Detecting Tonic-Clonic Seizures in Multimodal Biosignal Data From Wearables: Methodology Design and Validation
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Abstract
Background: Video electroencephalography recordings, routinely used in epilepsy monitoring units, are the gold standard for monitoring epileptic seizures. However, monitoring is also needed in the day-to-day lives of people with epilepsy, where video electroencephalography is not feasible. Wearables could fill this gap by providing patients with an accurate log of their seizures. Objective: Although there are already systems available that provide promising results for the detection of tonic-clonic seizures (TCSs), research in this area is often limited to detection from 1 biosignal modality or only during the night when the patient is in bed. The aim of this study is to provide evidence that supervised machine learning can detect TCSs from multimodal data in a new data set during daytime and nighttime. Methods: An extensive data set of biosignals from a multimodal watch worn by people with epilepsy was recorded during their stay in the epilepsy monitoring unit at 2 European clinical sites. From a larger data set of 243 enrolled participants, those who had data recorded during TCSs were selected, amounting to 10 participants with 21 TCSs. Accelerometry and electrodermal activity recorded by the wearable device were used for analysis, and seizure manifestation was annotated in detail by clinical experts. Ten accelerometry and 3 electrodermal activity features were calculated for sliding windows of variable size across the data. A gradient tree boosting algorithm was used for seizure detection, and the optimal parameter combination was determined in a leave-one-participant-out cross-validation on a training set of 10 seizures from 8 participants. The model was then evaluated on an out-of-sample test set of 11 seizures from the remaining 2 participants. To assess specificity, we additionally analyzed data from up to 29 participants without TCSs during the model evaluation. Results: In the leave-one-participant-out cross-validation, the model optimized for sensitivity could detect all 10 seizures with a false alarm rate of 0.46 per day in 17.3 days of data. In a test set of 11 out-of-sample TCSs, amounting to 8.3 days of data, the model could detect 10 seizures and produced no false positives. Increasing the test set to include data from 28 more participants without additional TCSs resulted in a false alarm rate of 0.19 per day in 78 days of wearable data. Conclusions: We show that a gradient tree boosting machine can robustly detect TCSs from multimodal wearable data in an original data set and that even with very limited training data, supervised machine learning can achieve a high sensitivity and low false-positive rate. This methodology may offer a promising way to approach wearable-based nonconvulsive seizure detection.
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Ambulatory seizure monitoring using wearables is now within reach.
“Ambulatory seizure monitoring using wearables is now within reach1–7”
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- Authors
- Böttcher, S., Bruno, E., Manyakov, N. V., Epitashvili, N., Claes, K., Glasstetter, M., Thorpe, S., Lees, S., Dümpelmann, M., Van Laerhoven, K., Richardson, M. P., Schulze-Bonhage, A., & The RADAR-CNS Consortium
- Affiliations
- University of Freiburg (DE)
- King's College London (GB)
- Janssen (Belgium) (BE)
- Corresponding author
- Böttcher, Sebastian
- ORCID
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2021-11-19
- Volume
- 9
- Issue
- 11
- Pages
- e27674
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC8663471
- OpenAlex
- W3196916920
- Cited by
- 39
- References
- 35
- Citations by year
- 2026: 9 · 2025: 4 · 2024: 10 · 2023: 8 · 2022: 6 · 2021: 2
- FWCI
- 3.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Funders
- Wellcome Trust
- European Federation of Pharmaceutical Industries and Associations
- European Commission (115902, HORIZON2020)
- Albert-Ludwigs-Universität Freiburg
- Innovative Medicines Initiative (115902)
- 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
- tonic-clonic seizures, seizure detection, epilepsy, wearable devices, multimodal biosignals, accelerometry, electrodermal activity, gradient boosting decision tree, epilepsy monitoring unit, false alarm rate, smartwatches, out-of-sample evaluation, sensitivity and specificity
- MeSH terms
- Wearable Electronic Devices, Algorithms, Electroencephalography, Humans, Seizures, Accelerometry
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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