Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning.

Journal: Scientific reports
PMID: 34754043

Abstract

The ability to forecast seizures minutes to hours in advance of an event has been verified using invasive EEG devices, but has not been previously demonstrated using noninvasive wearable devices over long durations in an ambulatory setting. In this study we developed a seizure forecasting system with a long short-term memory (LSTM) recurrent neural network (RNN) algorithm, using a noninvasive wrist-worn research-grade physiological sensor device, and tested the system in patients with epilepsy in the field, with concurrent invasive EEG confirmation of seizures via an implanted recording device. The system achieved forecasting performance significantly better than a random predictor for 5 of 6 patients studied, with mean AUC-ROC of 0.80 (range 0.72-0.92). These results provide the first clear evidence that direct seizure forecasts are possible using wearable devices in the ambulatory setting for many patients with epilepsy.

Authors

  • Mona Nasseri
    Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA.
  • Tal Pal Attia
    Departments of Neurology and Biomedical Engineering, Mayo Foundation, Rochester, MN, USA.
  • Boney Joseph
    Departments of Neurology and Biomedical Engineering, Mayo Foundation, Rochester, MN, USA.
  • Nicholas M Gregg
    Departments of Neurology and Biomedical Engineering, Mayo Foundation, Rochester, MN, USA.
  • Ewan S Nurse
    NeuroEngineering Laboratory, Department of Electrical & Electronic Engineering, The University of Melbourne, Melbourne, VIC, Australia, 3010; Centre for Neural Engineering, The University of Melbourne, Melbourne, VIC, Australia, 3010.
  • Pedro F Viana
    Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  • Gregory Worrell
  • Matthias Dumpelmann
  • Mark P Richardson
    Department of Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  • Dean R Freestone
    NeuroEngineering Laboratory, Department of Electrical & Electronic Engineering, The University of Melbourne, Melbourne, VIC, Australia, 3010; Centre for Neural Engineering, The University of Melbourne, Melbourne, VIC, Australia, 3010; Department of Medicine St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia, 3065; Department of Statistics, Columbia University, New York, New York, USA, 10027.
  • Benjamin H Brinkmann
    Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA.

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