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Cited by 240Epilepsy research and treatmentEEG and Brain-Computer InterfacesContext-Aware Activity Recognition Systems

The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.

Multimodal wrist-worn devices for seizure detection and advancing research: Focus on the Empatica wristbands

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Regalia, Giulia, Onorati, Francesco, Lai, Matteo, Caborni, Chiara, Picard, Rosalind W.·2019·Epilepsy Research
DOI 10.1016/j.eplepsyres.2019.02.007
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Abstract

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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Research works printed but not linked: 4

  1. 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
  2. Onorati, 2016, Improvement of a convulsive seizure detector relying on accelerometer and electrodermal activity collected continuously by a wristband, Epilepsy Pipeline Conference
  3. Onorati, 2016, Improving performances of Embrace, a convulsive seizure detector wristband: from small clinical setting to large outpatients’ framework, Epilepsia, 57, 1
  4. 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

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Metadata

Authors
Regalia, G., Onorati, F., Lai, M., Caborni, C., & Picard, R. W.
Affiliations
MIT Media Lab (US)
Corresponding author
Regalia, Giulia
ORCID
Venue
Epilepsy Research
Publisher
Elsevier BV
Published
2019-07
Volume
153
Pages
79-82
ISSN
0920-1211 (ISSN-L)
DOI
10.1016/j.eplepsyres.2019.02.007
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
Licence
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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