Multi-Dimensional Enhanced Seizure Prediction Framework Based on Graph Convolutional Network

In terms of seizure prediction, how to fully mine relational data information among multiple channels of epileptic EEG? This is a scientific research subject worthy of further exploration. Recently, we propose a multi-dimensional enhanced seizure prediction framework, which mainly includes information reconstruction space, graph state encoder, and space-time predictor. It takes multi-channel spatial relationship as breakthrough point. At the same time, it reconstructs data unit from frequency band level, updates graph coding representation, and explores space-time relationship. Through experiments on CHB-MIT dataset, sensitivity of the model reaches 98.61%, which proves effectiveness of the proposed model.

Automated summary

In the context of seizure prediction, a multi-dimensional enhanced framework was proposed to fully explore the relational data information among multiple channels of epileptic EEG. Through experiments on the CHB-MIT dataset, the model achieved a sensitivity of 98.61%, demonstrating the effectiveness of the proposed approach.