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Abstract Objective. This exploratory study investigates cyclical changes in physiological features across the menstrual cycle in women with epilepsy, focusing on their potential relationship with seizure occurrence.Approach. Nocturnal data during sleep were collected from two women with ovulatory cycles and compared with data from healthy controls, two non-ovulatory women, one postmenopausal woman, and two male patients. The aim was to characterize signal patterns across different reproductive states and to explore whether menstrual-related rhythms correspond to seizure timing. Circular statistics mapped signals onto an angular scale, allowing identification of biphasic patterns linked to ovulation, while machine learning algorithms identified ovulatory phases.Main Results. In ovulatory participants, seizure activity predominantly occurred around the late luteal and early follicular phases (p < 0.05), and non-uniform and biphaisc trends were observed in temperature, resembling patterns in healthy participants. In contrast, individuals taking enzyme-inducing antiepileptic drugs showed disrupted physiological rhythms. Although hormonal fluctuations appear to drive cyclical patterns, additional rhythms (e.g. weekly) were also observed, suggesting multifactorial influences.Significance. These preliminary findings underscore the need to account for menstrual and other biological cycles in seizure forecasting models and provide a foundation for future studies involving larger cohorts. 

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.