Circadian Phase Locking of Epilepsy Seizures in Wearable Data: A Single-Patient Case Study

TL;DR

This study investigates the relationship between seizure timing and circadian rhythms using wearable data from a single epilepsy patient, highlighting the potential of physiological phase modeling for seizure forecasting.

Contribution

It demonstrates that seizure onsets show significant circadian phase preference when analyzed through wearable-derived physiological signals, offering a novel approach for seizure prediction.

Findings

Seizures exhibit significant circadian phase concentration.

Multiday rhythms do not show consistent phase clustering.

Physiological phase modeling can enhance seizure forecasting.

Abstract

Epilepsy is a common, chronic neurological disorder characterized by recurrent seizures caused by sudden bursts of abnormal electrical activity in the brain. Seizures can often be unpredictable, leading to uncertainty and anxiety for people with epilepsy. To address this problem, the Epilepsy UK Priority Setting Partnership identified research into seizure forecasting technology as a priority. Seizure onsets are recorded as discrete events embedded within continuously sampled physiological signals that exhibit strong circadian and multi-day rhythms. Standard modelling approaches often treat time as linear or rely on clock-time features, which may not explicitly capture the underlying physiological phase. In this paper, we examine whether seizure onsets exhibit phase preference relative to circadian rhythms derived from wearable inter-beat interval (IBI) data. As a proof-of-concept, using 176 days wearable and seizure diary data from a single patient, we extract oscillatory components via band-limited filtering and Hilbert-based phase estimation, and test for non-uniform seizure-phase alignment using circular statistics. We observe significant circadian phase concentration, while multiday bands do not show consistent or statistically significant phase clustering in this dataset. Exploratory logistic baselines indicate modest but detectable structure beyond simple clock-time effects. We argue that explicit physiological phase representations provide an interpretable bridge between continuous wearable sensing and sparse clinical events and may augment existing seizure forecasting pipelines. We discuss implications for multi-scale modelling, patient-facing interfaces, and future multi-patient validation