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Multimodal wrist-worn devices for seizure detection and advancing research: Focus on the Empatica wristbands
Open accessAbstract
Wearable automated seizure detection devices offer a high potential to improve seizure management, through continuous ambulatory monitoring, accurate seizure counts, and real-time alerts for prompt intervention. More importantly, these devices can be a life-saving help for people with a higher risk of sudden unexpected death in epilepsy (SUDEP), especially in case of generalized tonic-clonic seizures (GTCS). The Embrace and E4 wristbands (Empatica) are the first commercially available multimodal wristbands that were designed to sense the physiological hallmarks of ongoing GTCS: while Embrace only embeds a machine learning-based detection algorithm, both E4 and Embrace devices are equipped with motion (accelerometers, ACC) and electrodermal activity (EDA) sensors and both the devices received medical clearance (E4 from EU CE, Embrace from EU CE and US FDA). The aim of this contribution is to provide updated evidence of the effectiveness of GTCS detection and monitoring relying on the combination of ACM and EDA sensors. A machine learning algorithm able to recognize ACC and EDA signatures of GTCS-like events has been developed on E4 data, labeled using gold-standard video-EEG examined by epileptologists in clinical centers, and has undergone continuous improvement. While keeping an elevated sensitivity to GTCS (92-100%), algorithm improvements and growing data availability led to lower false alarm rate (FAR) from the initial ˜2 down to 0.2-1 false alarms per day, as showed by retrospective and prospective analyses in inpatient settings. Algorithm adjustment to better discriminate real-life physical activities from GTCS, has brought the initial FAR of ˜6 on outpatient real life settings, down to values comparable to best-case clinical settings (FAR < 0.5), with comparable sensitivity. Moreover, using multimodal sensing, it has been possible not only to detect GTCS but also to quantify seizure-induced autonomic dysfunction, based on automatic features of abnormal motion and EDA. The latter biosignal correlates with the duration of post-ictal generalized EEG suppression, a biomarker observed in 100% of monitored SUDEP cases.
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“Empatica received FDA approval in 2018 for their wearable Embrace, which associated with electrodermal captors can detect generalized epilepsy seizures and report to a mobile application that is able to alert close relatives and trusted physician with complementary information about patient localization (41).”
From Artificial Intelligence in Medicine: Today and Tomorrow (2020) · cites this paper as [41]
Experimental — accuracy not yet measuredIn the citing paper
“In seizure monitoring, well-established wearables are the Empatica Embrace and E466.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [66]
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Boucsein, W. (2012) · doi:10.1007/978-1-4614-1126-0 · Cited by 2,526
Ryvlin, P., Nashef, L. A. M., Lhatoo, S. D., Bateman, L. M., Bird, J., Bleasel, A., Boon, P., Crespel, A., Dworetzky, B. A., Høgenhaven, H., et al. (2013) · The Lancet Neurology · doi:10.1016/s1474-4422(13)70214-x · Cited by 1,137
Convulsive seizure detection using a wrist‐worn electrodermal activity and accelerometry biosensor
Poh, M., Loddenkemper, T., Reinsberger, C., Swenson, N. C., Goyal, S., Sabtala, M. C., Madsen, J. R., & Picard, R. W. (2012) · Epilepsia · doi:10.1111/j.1528-1167.2012.03444.x · Cited by 272
Self-citationAutonomic changes with seizures correlate with postictal EEG suppression
Poh, M., Loddenkemper, T., Reinsberger, C., Swenson, N. C., Goyal, S., Madsen, J. R., & Picard, R. W. (2012) · Neurology · doi:10.1212/wnl.0b013e318258f7f1 · Cited by 242
Self-citationBeniczky, S., Polster, T., Kjaer, T. W., & Hjalgrim, H. (2013) · Epilepsia · doi:10.1111/epi.12120 · Cited by 230
Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors
Onorati, F., Regalia, G., Caborni, C., Migliorini, M., Bender, D., Poh, M., Frazier, C., Kovitch Thropp, E., Mynatt, E. D., Bidwell, J., et al. (2017) · Epilepsia · doi:10.1111/epi.13899 · Cited by 212
Self-citationStandards for testing and clinical validation of seizure detection devices
Beniczky, S., & Ryvlin, P. (2018) · Epilepsia · doi:10.1111/epi.14049 · Cited by 150
Meritam, P., Ryvlin, P., & Beniczky, S. (2018) · Epilepsia · doi:10.1111/epi.14051 · Cited by 90
Wearable devices for sudden unexpected death in epilepsy prevention
Ryvlin, P., Ciumas, C., Wisniewski, I., & Beniczky, S. (2018) · Epilepsia · doi:10.1111/epi.14054 · Cited by 87
Multimodal seizure detection: A review
Leijten, F. S. S. (2018) · Epilepsia · doi:10.1111/epi.14047 · Cited by 78
Research works printed but not linked: 4
- Caborni, 2017, Tuning decision thresholds for active/rest periods significantly improves seizure detection algorithm performance: an evaluation using Embrace Smartwatch on outpatient settings, Epilepsia, 58, S1
- Onorati, 2016, Improvement of a convulsive seizure detector relying on accelerometer and electrodermal activity collected continuously by a wristband, Epilepsy Pipeline Conference
- Onorati, 2016, Improving performances of Embrace, a convulsive seizure detector wristband: from small clinical setting to large outpatients’ framework, Epilepsia, 57, 1
- Picard, 2016, Embrace, a wearable convulsive seizure detection and alert system - first performance report of a case study in real-life settings, American Epilepsy Society Annual Meeting 2016
Cited by
Sorted by citations
Benjamens, S., Dhunnoo, P., & Meskó, B. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00324-0 · Cited by 1,215
Artificial Intelligence in Medicine: Today and Tomorrow
Briganti, G., & Le Moine, O. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.00027 · Cited by 743
Machine Learning for Healthcare Wearable Devices: The Big Picture
Sabry, F., Eltaras, T., Labda, W., Alzoubi, K., & Malluhi, Q. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4653923 · Cited by 321
AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring
Wasilewski, T., Kamysz, W., & Gębicki, J. (2024) · Biosensors · doi:10.3390/bios14070356 · Cited by 328
Vos, G., Trinh, K., Sarnyai, Z., & Azghadi, M. R. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105026 · Cited by 200
Metadata
- Authors
- Regalia, G., Onorati, F., Lai, M., Caborni, C., & Picard, R. W.
- Affiliations
- MIT Media Lab (US)
- Corresponding author
- Regalia, Giulia
- Venue
- Epilepsy Research
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 153
- Pages
- 79-82
- ISSN
- 0920-1211 (ISSN-L)
- OpenAlex
- W2916057939
- Cited by
- 240
- References
- 18
- Citations by year
- 2026: 22 · 2025: 34 · 2024: 32 · 2023: 37 · 2022: 36 · 2021: 44 · 2020: 28 · 2019: 7
- FWCI
- 22.86
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Epilepsy Research (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
- Topics
- Health Sciences › Medicine › Clinical Neurology › Epilepsy research and treatment
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- automated seizure detection, generalized tonic-clonic seizures, Empatica E4, wrist-worn devices, electrodermal activity, accelerometer, multimodal biosensing, sudden unexpected death in epilepsy, autonomic dysfunction, false alarm rate, seizure monitoring
- MeSH terms
- Machine Learning, Wearable Electronic Devices, Clinical Trials as Topic, Electroencephalography, Galvanic Skin Response, Humans, Longitudinal Studies, Seizures, Wrist, Biomedical Research
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