Machine learning and wearable devices of the future. Issue Volume 62:Issue S2(2021)Supplement (26th July 2020)

• Record Type: Journal Article
• Authors: Beniczky, Sándor; Karoly, Philippa; Nurse, Ewan; Ryvlin, Philippe; Cook, Mark
• Other Names: Nehlig Astrid guestEditor.; Wiebe Samuel guestEditor.
• Is Part Of: Epilepsia. Volume 62:Issue S2(2021)Supplement
• Page Start: S116
• Page End: S124
• Publication Date: 2020-07-26
• Subjects: epilepsy -- machine learning -- seizure detection -- seizure prediction -- wearable devices; Epilepsy -- Periodicals; 616.853
• DOI: 10.1111/epi.16555
• Languages: English
• ISSNs: 0013-9580

Abstract

Abstract: 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.