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

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 s...

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Autores principales:	Mona Nasseri, Tal Pal Attia, Boney Joseph, Nicholas M. Gregg, Ewan S. Nurse, Pedro F. Viana, Gregory Worrell, Matthias Dümpelmann, Mark P. Richardson, Dean R. Freestone, Benjamin H. Brinkmann
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a602d4f7464843138012ca3c210ca25c
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spelling	oai:doaj.org-article:a602d4f7464843138012ca3c210ca25c2021-11-14T12:18:07ZAmbulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning10.1038/s41598-021-01449-22045-2322https://doaj.org/article/a602d4f7464843138012ca3c210ca25c2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01449-2https://doaj.org/toc/2045-2322Abstract 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.Mona NasseriTal Pal AttiaBoney JosephNicholas M. GreggEwan S. NursePedro F. VianaGregory WorrellMatthias DümpelmannMark P. RichardsonDean R. FreestoneBenjamin H. BrinkmannNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mona Nasseri Tal Pal Attia Boney Joseph Nicholas M. Gregg Ewan S. Nurse Pedro F. Viana Gregory Worrell Matthias Dümpelmann Mark P. Richardson Dean R. Freestone Benjamin H. Brinkmann Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning
description	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.
format	article
author	Mona Nasseri Tal Pal Attia Boney Joseph Nicholas M. Gregg Ewan S. Nurse Pedro F. Viana Gregory Worrell Matthias Dümpelmann Mark P. Richardson Dean R. Freestone Benjamin H. Brinkmann
author_facet	Mona Nasseri Tal Pal Attia Boney Joseph Nicholas M. Gregg Ewan S. Nurse Pedro F. Viana Gregory Worrell Matthias Dümpelmann Mark P. Richardson Dean R. Freestone Benjamin H. Brinkmann
author_sort	Mona Nasseri
title	Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning
title_short	Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning
title_full	Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning
title_fullStr	Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning
title_full_unstemmed	Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning
title_sort	ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a602d4f7464843138012ca3c210ca25c
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Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning

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