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Machine learning and wearable devices of the future
Author(s) -
Beniczky Sándor,
Karoly Philippa,
Nurse Ewan,
Ryvlin Philippe,
Cook Mark
Publication year - 2021
Publication title -
epilepsia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.687
H-Index - 191
eISSN - 1528-1167
pISSN - 0013-9580
DOI - 10.1111/epi.16555
Subject(s) - epilepsy , wearable computer , electroencephalography , computer science , wearable technology , artificial intelligence , medicine , machine learning , psychology , neuroscience , embedded system
Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applications of ML in epilepsy is seizure detection and prediction, using wearable devices (WDs). However, not all currently available algorithms implemented in WDs are using ML. In this review, we summarize the state of the art of using WDs and ML in epilepsy, and we outline future development in these domains. There is published evidence for reliable detection of epileptic seizures using implanted electroencephalography (EEG) electrodes and wearable, non‐EEG devices. Application of ML using the data recorded with WDs from a large number of patients could change radically the way we diagnose and manage patients with epilepsy.