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

Hasija, T.; Kuschel, M.; Jackson, M.; Dailey, S.; Menne, H.; Reinsberger, C.; Vieluf, S.; Loddenkemper, T.

doi:10.1101/2025.01.28.635212

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bioRxiv · 10.1101/2025.01.28.635212

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

Hasija, T.·Kuschel, M.·Jackson, M.·Dailey, S.·Menne, H.·Reinsberger, C.·Vieluf, S.·Loddenkemper, T.

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Abstract

AO_SCPLOWBSTRACTC_SCPLOWThe 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.

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Hasija, T., Kuschel, M., Jackson, M., Dailey, S., Menne, H., Reinsberger, C., Vieluf, S., Loddenkemper, T.. 2025-01-29. Improving wearable-based seizure prediction by feature fusion using growing network. https://doi.org/10.1101/2025.01.28.635212

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