Noninvasive detection of focal seizures in ambulatory patients.

Autor: Ryvlin P; Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland., Cammoun L; Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland., Hubbard I; Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland., Ravey F; Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland., Beniczky S; Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark.; Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark., Atienza D; Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland.; Embedded Systems Laboratory, Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland.
Jazyk: angličtina
Zdroj: Epilepsia [Epilepsia] 2020 Nov; Vol. 61 Suppl 1, pp. S47-S54. Date of Electronic Publication: 2020 Jun 02.
DOI: 10.1111/epi.16538
Abstrakt: Reliably detecting focal seizures without secondary generalization during daily life activities, chronically, using convenient portable or wearable devices, would offer patients with active epilepsy a number of potential benefits, such as providing more reliable seizure count to optimize treatment and seizure forecasting, and triggering alarms to promote safeguarding interventions. However, no generic solution is currently available to reach these objectives. A number of biosignals are sensitive to specific forms of focal seizures, in particular heart rate and its variability for seizures affecting the neurovegetative system, and accelerometry for those responsible for prominent motor activity. However, most studies demonstrate high rates of false detection or poor sensitivity, with only a minority of patients benefiting from acceptable levels of accuracy. To tackle this challenging issue, several lines of technological progress are envisioned, including multimodal biosensing with cross-modal analytics, a combination of embedded and distributed self-aware machine learning, and ultra-low-power design to enable appropriate autonomy of such sophisticated portable solutions.
(© 2020 The Authors. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)
Databáze: MEDLINE
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