Evaluation of an Activity Tracker to Detect Seizures Using Machine Learning

被引：4

作者：

Mittlesteadt, Jackson ^{[1
]}

Bambach, Sven ^{[2
]}

Dawes, Alex ^{[3
]}

Wentzel, Evelynne ^{[2
]}

Debs, Andrea ^{[2
]}

Sezgin, Emre ^{[2
]}

Digby, Dan ^{[2
]}

Huang, Yungui ^{[2
]}

Ganger, Andrea ^{[4
]}

Bhatnagar, Shivani ^{[4
]}

Ehrenberg, Lori ^{[4
]}

Nunley, Sunjay ^{[5
,6
]}

Glynn, Peter ^{[4
]}

Lin, Simon ^{[2
]}

Rust, Steve ^{[2
]}

Patel, Anup D. ^{[4
]}

机构：

[1] Univ Notre Dame, South Bend, IN USA

[2] Nationwide Childrens Hosp, Abigail Wexner Res Inst, Columbus, OH 43205 USA

[3] Ohio State Univ, Columbus, OH 43210 USA

[4] Nationwide Childrens Hosp, Div Neurol, Columbus, OH 43205 USA

[5] Prisma Hlth Childrens Hosp, Greenville, SC USA

[6] Univ South Carolina, Sch Med, Greenville, SC USA

来源：

JOURNAL OF CHILD NEUROLOGY | 2020年 / 35卷 / 13期

关键词：

seizure; detection; algorithm; epilepsy; activity; OPERATIONAL CLASSIFICATION; ILAE COMMISSION; POSITION PAPER; EPILEPSY; VALIDATION; DEVICES;

D O I：

10.1177/0883073820937515

中图分类号：

R74 [神经病学与精神病学];

学科分类号：

摘要：

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.

引用

页码：873 / 878

页数：6