Evaluation of an Activity Tracker to Detect Seizures Using Machine Learning

• Published in: Journal of child neurology 2020-11, Vol.35 (13), p.873-878
• Main Authors: Mittlesteadt, Jackson, Bambach, Sven, Dawes, Alex, Wentzel, Evelynne, Debs, Andrea, Sezgin, Emre, Digby, Dan, Huang, Yungui, Ganger, Andrea, Bhatnagar, Shivani, Ehrenberg, Lori, Nunley, Sunjay, Glynn, Peter, Lin, Simon, Rust, Steve, Patel, Anup D.
• Format: Article
• Language: English
• ISSN: 0883-0738; 1708-8283
• DOI: 10.1177/0883073820937515

Currently, the tracking of seizures is highly subjective, dependent on qualitative information provided by the patient and family instead of quantifiable seizure data. Usage of a seizure detection device to potentially detect seizure events in a population of epilepsy patients has been previously done. Therefore, we chose the Fitbit Charge 2 smart watch to determine if it could detect seizure events in patients when compared to continuous electroencephalographic (EEG) monitoring for those admitted to an epilepsy monitoring unit. A total of 40 patients were enrolled in the study that met the criteria between 2015 and 2016. All seizure types were recorded. Twelve patients had a total of 53 epileptic seizures. The patient-aggregated receiver operating characteristic curve had an area under the curve of 0.58 [0.56, 0.60], indicating that the neural network models were generally able to detect seizure events at an above-chance level. However, the overall low specificity implied a false alarm rate that would likely make the model unsuitable in practice. Overall, the use of the Fitbit Charge 2 activity tracker does not appear well suited in its current form to detect epileptic seizures in patients with seizure activity when compared to data recorded from the continuous EEG.