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

Research output: Contribution to journalArticlepeer-review

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

Original languageEnglish
Article number21935
JournalScientific Reports
Issue number1
Early online date9 Nov 2021
E-pub ahead of print9 Nov 2021
PublishedDec 2021

Bibliographical note

Funding Information: This study was supported by the Epilepsy Foundation of America’s Epilepsy Innovation Institute “My Seizure Gauge”, and by the Mayo Neurology Artificial Intelligence program. The authors acknowledge technical and administrative support from Sherry Klingerman CCRP. Publisher Copyright: © 2021, The Author(s).

King's Authors


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.

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