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

FRONTIERS IN NEUROLOGY | FRONTIERS MEDIA SA | Published : 2021

Abstract

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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Grants

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.