• Detection of nocturnal epilept...

Treffer: Detection of nocturnal epileptic seizures using a wearable armband: A deep learning approach combining accelerometry and photoplethysmography signals.

Title:
Detection of nocturnal epileptic seizures using a wearable armband: A deep learning approach combining accelerometry and photoplethysmography signals.
Authors:
Dong C; Department of Medical Imaging, Hebei Medical University, 050031 Shijiazhuang, Hebei, China; Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, Netherlands., van Dijk JP; Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, Netherlands; The Kempenhaeghe Academic Center for Epileptology, 5591 VE Heeze, Netherlands., Aarts RM; Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, Netherlands., Wang Y; The Institute of Microelectronics, Chinese Academy of Sciences, 100029 Beijing, China., Long X; Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, Netherlands. Electronic address: x.long@tue.nl.
Source:
Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2026 Jan; Vol. 273, pp. 109087. Date of Electronic Publication: 2025 Sep 29.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier Scientific Publishers Country of Publication: Ireland NLM ID: 8506513 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-7565 (Electronic) Linking ISSN: 01692607 NLM ISO Abbreviation: Comput Methods Programs Biomed Subsets: MEDLINE
Imprint Name(s):
Publication: Limerick : Elsevier Scientific Publishers
Original Publication: Amsterdam : Elsevier Science Publishers, c1984-
MeSH Terms:
Photoplethysmography* , Deep Learning* , Accelerometry* , Wearable Electronic Devices* , Seizures*/diagnosis , Epilepsy*/diagnosis, Humans ; Male ; Female ; Prospective Studies ; Adult ; Middle Aged ; Algorithms ; Signal Processing, Computer-Assisted ; Electroencephalography ; Adolescent ; Young Adult
Contributed Indexing:
Keywords: Accelerometry; Deep learning; Long-term monitoring; Photoplethysmography; Wearable sensor; seizure detection
Entry Date(s):
Date Created: 20251010 Date Completed: 20251112 Latest Revision: 20251112
Update Code:
20251113
DOI:
10.1016/j.cmpb.2025.109087
PMID:
41072127
Database:
MEDLINE

Weitere Informationen

Background: Epileptic seizures can lead to severe outcomes including sudden unexpected death in epilepsy (SUDEP). Clinical standard for seizure diagnosis and detection requires electroencephalography and video monitoring, which is yet considered not suitable for home use, especially during nighttime sleep in a low-light condition. We proposed a deep learning (DL)-based approach to automatically detect nocturnal major seizures using a wearable armband that can potentially help reduce SUDEP risk through timely caregiver intervention.
Methods: In this prospective cohort study, 68 patients with major seizures were monitored for up to three months using a wearable armband (NightWatch®) capturing tri-axial accelerometry (ACM) and photoplethysmography (PPG) signals. A two-step approach was designed: (1) a pre-screening step using threshold-based algorithms to identify suspected seizure events (ACM standard deviation >0.4 or heart rate increase >10%), and (2) a DL model (CNN-LSTM with attention mechanism) to recognize true seizures. Model performance was evaluated via a 10-fold cross-validation, reporting sensitivity (SEN), false alarm rate (FAR), and area under the ROC curve (AUC).
Results: In 788 overnight recordings (6304 hours), a total of 1846 severe seizures were identified. The pre-screening step achieved 0.940 sensitivity in pre-identifying or 'preserving' seizures, reducing data volume by 81% (from 6304 to 1201 hours). The DL model demonstrated a mean accuracy of 0.793 [95% CI: 0.745-0.841], a mean sensitivity of 0.762 [95% CI: 0.704-0.821], a mean positive predictive value of 0.334 [95% CI: 0.229-0.356] and a mean false alarm rate of 0.165/hour [95% CI: 0.097-0.234]. These results exceeded those of single (signal) modality detection methods.
Conclusion: 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.
(Copyright © 2025 The Author(s). Published by Elsevier B.V. All rights reserved.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.