Method for extracting electroencephalogram characteristic based on quantitative electroencephalogram

Abstract

The invention discloses a method for extracting electroencephalogram (EEG) characteristic based on a quantitative EEG. The method comprises the following steps of synchronous acquiring 16-lead EEG signal potential data by using a personal computer (PC) and an electroencephalograph visual interface program and displaying EEG waveform acquired in real time; preprocessing the acquired EEG signals to remove power frequency interference, noise signals, electro-oculogram interference and myoelectric interference; respectively extracting power coupling coefficient based on absolute power and relative power of 5 frequency bands of the 16-lead EEG signals and asymmetry coefficient based on the density of high and low frequency band power spectrum; and classifying the EEG signals in the quiescent state by using a support vector machine (SVM) fusion network with a double-layer structure. By the method, the EEG signals of PSD patients and normal people can be distinguished, high classification accurate rate is achieved, the depression degree of the PSD patients can be effectively recognized, basis is provided for research on the objective diagnosis standard of PSD, and important social significance is achieved.

Description

technical field [0001] The invention relates to an electroencephalogram feature extraction method, in particular to an electroencephalogram feature extraction method based on quantitative electroencephalogram (QEEG). Background technique [0002] Stroke, also known as cerebral apoplexy, is the third cause of death after coronary heart disease and cancer worldwide, accounting for 12% of all deaths. In my country, stroke is currently the disease with the highest disability rate and the second fatality rate. With the obvious increase in the incidence of stroke, the resulting mental problems are also increasing. As one of the complications of stroke, PSD seriously threatens people's physical and mental health, and brings great economic and mental burdens to society and families. Therefore, it has attracted more and more attention from researchers. Currently, there is no uniform standard for the diagnosis of PSD. Scholars at home and abroad have basically adopted various diagno...

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Problems solved by technology

[0005] Support Vector Machine SVM (Support Vector Machine) is a new tool developed on the basis of statistical learning theory and using optimization methods to solve machine learning problems

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