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Automatic electroencephalogram epilepsy recognition method based on multi-view depth feature fusion

A deep feature, automatic recognition technology, applied in the fields of biomedical engineering and machine learning

Inactive Publication Date: 2018-08-21
BEIJING UNIV OF TECH
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Problems solved by technology

[0004] The purpose of the present invention is to solve the shortcomings of the existing deep learning algorithm in the automatic recognition of EEG epilepsy, and propose a method for automatic recognition of EEG epilepsy based on multi-view depth feature fusion, that is, to express the EEG time-frequency spectrum by introducing a signal processing method , combined with deep learning technology to extract the context features of multi-channel EEG signals at different levels, so that the fusion features are robust while improving the performance of the algorithm

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  • Automatic electroencephalogram epilepsy recognition method based on multi-view depth feature fusion
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  • Automatic electroencephalogram epilepsy recognition method based on multi-view depth feature fusion

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Embodiment Construction

[0037] The present invention is described in detail below in conjunction with accompanying drawing and specific embodiment:

[0038] figure 1 It is a schematic flow chart of the automatic recognition method of EEG epilepsy based on multi-view depth feature fusion, including the following steps:

[0039] Step 1. Collect multi-channel EEG data X, and mark the collected data with epilepsy Y, and use these marked data as the training data set {(X (i) ,Y (i) ), i=1,2,...,m}, where m is the number of training samples.

[0040] Step 2. Use the short-time Fourier transform to express the time-frequency information of the multi-channel EEG signals in the training set, and divide them into blocks according to the time direction to obtain the multi-channel EEG time-frequency matrix training set {(S (i) ,Y (i) ), i=1,2,...,m}. Among them, for the EEG signal x(t) of one channel, the short-time Fourier transform is used to express the EEG time-frequency information s x The formula is ...

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Abstract

The invention discloses an automatic electroencephalogram epilepsy recognition method based on multi-view depth feature fusion, and belongs to the field of biomedical engineering and machine learning.According to the automatic electroencephalogram epilepsy recognition method disclosed by the invention, through expressing an electroencephalogram time-frequency spectrum and using deep learning to train a multi-view depth feature fusion model, the fusion feature thereof has robustness while improving algorithm performance. Compared with the prior art, the automatic electroencephalogram epilepsyrecognition method disclosed by the invention effectively combines signal processing and deep learning techniques to extract different angle information of electroencephalogram data in time and channels, thereby jointly expressing epileptic characteristics and improving automatic recognition accuracy.

Description

technical field [0001] The invention relates to the fields of biomedical engineering and machine learning, in particular to an automatic recognition method of EEG epilepsy based on multi-view depth feature fusion. Background technique [0002] Epilepsy is a common chronic neurological disease that is extremely harmful to the health of patients. Among them, there are about 6 million epilepsy patients in my country, and the rapid growth rate is 650,000 to 700,000 per year. Epilepsy is a brain disease caused by abnormal discharge of brain neurons. Its main symptoms are convulsions, mental abnormalities, and paroxysmal changes in consciousness. At present, the detection of epilepsy is mainly diagnosed by medical experts through visual inspection based on recorded multi-channel electroencephalogram (electroencephalogram, EEG). Because of the uncertainty of seizures, doctors often require long-term monitoring of patients' EEGs. However, problems such as long manual detection tim...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0476G06K9/00G06K9/62
CPCA61B5/4094A61B5/7257A61B5/7267A61B5/369G06F2218/08G06F2218/12G06F18/2411G06F18/253
Inventor 贾克斌袁野刘鹏宇
Owner BEIJING UNIV OF TECH
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