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Abstract ObjectiveSeizure unpredictability can be debilitating and dangerous for people with epilepsy. Accurate seizure forecasters could improve quality of life for those with epilepsy but must be practical for long‐term use. This study presents the first validation of a seizure‐forecasting system using ultra‐long‐term, non‐invasive wearable data. MethodsEleven participants with epilepsy were recruited for continuous monitoring, capturing heart rate and step count via wrist‐worn devices and seizures via electroencephalography (average recording duration of 337 days). Two hybrid models—combining machine learning and cycle‐based methods—were proposed to forecast seizures at both short (minutes) and long (up to 44 days) horizons. ResultsThe Seizure Warning System (SWS), designed for forecasting near‐term seizures, and the Seizure Risk System (SRS), designed for forecasting long‐term risk, both outperformed traditional models. In addition, the SRS reduced high‐risk time by 29% while increasing sensitivity by 11%. SignificanceThese improvements mark a significant advancement in making seizure forecasting more practical and effective.
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Seizure occurrence is linked to multiday cycles in diverse physiological signals
Abstract ObjectiveThe factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing. MethodsIn this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist‐worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter–Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology. ResultsTen subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal. SignificanceSeizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time‐varying approaches to epilepsy care.
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- Award ID(s):
- 2138378
- PAR ID:
- 10419139
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Epilepsia
- Volume:
- 64
- Issue:
- 6
- ISSN:
- 0013-9580
- Page Range / eLocation ID:
- p. 1627-1639
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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