Seizure Prediction Using Multi-View Features and Improved Convolutional Gated Recurrent Network

Epilepsy is one of the most common neurological diseases worldwide. Early prediction of seizure onsets is of great significance for the safety of intractable epilepsy patients. This work aims to develop a reliable and accurate method for patient-specific seizure prediction based on scalp electroencephalograms (EEGs). Local fractal spectrum, relative band energy, and synchronization modularity features are used to reveal the characteristics of multi-channel EEG in perspectives of time domain, frequency domain, and functional connectivity, respectively. A novel framework, named multi-view convolutional gated recurrent network (Mv-CGRN), is proposed to comprehensively analyze the spatio-temporal sequences of multi-view features and capture the potential variations preceding the impending seizure. Moreover, an attention mechanism is embedded in Mv-CGRN to determine the optimal feature combinations for each patient by adaptively tuning the weight parameters. The proposed system achieves an average sensitivity of 94.50% and an average false positive rate (FPR) of 0.118/h on CHB-MIT scalp EEG dataset, using the leave-one-out cross validation (LOOCV). Our work shows a promising performance compared with the state-of-the-art works in the same filed.