Exploring the Interpretability of EEG-Inception Convolutional Neural Networks for Epilepsy Prediction

Abstract

Predicting epileptic seizures effectively allows patients to take preventive measures in advance, reducing accident risk and enhancing safety. Several modeling challenges remain open: (1) The complex spatiotemporal dependency of EEG signals makes it challenging to design a model that efficiently extracts spatial and temporal features from multi-channel EEG signals to classify epileptic EEG signals. (2)While many studies have utilized machine learning and deep learning for seizure prediction, they often lack research on model interpretability. To tackle the challenges above, this paper proposes a novel approach—an epilepsy prediction framework that combines EEG-Inception Convolutional Neural Networks (EICNN) with Feature Pattern Interpretability Post-processing (FPIP). It yielded substantial performance improvements and achieved an average sensitivity of 94.6%, a false prediction rate of 0.29/h in the study involving 22 pediatric patients from the CHB-MIT database using the leave-one-out method. The proposed FPIP provides visual interpretations and significantly enhances performance in predicting epileptic seizures.