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Machine learning and wearable devices of the future.
Journal article

Machine learning and wearable devices of the future.

  • Beniczky S Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark.
  • Karoly P The Graeme Clark Institute, The University of Melbourne, Melbourne, Vic., Australia.
  • Nurse E The Graeme Clark Institute, The University of Melbourne, Melbourne, Vic., Australia.
  • Ryvlin P Department of Clinical Neurosciences, CHUV, Lausanne, Switzerland.
  • Cook M The Graeme Clark Institute, The University of Melbourne, Melbourne, Vic., Australia.
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  • 2020-07-27
Published in:
  • Epilepsia. - 2020
epilepsy
machine learning
seizure detection
seizure prediction
wearable devices
English 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.
Language
  • English
Open access status
closed
Identifiers
Persistent URL
https://folia.unifr.ch/global/documents/149302
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