Detection of nocturnal epileptic seizures using a wearable armband: A deep learning approach combining accelerometry and photoplethysmography signals
Our two-step approach enables accurate, long-term detection of severe nocturnal seizures in home settings. The wearable system provides a practical solution for continuous monitoring and real-time alerts, thus potentially reducing SUDEP risk and improving patient safety, fulfilling an urgent unmet need in epilepsy care. Furthermore, by enabling long-term home monitoring, this system may help assess the relationship between seizure events and lifestyle-related triggers such as sleep deprivation, stress, physical exertion, or alcohol consumption, thereby supporting the development of personalized preventive strategies.
Authors
- Chunjiao Dong (ORCID: https://orcid.org/0000-0001-9141-4783)
- Ronald M. Aarts (ORCID: https://orcid.org/0000-0003-3194-0700)
- Xi Long (ORCID: https://orcid.org/0000-0001-9505-1270)
- Johannes van Dijk (ORCID: https://orcid.org/0000-0001-6188-2973)
- Yunfeng Wang (ORCID: https://orcid.org/0000-0001-7627-0929)
Institutions
- Hebei Medical University (CN)
- Institute of Microelectronics (SG)
- Institute of Microelectronics (CN)
- Kempenhaeghe (NL)
- Eindhoven University of Technology (NL)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2025-09-29
- DOI
- https://doi.org/10.1016/j.cmpb.2025.109087
- Citations
- 1
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 1.43
Funders
- Hebei Provincial Department of Human Resources and Social Security
- Hebei Medical University