Forecasting Seizure Likelihood With Wearable Technology
AuthorStirling, RE; Grayden, DB; D'Souza, W; Cook, MJ; Nurse, E; Freestone, DR; Payne, DE; Brinkmann, BH; Pal Attia, T; Viana, PF; ...
Source TitleFrontiers in Neurology
PublisherFRONTIERS MEDIA SA
Medicine (St Vincent's)
Document TypeJournal Article
CitationsStirling, R. E., Grayden, D. B., D'Souza, W., Cook, M. J., Nurse, E., Freestone, D. R., Payne, D. E., Brinkmann, B. H., Pal Attia, T., Viana, P. F., Richardson, M. P. & Karoly, P. J. (2021). Forecasting Seizure Likelihood With Wearable Technology. FRONTIERS IN NEUROLOGY, 12, https://doi.org/10.3389/fneur.2021.704060.
Access StatusOpen Access
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 tracked participants' (n = 11) heart rates, sleep, and step counts using wearable smartwatches and seizure occurrence using smartphone seizure diaries for at least 6 months (mean = 14.6 months, SD = 3.8 months). Eligible participants had a diagnosis of refractory epilepsy and reported at least 20 seizures (mean = 135, SD = 123) during the recording period. An ensembled machine learning and neural network model estimated seizure risk either daily or hourly, with retraining occurring on a weekly basis as additional data was collected. Performance was evaluated retrospectively against a rate-matched random forecast using the area under the receiver operating curve. A pseudo-prospective evaluation was also conducted on a held-out dataset. Of the 11 participants, seizures were predicted above chance in all (100%) participants using an hourly forecast and in ten (91%) participants using a daily forecast. The average time spent in high risk (prediction time) before a seizure occurred was 37 min in the hourly forecast and 3 days in the daily forecast. Cyclic features added the most predictive value to the forecasts, particularly circadian and multiday heart rate cycles. Wearable devices can be used to produce patient-specific seizure forecasts, particularly when biomarkers of seizure and epileptic activity cycles are utilized.
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