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

翻译：癫痫是一种常见的慢性神经系统疾病，其特征为大脑异常电活动突发导致反复发作的癫痫症状。癫痫发作往往不可预测，给患者带来不确定性和焦虑。为解决此问题，英国癫痫优先事项设定合作组织将癫痫发作预测技术研究列为优先方向。癫痫发作起始事件被记录为嵌入连续采集体征信号中的离散事件，而这些信号本身呈现强烈的昼夜节律和多日节律。标准建模方法通常将时间视为线性或依赖时钟时间特征，可能无法明确捕捉潜在的生理相位。本文探究癫痫发作起始事件是否表现出与可穿戴设备心搏间隔数据衍生的昼夜节律相关的相位偏好。作为概念验证，我们采用单名患者176天的可穿戴设备与癫痫日记数据，通过带限滤波和希尔伯特相位估计提取振荡成分，并运用圆形统计检验发作-相位对齐的非均匀性。我们观察到显著的昼夜节律相位集中现象，而多日节律频带在本数据集中未呈现一致或统计显著的相位聚类。探索性逻辑基线模型表明存在超越简单时钟时间效应的微弱但可检测的结构性特征。我们认为，明确的生理相位表征为连接连续可穿戴传感数据与稀疏临床事件提供了可解释的桥梁，并可能增强现有癫痫发作预测流程。本文讨论了该发现对多尺度建模、患者交互界面及未来多患者验证的影响。