EEG epileptic attack detection method based on deep 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: 2018-09-18
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 perfor

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  • EEG epileptic attack detection method based on deep channel attention perception
  • EEG epileptic attack detection method based on deep channel attention perception
  • EEG epileptic attack detection method based on deep 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, which belongs to the fields of biomedical engineering and machine learning, discloses an EEG epileptic attack detection method based on deep channel attention perception. According to the invention, an attention mechanism is introduced into multi-channel EEG epileptic attack detection to train an end-to-end deep channel attention perception model. With the model, the deep features of a brain wave signal can be extracted; and contribution scores of all channels to epileptic detection can be learned to select a most relevant EEG channel dynamically. Compared with the prior art, onthe basis of combination of deep feature extraction and the attention mechanism, the most relevant EEG channel is selected dynamically and the epileptic features are expressed synergistically, so that the fusion features have the channel perception capability; and the epileptic detection rate is increased and the interpretability is enhanced.

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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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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