| Literature DB >> 32712958 |
Sándor Beniczky1,2,3, Philippa Karoly4, Ewan Nurse4, Philippe Ryvlin5, Mark Cook4.
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.Entities:
Keywords: epilepsy; machine learning; seizure detection; seizure prediction; wearable devices
Year: 2020 PMID: 32712958 DOI: 10.1111/epi.16555
Source DB: PubMed Journal: Epilepsia ISSN: 0013-9580 Impact factor: 5.864