Seizure prediction method based on hypergraph convolution and shap feature feedback optimization

Abstract

The application discloses a seizure prediction method based on hypergraph convolution and SHAP feature feedback optimization, comprising: collecting and preprocessing multi-channel electroencephalogram and electrocardiogram combined signals, and extracting time-frequency domain features thereof; constructing a channel-level hypergraph fusing brain region priori and electrocardiogram information, obtaining a node feature matrix and an initialized hyperedge matrix; inputting the node feature and the initialized matrix into a hypergraph convolution neural network model, the hypergraph convolution neural network model aggregates information of each time window into a low-dimensional embedding vector; adopting a SHAP explainability analysis method to screen out TOP-K key markers with the largest contribution to prediction; fusing original waveform features and screened key marker features, constructing a double-branch prediction model, and outputting a seizure prediction result; the application introduces an explainable feedback optimization mechanism, significantly improves the accuracy and explainability of seizure prediction, and provides a reference basis for clinical auxiliary diagnosis and treatment decision.

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