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Analysis method and application of EEG signal based on complex network

A technology of EEG signals and complex networks, applied in the fields of application, medical science, diagnosis, etc., can solve the problems of patients' physical, mental and intellectual influences, and achieve high accuracy results

Active Publication Date: 2019-05-21
钧晟(天津)科技发展有限公司
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  • Claims
  • Application Information

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Problems solved by technology

As a chronic disease, epilepsy does not have much impact on patients in the short term, but long-term frequent seizures can seriously affect the patient's body, mind and intelligence.

Method used

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  • Analysis method and application of EEG signal based on complex network
  • Analysis method and application of EEG signal based on complex network
  • Analysis method and application of EEG signal based on complex network

Examples

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example 1

[0067] The EEG data sets of five epileptic patients during the seizure period and the EEG data sets of five healthy people in a relaxed state were collected respectively. The EEG signals of each subject were multi-channel EEG signals with 20 electrodes. The 10-20 international standard is adopted, the sampling frequency is 173.61Hz, and the sampling time is 23.6 seconds. After preprocessing the collected raw EEG data, the denoised EEG data can be obtained. Construct the complex network of multi-scale horizontal finite traversal visual image for each preprocessed EEG signal separately, where the finite traversal line-of-sight distance L=1, and calculate the average aggregation coefficient and clustering of nodes in the complex network of horizontal finite traversal visual image at different scales Coefficient entropy value, based on leave-one-out cross-validation and ten-fold cross-validation, the method of the present invention can realize accurate classification of EEG data u...

example 2

[0069] Collect the EEG signals in the deep sleep stage and the EEG signals in the rapid eye movement stage of 25 adults with sleep disorders respectively. For the collected data, construct a multi-scale level limited traversal visual complex network of each EEG signal , wherein the finite traversal visual distance L=1, calculate the average aggregation coefficient and the aggregation coefficient entropy value of the nodes in the horizontal finite traversal visual complex network at different scales, and based on the leave-one-out cross-validation, the method of the present invention can realize different sleep stages The accurate classification of EEG data in the brain state can reach an accuracy rate of 97%. Based on the ten-fold cross-validation, the method of the present invention can also realize the accurate classification of the EEG data in brain states of different sleep stages, and the accuracy rate can reach 97.33%. Therefore, the method of the present invention can ef...

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Abstract

The invention discloses an analysis method and application of EEG signals based on a complex network. The analysis method of the EEG signals based on the complex network comprises the following steps: constructing multi-scale level limited penetrable visibility graph complex networks; calculating characteristic indexes of each multi-scale level limited penetrable visibility graph complex network; combining a support vector machine to classify the EEG signals, namely using a leave-one-out cross-validation and support vector machine classifier to classify all two-dimensional index vectors, and using a ten-fold cross-validation and support vector machine classifier to classify all the two-dimensional index vectors. According to the invention, multi-scale ideas and level limited penetrable visibility graph theories are combined to construct an EEG multi-scale level limited penetrable visibility graph complex network so as to extract complex network indexes, and the support vector machine classifier in machine learning is combined to realize high-accuracy classification for different EEG signals. The analysis method and application of the EEG signals based on the complex network can be applied to smart head-mounted wearable equipment, and sleep EEG signals are measured through analyzing the smart wearable equipment to monitor the brain state of a user, furthermore, necessary early warning can be provided.

Description

technical field [0001] The invention relates to an analysis method and application of electroencephalogram signals. In particular, it relates to a complex network-based EEG signal analysis method and application. Background technique [0002] EEG signals are the overall reflection of the electrophysiological activities of brain nerve cells on the surface of the cerebral cortex or scalp. EEG signals contain a large amount of physiological and disease information. By analyzing EEG signals, a large amount of functional and disease information can be obtained, which can provide effective methods for brain function analysis and disease diagnosis based on these information. But because the EEG signal is a non-stationary random signal without ergodicity, and its background noise is very strong, the analysis and processing of the EEG signal has always been a very attractive but quite difficult research topic. Epilepsy is a chronic disease in which the sudden abnormal discharge of ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/0476
CPCA61B5/7264A61B5/369
Inventor 高忠科蔡清杨宇轩党伟东
Owner 钧晟(天津)科技发展有限公司
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