Non-invasive wearable seizure detection using long–short-term memory networks with transfer learning
Open accessAbstract
Objective . The detection of seizures using wearable devices would improve epilepsy management, but reliable detection of seizures in an ambulatory environment remains challenging, and current studies lack concurrent validation of seizures using electroencephalography (EEG) data. Approach . An adaptively trained long–short-term memory deep neural network was developed and trained using a modest number of seizure data sets from wrist-worn devices. Transfer learning was used to adapt a classifier that was initially trained on intracranial electroencephalography (iEEG) signals to facilitate classification of non-EEG physiological datasets comprising accelerometry, blood volume pulse, skin electrodermal activity, heart rate, and temperature signals. The algorithm’s performance was assessed with and without pre-training on iEEG signals and transfer learning. To assess the performance of the seizure detection classifier using long-term ambulatory data, wearable devices were used for multiple months with an implanted neurostimulator capable of recording iEEG signals, which provided independent electrographic seizure detections that were reviewed by a board-certified epileptologist. Main results . For 19 motor seizures from 10 in-hospital patients, the algorithm yielded a mean area under curve (AUC), a sensitivity, and an false alarm rate per day (FAR/day) of 0.98, 0.93, and 2.3, respectively. Additionally, for eight seizures with probable motor semiology from two ambulatory patients, the classifier achieved a mean AUC of 0.97 and an FAR of 2.45 events/day at a sensitivity of 0.9. For all seizure types in the ambulatory setting, the classifier had a mean AUC of 0.82 with a sensitivity of 0.47 and an FAR of 7.2 events/day. Significance . The performance of the algorithm was evaluated using motor and non-motor seizures during in-hospital and ambulatory use. The classifier was able to detect multiple types of motor and non-motor seizures, but performed significantly better on motor seizures.
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A clean BVP signal from the Empatica E4 without noise from motion artifacts has a smooth quasi-periodic signature, which can be separated from random noise or motion artifacts by analyzing the entropy of the signal spectrum.
“A clean BVP signal from the Empatica E4 without noise from motion artifacts has a smooth quasi-periodic signature, which can be separated from random noise or, more importantly, from motion artifacts by analyzing the entropy of the signal spectrum.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [23]
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- Authors
- Nasseri, M., Pal Attia, T., Joseph, B., Gregg, N. M., Nurse, E. S., Viana, P. F., Schulze-Bonhage, A., Dümpelmann, M., Worrell, G., Freestone, D. R., Richardson, M. P., & Brinkmann, B. H.
- Affiliations
- University of North Florida (US)
- Mayo Clinic (US)
- The University of Melbourne (AU)
- University of Lisbon (PT)
- University of Freiburg (DE)
- King's College London (GB)
- Corresponding author
- Brinkmann, Benjamin H
- ORCID
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2021-04-08
- Volume
- 18
- Issue
- 5
- Pages
- 056017
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W3137217840
- Cited by
- 66
- References
- 38
- Citations by year
- 2026: 6 · 2025: 20 · 2024: 9 · 2023: 13 · 2022: 13 · 2021: 5
- FWCI
- 5.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- University of North Florida Digital Commons (University of North Florida) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion)
- Funders
- Epilepsy Foundation (Epilepsy Innovation Institute My Seizure Gauge)
- American Epilepsy Society (Research & Training Fellowship for Clinicians (N.M)
- 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
- seizure detection, epilepsy, wearable devices, long short-term memory networks, transfer learning, intracranial EEG, ambulatory EEG, motor seizures, accelerometry, blood volume pulse, electrodermal activity, heart rate, implanted neurostimulator, false alarm rate
- MeSH terms
- Machine Learning, Wearable Electronic Devices, Algorithms, Electroencephalography, Humans, Seizures, Neural Networks, Computer
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