Improving wearable-based seizure prediction by feature fusion using an explainable growing network.

Abstract

The unpredictability of seizures is highly burdensome for people with epilepsy and their caregivers, with significant impacts on their health, quality of life, and cognitive, social, and emotional well-being. Non-stigmatizing and user-friendly wearable devices may provide information to predict seizures based on physiological data. We propose a patient-agnostic seizure prediction method that identifies group-level patterns across data from multiple patients. We employ a supervised long-short-term network (LSTM) and add an unsupervised deep canonically correlated autoencoder (DCCAE) and 24-hour patterns using time-of-day information. We fuse features from these three techniques using a growing neural network, allowing incremental learning. Our method incorporates all three feature sets and improves prediction accuracy over the baseline LSTM by 7.3%, from 74.4% to 81.7%, when averaged across all patients, and outperforms the LSTM in 84% of patients. Compared to the all-at-once fusion, the growing network improves the accuracy by 9.5%. We report the contributions from different feature sets using Shapley additive explanations (SHAP). We also analyze the impact of preictal data duration, wearable data quality, and clinical variables on the prediction performance. An effective seizure prediction method using wearable devices has the potential to save lives and significantly improve the quality of life for people with epilepsy.