselvesm(j) A distance of
4. Let the similar tolerance distance be r, the calculation satisfies
Vector C ofm(i) And calculating the probability distribution thereof
5. Calculating the logarithmic mean value psim(r) increasing the window length to m +1, repeating the above steps, recalculating Ψm+1(r)
6. Calculating the value of the approximate entropy ApEn:
ApEn(m,r,N)＝Ψm(r)-Ψm+1(r)
the sample entropy calculation mode is as follows:
1. obtaining the value Ψ using the same steps as ApEnm(r)
2. Increase window length to m +1, repeat the above steps (steps 1 to 4 of the approximate entropy algorithm), recalculate Ψm+1(r)
3. Calculating the value of sample entropy SampEn:
SampEn(r,m,N)＝ln(Ψm+1(r)/Ψm(r))
the permutation entropy calculation mode is as follows:
1. reconstructing the phase space of a time series x (n) of length n with lag time τ into an m-dimensional vector:
Xm(i)＝[x(i),x(i+τ),…,x(i+(m-1)τ)],i＝1,…,n-(m-1)τ
2. by comparing the values of the components of the phase space, arranging them in ascending order according to their magnitude:
x[i+(j1-1)τ]≤x[i+(j2-1)τ]≤…≤x[i+(jm-1)τ]
3. there is m! A variety of different symbol sequences (j)1,j2,…,jm) In the mapping of the m-dimensional phase space, the probability P of each symbol sequence is calculated1,P2,…,PkThe permutation entropy time series x (i) resulting in k different symbol sequences can be defined as:
further, in the process of step S1, the sampling frequency of the electroencephalogram signal is 256Hz, and the acquisition time lasts for 5 minutes or more.
Further, as shown in fig. 2, the electroencephalogram acquisition module is bidirectionally connected with the signal analysis module, and the signal analysis module is bidirectionally connected with the evaluation display module.
Further, as shown in fig. 3, the signal analysis module includes an electroencephalogram signal preprocessing system, a signal feature extraction system, and a signal feature analysis system; the electroencephalogram signal preprocessing system is in bidirectional connection with the signal feature extraction system, and the signal feature extraction system is in bidirectional connection with the signal feature analysis system.
Further, as shown in fig. 4, the optimization algorithm in step S4 includes the following steps:
s41, obtaining an entropy result obtained by calculation of the signal feature extraction system;
s42, calculating the entropy result of the whole brain area according to an average algorithm;
s43, calculating the entropy result of each brain area according to the brain area position of the EEG signal acquisition module channel and the weight ratio occupied by each channel;
s44, the result of brain area analysis of the patient case is output.
Wherein the brain region comprises a frontal lobe, a temporal lobe, and a parietal lobe; the entropy result includes approximate entropy, sample entropy, and permutation entropy.
Further, in step S5, the evaluation display module displays the results including different results for the whole brain region and the specific brain region of the patient.
Through the steps of the invention, a visualization result display as shown in fig. 5 is finally obtained:
the results of the approximate entropy ApEn (upper graph), sample entropy SampEn (middle graph) and permutation entropy PerEn (lower graph) of MCS + patient and MCS-patient in the resting state are shown in fig. 5. The weight of the color reflects the size of the entropy and the activity level of the brain area, and the darker the color represents the larger the entropy of the area, the higher the activity level of the brain function.
The method adopts multi-feature joint analysis, which is beneficial to avoiding the limitation of single feature analysis; the method adopts the targeted analysis of the brain area data, which is beneficial to the targeted evaluation of consciousness conditions of different types of patients; the invention adopts the result output of the brain-map, which is favorable for intuitively understanding the change of the analysis result.
The foregoing has outlined rather broadly the preferred embodiments and principles of the present invention and it will be appreciated that those skilled in the art may devise variations of the present invention that are within the spirit and scope of the appended claims.
Claims (7)
1. The minimum consciousness state degree evaluation method based on the electroencephalogram features is characterized by comprising the following steps of:
s1, acquiring electroencephalogram signals: the medical staff installs the electroencephalogram signal acquisition module at the head of the patient and then controls the electroencephalogram signal acquisition module to acquire the electroencephalogram signal through the signal analysis module;
s2, preprocessing the electroencephalogram signals: the electroencephalogram signal acquisition module is used for transmitting the acquired electroencephalogram signals to the signal analysis module, an electroencephalogram signal preprocessing system in the signal analysis module is used for filtering and denoising the acquired electroencephalogram signals, and then electro-oculogram, myoelectricity and eye drift are removed through an independent component analysis method to obtain the required electroencephalogram signals;
s3, extraction of electroencephalogram signal features: the electroencephalogram signal data after being preprocessed in the step S2 are transmitted to a signal feature extraction system in a signal analysis module, the signal feature extraction system respectively calculates nonlinear parameters of each channel of the electroencephalogram signal acquisition module according to the acquired electroencephalogram signal data, and transmits the result to a signal feature analysis system in the signal analysis module;
s4, analyzing the characteristics of the electroencephalogram signals: the signal characteristic analysis system of the signal analysis module integrates and analyzes the electroencephalogram signal data obtained in the step S3, and performs targeted analysis on data of different brain areas by using an optimization algorithm according to the distribution condition of the electroencephalogram signal acquisition module;
s5, visualization of evaluation results: and obtaining a nonlinear parameter integration analysis result from the signal characteristic analysis system, displaying the result by an evaluation display module in combination with a clinical evaluation result of a patient, and further visually outputting the result by utilizing a brain topographic map.
2. The method for evaluating the degree of minimum state of consciousness based on electroencephalogram characteristics of claim 1, wherein the non-linear parameters include approximate entropy, sample entropy, and permutation entropy.
3. The method for evaluating the degree of minimum consciousness state based on electroencephalogram characteristics as claimed in claim 1, wherein the electroencephalogram acquisition module is bidirectionally connected with the signal analysis module, and the signal analysis module is bidirectionally connected with the evaluation display module.
4. The method for assessing the degree of minimal state of consciousness based on electroencephalogram characteristics of claim 1, wherein said signal analysis module comprises an electroencephalogram signal preprocessing system, a signal characteristic extraction system, and a signal characteristic analysis system; the electroencephalogram signal preprocessing system is in bidirectional connection with the signal feature extraction system, and the signal feature extraction system is in bidirectional connection with the signal feature analysis system.
5. The method for evaluating the degree of minimum state of consciousness based on electroencephalogram characteristics of claim 2, wherein the optimization algorithm in step S4 comprises the steps of:
s41, obtaining an entropy result obtained by calculation of the signal feature extraction system;
s42, calculating the entropy result of the whole brain area according to an average algorithm;
s43, calculating the entropy result of each brain area according to the brain area position of the EEG signal acquisition module channel and the weight ratio occupied by each channel;
s44, the result of brain area analysis of the patient case is output.
6. The method of evaluating a degree of least state of consciousness based on electroencephalographic features of claim 5, wherein the brain region includes a frontal lobe, a temporal lobe, and a parietal lobe.
7. The method for evaluating the degree of minimum consciousness state based on electroencephalogram characteristics as recited in claim 1, wherein in step S5, said evaluation display module displays the results including the results different between the whole brain area and the specific brain area of the patient.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114259241A (en) * | 2021-12-16 | 2022-04-01 | 杭州电子科技大学 | Cognitive function visualization system based on EEG |
| CN117838063A (en) * | 2024-03-04 | 2024-04-09 | 江西杰联医疗设备有限公司 | Physiological information early warning processing system and electronic equipment under anesthesia scene |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109717866A (en) * | 2019-02-28 | 2019-05-07 | 温州大学 | A kind of disturbance of consciousness diagnostic method based on EEG signals |
| CN110338760A (en) * | 2019-07-01 | 2019-10-18 | 上海交通大学 | A kind of three classification method of schizophrenia based on electroencephalogram frequency domain data |
| CN111110231A (en) * | 2020-01-15 | 2020-05-08 | 燕山大学 | Consciousness assessment method based on multichannel neural signal coupling analysis |
| CN112353407A (en) * | 2020-10-27 | 2021-02-12 | 燕山大学 | Evaluation system and method based on active training of neurological rehabilitation |
| KR20210034458A (en) * | 2019-09-20 | 2021-03-30 | 고려대학교 산학협력단 | Deep neural network based conscious state determination device and method |
-
2021
- 2021-07-13 CN CN202110789777.0A patent/CN113558640A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109717866A (en) * | 2019-02-28 | 2019-05-07 | 温州大学 | A kind of disturbance of consciousness diagnostic method based on EEG signals |
| CN110338760A (en) * | 2019-07-01 | 2019-10-18 | 上海交通大学 | A kind of three classification method of schizophrenia based on electroencephalogram frequency domain data |
| KR20210034458A (en) * | 2019-09-20 | 2021-03-30 | 고려대학교 산학협력단 | Deep neural network based conscious state determination device and method |
| CN111110231A (en) * | 2020-01-15 | 2020-05-08 | 燕山大学 | Consciousness assessment method based on multichannel neural signal coupling analysis |
| CN112353407A (en) * | 2020-10-27 | 2021-02-12 | 燕山大学 | Evaluation system and method based on active training of neurological rehabilitation |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114259241A (en) * | 2021-12-16 | 2022-04-01 | 杭州电子科技大学 | Cognitive function visualization system based on EEG |
| CN114259241B (en) * | 2021-12-16 | 2024-03-29 | 杭州电子科技大学 | EEG-based cognitive function visualization system |
| CN117838063A (en) * | 2024-03-04 | 2024-04-09 | 江西杰联医疗设备有限公司 | Physiological information early warning processing system and electronic equipment under anesthesia scene |
| CN117838063B (en) * | 2024-03-04 | 2024-05-24 | 江西杰联医疗设备有限公司 | Physiological information early warning processing system and electronic equipment under anesthesia scene |
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