Machine learning and wearable devices of the future
Open accessAbstract
Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applications of ML in epilepsy is seizure detection and prediction, using wearable devices (WDs). However, not all currently available algorithms implemented in WDs are using ML. In this review, we summarize the state of the art of using WDs and ML in epilepsy, and we outline future development in these domains. There is published evidence for reliable detection of epileptic seizures using implanted electroencephalography (EEG) electrodes and wearable, non‐EEG devices. Application of ML using the data recorded with WDs from a large number of patients could change radically the way we diagnose and manage patients with epilepsy.
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Ambulatory seizure monitoring using wearables is now within reach.
“Ambulatory seizure monitoring using wearables is now within reach1–7”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [2]
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Research works printed but not linked: 2
- An S, Malhotra K, Dilley C, et al. Predicting drug-resistant epilepsy -A machine learning approach based on administrative claims data. Epilepsy Behav 2018; 89:118-25.
- Beniczky, S., Karoly, P., Nurse, E., Ryvlin, P. & Cook, M. (2020). Machine learning and wearable devices of the future. EPILEPSIA, 62 (S2), pp.S116-S124. https://doi.org/10.1111/epi.16555. Persistent Link: http://hdl.handle.net/11343/276056 File Description: Accepted
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Self-citation
- Authors
- Beniczky, S., Karoly, P., Nurse, E., Ryvlin, P., & Cook, M.
- Affiliations
- Aarhus University (DK)
- The University of Melbourne (AU)
- Centre Hospitalier Universitaire Vaudois (CH)
- Corresponding author
- Beniczky, Sándor
- Venue
- Epilepsia
- Publisher
- Wiley
- Published
- 2020-07-26
- Volume
- 62
- Issue
- S2
- Pages
- S116-S124
- ISSN
- 0013-9580 (ISSN-L)
- 1528-1167
- OpenAlex
- W3044588961
- Cited by
- 155
- References
- 88
- Citations by year
- 2026: 14 · 2025: 23 · 2024: 31 · 2023: 34 · 2022: 27 · 2021: 23 · 2020: 3
- FWCI
- 2.94
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Epilepsia (publishedVersion)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- IRIS (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion)
- Funders
- Epilepsy 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
- epilepsy, seizure detection, seizure prediction, wearable devices, machine learning
- MeSH terms
- Machine Learning, Wearable Electronic Devices, Electroencephalography, Forecasting, Humans, Seizures
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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