Machine learning and wearable devices of the future

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review


  • Sándor Beniczky
  • Philippa Karoly, University of Melbourne
  • ,
  • Ewan Nurse, University of Melbourne
  • ,
  • Philippe Ryvlin, University of Lausanne
  • ,
  • Mark Cook, University of Melbourne

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.

Sider (fra-til)S116-S124
Antal sider9
StatusUdgivet - mar. 2021

Bibliografisk note

Funding Information:
The research was supported by the Juhl Family Foundation. EN is funded by the ‘My Seizure Gauge' grant provided by the Epilepsy Innovation Institute, a research program of the Epilepsy Foundation of America.

Publisher Copyright:
© 2020 International League Against Epilepsy

Copyright 2021 Elsevier B.V., All rights reserved.

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