Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting. (11th October 2020)

• Record Type: Journal Article
• Authors: Meisel, Christian; El Atrache, Rima; Jackson, Michele; Schubach, Sarah; Ufongene, Claire; Loddenkemper, Tobias
• Is Part Of: Epilepsia. Volume 61:issue 12(2020)
• Page Start: 2653
• Page End: 2666
• Publication Date: 2020-10-11
• Subjects: precision medicine -- seizure forecasting -- wearable devices; Epilepsy -- Periodicals; 616.853
• DOI: 10.1111/epi.16719
• Languages: English
• ISSNs: 0013-9580

Abstract

Abstract: Objective: Seizure forecasting may provide patients with timely warnings to adapt their daily activities and help clinicians deliver more objective, personalized treatments. Although recent work has convincingly demonstrated that seizure risk assessment is in principle possible, these early approaches relied largely on complex, often invasive setups including intracranial electrocorticography, implanted devices, and multichannel electroencephalography, and required patient‐specific adaptation or learning to perform optimally, all of which limit translation to broad clinical application. To facilitate broader adaptation of seizure forecasting in clinical practice, noninvasive, easily applicable techniques that reliably assess seizure risk without much prior tuning are crucial. Wristbands that continuously record physiological parameters, including electrodermal activity, body temperature, blood volume pulse, and actigraphy, may afford monitoring of autonomous nervous system function and movement relevant for such a task, hence minimizing potential complications associated with invasive monitoring and avoiding stigma associated with bulky external monitoring devices on the head. Methods: Here, we applied deep learning on multimodal wristband sensor data from 69 patients with epilepsy (total duration > 2311 hours, 452 seizures) to assess its capability to forecast seizures in a statistically significant way. Results: Using a leave‐one‐subject‐out cross‐validation approach, we identified better‐than‐chance predictability in 43% of the patients. Time‐matched seizure surrogate data analyses indicated forecasting not to be driven simply by time of day or vigilance state. Prediction performance peaked when all sensor modalities were used, and did not differ between generalized and focal seizure types, but generally increased with the size of the training dataset, indicating potential further improvement with larger datasets in the future. Significance: Collectively, these results show that statistically significant seizure risk assessments are feasible from easy‐to‐use, noninvasive wearable devices without the need of patient‐specific training or parameter optimization.