Leveraging Channel Coherence in Long-Term iEEG Data for Seizure Prediction.

Abstract

Epilepsy affects millions worldwide, posing significant challenges due to the erratic and unexpected nature of seizures. Despite advancements, existing seizure prediction techniques remain limited in their ability to forecast seizures with high accuracy, impacting the quality of life for those with epilepsy. This research introduces the Coherence-based Seizure Prediction (CoSP) method, which integrates coherence analysis with deep learning to enhance seizure prediction efficacy. In CoSP, electroencephalography (EEG) recordings are divided into 10-second segments to extract channel pairwise coherence. This coherence data is then used to train a four-layer convolutional neural network to predict the probability of being in a preictal state. The predicted probabilities are then processed to issue seizure warnings. CoSP was evaluated in a pseudo-prospective setting using long-term iEEG data from ten patients in the NeuroVista seizure advisory system. CoSP demonstrated promising predictive performance across a range of preictal intervals (4 to 180 minutes). CoSP achieved a median Seizure Sensitivity (SS) of 0.79, a median false alarm rate of 0.15 per hour, and a median Time in Warning (TiW) of 27%, highlighting its potential for accurate and reliable seizure prediction. Statistical analysis confirmed that CoSP significantly outperformed chance (p = 0.001) and other baseline methods (p <0.05) under similar evaluation configurations.