Evaluation of an Activity Tracker to Detect Seizures Using Machine Learning. (November 2020)

• Record Type: Journal Article
• 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.
• Is Part Of: Journal of child neurology. Volume 35:Number 13(2020:Dec.)
• Page Start: 873
• Page End: 878
• Publication Date: 2020-11
• Subjects: seizure -- detection -- algorithm -- epilepsy -- activity; Nervous system -- Diseases -- Periodicals; 618.928
• DOI: 10.1177/0883073820937515
• Languages: English
• ISSNs: 0883-0738

Abstract

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.