Journal article

Forecasting Seizure Likelihood With Wearable Technology

Rachel E Stirling, David B Grayden, Wendyl D'Souza, Mark J Cook, Ewan Nurse, Dean R Freestone, Daniel E Payne, Benjamin H Brinkmann, Tal Pal Attia, Pedro F Viana, Mark P Richardson, Philippa J Karoly



The unpredictability of epileptic seizures exposes people with epilepsy to potential physical harm, restricts day-to-day activities, and impacts mental well-being. Accurate seizure forecasters would reduce the uncertainty associated with seizures but need to be feasible and accessible in the long-term. Wearable devices are perfect candidates to develop non-invasive, accessible forecasts but are yet to be investigated in long-term studies. We hypothesized that machine learning models could utilize heart rate as a biomarker for well-established cycles of seizures and epileptic activity, in addition to other wearable signals, to forecast high and low risk seizure periods. This feasibility study..

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Awarded by NHMRC Investigator Grant

Funding Acknowledgements

This research was funded by an NHMRC Investigator Grant 1178220, the Epilepsy Foundation of America's My Seizure Gauge grant and the BioMedTech Horizons 3 program, an initiative of MTPConnect. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.