Machine learning and wearable devices of the future.

Journal: Epilepsia
Published Date:

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.

Authors

  • Sándor Beniczky
    Department of Clinical Neurophysiology, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark. Electronic address: sbz@filadelfia.dk.
  • Philippa Karoly
    IBM Research - Australia, 204 Lygon Street, 3053 Carlton, VIC, Australia; The University of Melbourne, 3010 Parkville, VIC, Australia.
  • Ewan Nurse
    IBM Research - Australia, 204 Lygon Street, 3053 Carlton, VIC, Australia; The University of Melbourne, 3010 Parkville, VIC, Australia.
  • Philippe Ryvlin
    Department of Clinical Neurosciences, CHUV, Lausanne, Switzerland.
  • Mark Cook
    The University of Melbourne, 3010 Parkville, VIC, Australia.