Method for extracting electroencephalogram characteristic based on quantitative electroencephalogram

A feature extraction, EEG technology, used in diagnostic recording/measurement, medical science, instruments, etc.

Inactive Publication Date: 2014-04-16
TIANJIN PEOPLE HOSPITAL
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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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  • Method for extracting electroencephalogram characteristic based on quantitative electroencephalogram
  • Method for extracting electroencephalogram characteristic based on quantitative electroencephalogram
  • Method for extracting electroencephalogram characteristic based on quantitative electroencephalogram

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Embodiment Construction

[0065] The present invention will be further described in detail below in combination with specific embodiments.

[0066] The present invention is based on the electroencephalogram feature extraction method of quantitative electroencephalogram, and adopts multi-lead electroencephalograph to collect electroencephalogram signal in real time. The sampling rate is 250Hz, the filter passband is 0.5Hz~70Hz, and the electrode impedance is less than 10KΩ; the number of leads is 16, and the electrodes are arranged according to the international standard lead 10-20 electrode system, such as Figure 2-1 and Figure 2-1 As shown, the electrode Cz is used as the reference electrode, the forehead is used as the reference ground, and the electrodes are connected for real-time acquisition of EEG signals in a resting state.

[0067] Follow the steps below, such as figure 1 Shown:

[0068] Step 1. Use the visual interface program of the PC and the electroencephalograph to realize the synchro...

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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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/048G06K9/62A61B5/374
Inventor 杜金刚王勇军明东王春方王静孙长城
Owner TIANJIN PEOPLE HOSPITAL
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