Research Area:  Machine Learning

Abstract:

Epilepsy affects almost 1% of the population and most of the approximately 20–30% of patients with refractory epilepsy have one or more seizures per month. Seizure detection devices allow an objective assessment of seizure frequency and a treatment tailored to the individual patient. A rapid recognition and treatment of seizures through closed-loop systems could potentially decrease morbidity and mortality in epilepsy. However, no single detection device can detect all seizure types. Therefore, the choice of a seizure detection device should consider the patient-specific seizure semiologies. This review of the literature evaluates seizure detection devices and their effectiveness for different seizure types. Our aim is to summarize current evidence, offer suggestions on how to select the most suitable seizure detection device for each patient and provide guidance to physicians, families and researchers when choosing or designing seizure detection devices. Further, this review will guide future prospective validation studies.

Keywords:  

Author(s) Name:  Ulate-Campos .A , Coughlin .F , Gaínza Lein .M, Sánchez Fernández .I , Pearl .P.L , Loddenkemper T

Publisher name:  ELSEVIER

DOI:  10.1016/j.seizure.2016.06.008

Volume Information:  Volume 40, August 2016, Pages 88-101

Paper Link:   https://www.sciencedirect.com/science/article/pii/S1059131116300711