Analysis method and application of electroencephalogram (EEG) signals based on complex network

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

Active Publication Date: 2017-03-08
钧晟(天津)科技发展有限公司
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  • Abstract
  • Description
  • 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

Method used

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  • Analysis method and application of electroencephalogram (EEG) signals based on complex network
  • Analysis method and application of electroencephalogram (EEG) signals based on complex network
  • Analysis method and application of electroencephalogram (EEG) signals based on complex network

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example 1

[0067] The EEG data sets of five epilepsy patients during the seizure period and the EEG data sets of five healthy people in the relaxed state were collected respectively. The EEG signals of each subject were multi-channel EEG signals with 20 electrodes. It adopts 10-20 international standard placement, 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 multi-scale horizontal finite traversing visual complex network of each preprocessed EEG signal, where the finite traversing visual distance L=1, and calculate the average clustering coefficient and aggregation of nodes in the horizontal finite traversing visual complex network 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 achieve accurate classification of EEG data in epilepsy and healthy brai...

example 2

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

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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 electroencephalographic signal analysis method and application. In particular, it relates to a complex network-based EEG signal analysis method and application. Background technique [0002] EEG signal is the general reflection of the electrophysiological activity 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 function and disease information can be obtained, which can provide effective methods for brain function analysis and disease diagnosis based on this information. However, since EEG signals are non-stationary random signals that do not have ergodic nature and their background noise is very strong, the analysis and processing of EEG signals has always been a very attractive but difficult research topic. Epilepsy is a chronic disease in which the sudden abnormal discharge of neu...

Claims

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Application Information

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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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