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

The unpredictability of seizures is one of the most compromising features reported by people with epilepsy. Non-stigmatizing and easy-to-use 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 supervised long-short-term networks (LSTMs) and add unsupervised deep canonically correlated autoencoders (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 with all three features improves prediction accuracy over the baseline LSTM by 7.3%, from 74.4% to 81.7%, 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 analyze the impact of preictal data duration, wearable data quality, and clinical variables on the prediction performance.