Ambulatory seizure detection.

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Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_124B9EC42341
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Ambulatory seizure detection.
Journal
Current opinion in neurology
Author(s)
Bernini A., Dan J., Ryvlin P.
ISSN
1473-6551 (Electronic)
ISSN-L
1350-7540
Publication state
Published
Issued date
01/04/2024
Peer-reviewed
Oui
Volume
37
Number
2
Pages
99-104
Language
english
Notes
Publication types: Review ; Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
To review recent advances in the field of seizure detection in ambulatory patients with epilepsy.
Recent studies have shown that wrist or arm wearable sensors, using 3D-accelerometry, electrodermal activity or photoplethysmography, in isolation or in combination, can reliably detect focal-to-bilateral and generalized tonic-clonic seizures (GTCS), with a sensitivity over 90%, and false alarm rates varying from 0.1 to 1.2 per day. A headband EEG has also demonstrated a high sensitivity for detecting and help monitoring generalized absence seizures. In contrast, no appropriate solution is yet available to detect focal seizures, though some promising findings were reported using ECG-based heart rate variability biomarkers and subcutaneous EEG.
Several FDA and/or EU-certified solutions are available to detect GTCS and trigger an alarm with acceptable rates of false alarms. However, data are still missing regarding the impact of such intervention on patients' safety. Noninvasive solutions to reliably detect focal seizures in ambulatory patients, based on either EEG or non-EEG biosignals, remain to be developed. To this end, a number of challenges need to be addressed, including the performance, but also the transparency and interpretability of machine learning algorithms.
Keywords
Humans, Electroencephalography, Seizures/diagnosis, Epilepsy, Algorithms, Machine Learning
Pubmed
Web of science
Open Access
Yes
Create date
25/03/2024 14:58
Last modification date
21/06/2024 13:32
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