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

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...

AI Technical Summary

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.

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