Multi-dimensional enhanced epileptic seizure prediction system based on graph convolutional network

Abstract

The invention provides a multi-dimensional enhanced epileptic seizure prediction system based on a graph convolutional network, the scheme takes a multi-channel spatial relationship as a breakthrough opening, the contribution degree of the multi-channel spatial relationship to epileptic seizure prediction is explored from three dimensions of frequency + space + time. The model comprises three parts, respectively an information reconstruction space, a graph encoder and a space-time predictor. The information reconstruction space and the graph encoder mentioned by the model allow feature enhancement and feature extraction of richer epilepsy electroencephalogram signals, and especially explores the correlation among electroencephalogram channels, so that feature representation is enhanced, and the epilepsy electroencephalogram seizure prediction accuracy is improved; meanwhile, the core structure of the space-time predictor in the scheme adopts a gating circulation unit which is used for exploring the law of epilepsy electroencephalogram signals on the time level, and the purpose of improving the model operation efficiency is achieved by reducing the network parameter scale.

Description

technical field [0001] The disclosure belongs to the technical field of EEG signal processing, and in particular relates to a multi-dimensional enhanced epileptic seizure prediction system based on a graph convolutional network. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Epilepsy is a chronic disease of functional disorder caused by abnormal discharge of brain neurons. The symptoms are sudden and temporary. Moreover, the prevalence is high, and drug therapy and surgical resection are the main forms of treatment. Due to the unpredictability of its onset, it has caused great psychological pressure and serious life troubles to patients. Since epileptic seizures often produce special EEG waveforms such as spikes, sharp waves, spike-slow waves, and sharp-slow waves, in clinical diagnosis, medical workers usually conduct long-term EEG exa...

AI Technical Summary

Problems solved by technology

[0005] In the task of predicting epileptic seizures, the inventors found that many existing methods mainly focus on the signal data of each channel, while ignoring the correlation between channels, and the depth of mining the spatial relationship between multiple channels is insufficient. The dimensionality is shallow, which leads to the problem of low accuracy of existing seizure prediction systems

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