Circadian Phase Locking of Epilepsy Seizures in Wearable Data: A Single-Patient Case Study
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911608201019392 |
|---|---|
| author | Ewart-James, Berenika Wragg, Matthew Keshtmand, Nawid Brigden, Amberly Marshall, Paul Santos-Rodriguez, Raul |
| author_facet | Ewart-James, Berenika Wragg, Matthew Keshtmand, Nawid Brigden, Amberly Marshall, Paul Santos-Rodriguez, Raul |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_18297 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Circadian Phase Locking of Epilepsy Seizures in Wearable Data: A Single-Patient Case Study Ewart-James, Berenika Wragg, Matthew Keshtmand, Nawid Brigden, Amberly Marshall, Paul Santos-Rodriguez, Raul Human-Computer Interaction 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 |
| title | Circadian Phase Locking of Epilepsy Seizures in Wearable Data: A Single-Patient Case Study |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2604.18297 |