Development of a Multivariable Seizure Likelihood Assessment Based on Clinical Information and Short Autonomic Activity Recordings for Children With Epilepsy

• Published in: Pediatric neurology 2023-11, Vol.148, p.118-127
• Main Authors: Vieluf, Solveig, Cantley, Sarah, Jackson, Michele, Zhang, Bo, Bosl, William J., Loddenkemper, Tobias
• Format: Article
• Language: English
• Subjects: Clinical data; Pediatric patients; Seizure forecasting; Supervised learning; Wearables
• ISSN: 0887-8994; 1873-5150
• DOI: 10.1016/j.pediatrneurol.2023.07.018

Predicting seizure likelihood for the following day would enable clinicians to extend or potentially schedule video-electroencephalography (EEG) monitoring when seizure risk is high. Combining standardized clinical data with short-term recordings of wearables to predict seizure likelihood could have high practical relevance as wearable data is easy and fast to collect. As a first step toward seizure forecasting, we classified patients based on whether they had seizures or not during the following recording. Pediatric patients admitted to the epilepsy monitoring unit wore a wearable that recorded the heart rate (HR), heart rate variability (HRV), electrodermal activity (EDA), and peripheral body temperature. We utilized short recordings from 9:00 to 9:15 pm and compared mean values between patients with and without impending seizures. In addition, we collected clinical data: age, sex, age at first seizure, generalized slowing, focal slowing, and spikes on EEG, magnetic resonance imaging findings, and antiseizure medication reduction. We used conventional machine learning techniques with cross-validation to classify patients with and without impending seizures. We included 139 patients: 78 had no seizures and 61 had at least one seizure after 9 pm during the concurrent video-EEG and E4 recordings. HR (P