Preictal Period Optimization for Deep Learning-Based Epileptic Seizure Prediction

TL;DR

This paper introduces a deep learning model and a novel metric to optimize the preictal period for epileptic seizure prediction, achieving high accuracy and personalized predictions on pediatric EEG data.

Contribution

It develops a CNN-Transformer model combined with the CIOPR metric to evaluate and optimize preictal periods for improved seizure prediction performance.

Findings

High prediction accuracy with sensitivity of 99.31% and specificity of 95.34%.

Average prediction time of 76.8 minutes before seizure onset.

The CIOPR metric effectively assesses the impact of preictal period definitions.

Abstract

Accurate prediction of epileptic seizures could prove critical for improving patient safety and quality of life in drug-resistant epilepsy. Although deep learning-based approaches have shown promising seizure prediction performance using scalp electroencephalogram (EEG) signals, substantial limitations still impede their clinical adoption. Furthermore, identifying the optimal preictal period (OPP) for labeling EEG segments remains a challenge. Here, we not only develop a competitive deep learning model for seizure prediction but, more importantly, leverage it to demonstrate a methodology to comprehensively evaluate the predictive performance in the seizure prediction task. For this, we introduce a CNN-Transformer deep learning model to detect preictal spatiotemporal dynamics, alongside a novel Continuous Input-Output Performance Ratio (CIOPR) metric to determine the OPP. We trained and evaluated our model on 19 pediatric patients of the open-access CHB-MIT dataset in a subject-specific manner. Using the OPP of each patient, preictal and interictal segments were correctly identified with an average sensitivity of 99.31%, specificity of 95.34%, AUC of 99.35%, and F1- score of 97.46%, while prediction time averaged 76.8 minutes before onset. Notably, our novel CIOPR metric allowed outlining the impact of different preictal period definitions on prediction time, accuracy, output stability, and transition time between interictal and preictal states in a comprehensive and quantitative way and highlighted the importance of considering both inter- and intra-patient variability in seizure prediction.