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An EEG seizure detection method based on depth channel attention perception

A technology for epileptic seizures and detection methods, applied in the fields of biomedical engineering and machine learning, can solve problems such as difficulty in ensuring the stability of epilepsy detection performance, and achieve high accuracy and recall rates

Active Publication Date: 2022-04-15
BEIJING UNIV OF TECH
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Problems solved by technology

This multi-stage model is difficult to guarantee the stability of epilepsy detection performance, because it needs to use manual means to coordinate the work of modules in each stage

Method used

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  • An EEG seizure detection method based on depth channel attention perception
  • An EEG seizure detection method based on depth channel attention perception
  • An EEG seizure detection method based on depth channel attention perception

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Embodiment Construction

[0027] The present invention is described in detail below in conjunction with accompanying drawing and specific embodiment:

[0028] figure 1 It is a schematic flow chart of an EEG seizure detection method based on depth channel attention perception, including the following steps:

[0029] 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.

[0030] Step 2. Preprocessing the training data. Use the short-time Fourier transform to express the time-frequency information of the biomedical signals in the training set, and divide them into blocks according to the time direction to generate a multi-channel EEG time-frequency matrix training set {(S (i) ,Y (i) ), i=1,2,...,m}. Among them, for the biomedical signal sample x(t), the formula for expressing the EEG time-frequency information s using the short-time Fourier tr...

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Abstract

The invention discloses an EEG seizure detection method based on depth channel attention perception, which belongs to the fields of biomedical engineering and machine learning. The present invention introduces an attention mechanism into multi-channel EEG epileptic seizure detection, and trains an end-to-end deep channel attention perception model. The model can not only extract the deep features of the EEG signal, but also learn the contribution scores of each channel to the detection of epilepsy at the same time, so as to realize the dynamic selection of the most relevant EEG channel. Compared with the prior art, the present invention dynamically selects the most relevant EEG channel and expresses epilepsy features cooperatively by combining deep feature extraction and attention mechanism, so that its fusion features have channel-aware ability, and can improve the detection rate of epilepsy while improving the epilepsy detection rate. explanatory.

Description

technical field [0001] The invention relates to the fields of biomedical engineering and machine learning, in particular to an EEG seizure detection method based on deep channel attention perception. Background technique [0002] Epilepsy is a chronic neurological disease caused by abnormal discharge of brain neurons. There are about 6 million epilepsy patients in my country and the number is increasing rapidly year by year. The clinical features of epilepsy usually manifest as convulsions, mental abnormalities, paroxysmal changes in consciousness, etc., which are extremely harmful to the physical and mental health of patients. With the increasing development and popularization of medical information construction, epilepsy diagnosis can be made by medical experts directly based on multi-channel electroencephalogram (electroencephalogram, EEG) through visual detection. But because of the uncertainty of seizures, doctors need to monitor patients' lengthy EEG recordings for a ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00
CPCG06F2218/08G06F2218/12
Inventor 贾克斌袁野孙中华
Owner BEIJING UNIV OF TECH
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