Convulsive seizure detection using a wrist‐worn electrodermal activity and accelerometry biosensor
Abstract
Summary: The special requirements for a seizure detector suitable for everyday use in terms of cost, comfort, and social acceptance call for alternatives to electroencephalography (EEG)–based methods. Therefore, we developed an algorithm for automatic detection of generalized tonic–clonic (GTC) seizures based on sympathetically mediated electrodermal activity (EDA) and accelerometry measured using a novel wrist‐worn biosensor. The problem of GTC seizure detection was posed as a supervised learning task in which the goal was to classify 10‐s epochs as a seizure or nonseizure event based on 19 extracted features from EDA and accelerometry recordings using a Support Vector Machine. Performance was evaluated using a double cross‐validation method. The new seizure detection algorithm was tested on >4,213 h of recordings from 80 patients and detected 15 (94%) of 16 of the GTC seizures from seven patients with 130 false alarms (0.74 per 24 h). This algorithm can potentially provide a convulsive seizure alarm system for caregivers and objective quantification of seizure frequency.
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One epilepsy study has suggested that the use of electrodermal activity together with accelerometry might increase sensitivity and specificity in seizure detection, compared to the use of accelerometry only.
“One epilepsy study has suggested that the use of electrodermal activity together with accelerometry might increase sensitivity and specificity in seizure detection, compared to the use of accelerometry only [72].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [72]
In a study of 80 pediatric patients with varying degrees of epilepsy, Empatica Embrace detected 94% of GTC seizures with less than one false alarm per 24-h period.
“In a study of 80 pediatric patients with varying degrees of epilepsy, Embrace detected 94% of GTC seizures with less than one false alarm per 24-h period [20].”
From Wearables and the Medical Revolution (2018) · cites this paper as [20]
The Empatica Embrace can detect seizures using electrodermal activity and accelerometry and can also notify emergency contacts.
“The Empatica Embrace (MA, USA), a smartwatch that evolved out of the MIT Media Lab, can detect seizures using electrodermal activity and accelerometry and can also notify emergency contacts [20,21,103].”
From Wearables and the Medical Revolution (2018) · cites this paper as [20]
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- Authors
- Poh, M., Loddenkemper, T., Reinsberger, C., Swenson, N. C., Goyal, S., Sabtala, M. C., Madsen, J. R., & Picard, R. W.
- Affiliations
- Harvard University (US)
- Boston Children's Hospital (US)
- Brigham and Women's Hospital (US)
- MIT Media Lab (US)
- Corresponding author
- Poh, Ming‐Zher
- ORCID
- Venue
- Epilepsia
- Publisher
- Wiley
- Published
- 2012-03-20
- Volume
- 53
- Issue
- 5
- Pages
- e93-7
- ISSN
- 0013-9580 (ISSN-L)
- 1528-1167
- OpenAlex
- W2138499982
- Cited by
- 272
- References
- 12
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 18 · 2023: 15 · 2022: 24 · 2021: 31 · 2020: 28 · 2019: 23 · 2018: 35 · 2017: 16 · 2016: 18 · 2015: 20 · 2014: 12 · 2013: 8 · 2012: 2
- FWCI
- 6.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Epilepsia (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Nancy Lurie Marks Family Foundation
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Psychiatry and Mental health › Epilepsy research and treatment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- generalized tonic-clonic seizures, electrodermal activity, accelerometry, support vector machine classification, epilepsy
- MeSH terms
- Adolescent, Child, Child, Preschool, Electroencephalography, Female, Galvanic Skin Response, Humans, Kinetocardiography, Male, Seizures, Time Factors, Wrist, Biosensing Techniques
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