Seizure detection using wearable sensors and machine learning: Setting a benchmark. (15th July 2021)

• Record Type: Journal Article
• Authors: Tang, Jianbin; El Atrache, Rima; Yu, Shuang; Asif, Umar; Jackson, Michele; Roy, Subhrajit; Mirmomeni, Mahtab; Cantley, Sarah; Sheehan, Theodore; Schubach, Sarah; Ufongene, Claire; Vieluf, Solveig; Meisel, Christian; Harrer, Stefan; Loddenkemper, Tobias
• Is Part Of: Epilepsia. Volume 62:issue 8(2021)
• Page Start: 1807
• Page End: 1819
• Publication Date: 2021-07-15
• Subjects: deep learning -- epilepsy -- machine learning -- multisensor recordings -- wearable devices; Epilepsy -- Periodicals; 616.853
• DOI: 10.1111/epi.16967
• Languages: English
• ISSNs: 0013-9580

Abstract

Abstract: Objective: Tracking seizures is crucial for epilepsy monitoring and treatment evaluation. Current epilepsy care relies on caretaker seizure diaries, but clinical seizure monitoring may miss seizures. Wearable devices may be better tolerated and more suitable for long‐term ambulatory monitoring. This study evaluates the seizure detection performance of custom‐developed machine learning (ML) algorithms across a broad spectrum of epileptic seizures utilizing wrist‐ and ankle‐worn multisignal biosensors. Methods: We enrolled patients admitted to the epilepsy monitoring unit and asked them to wear a wearable sensor on either their wrists or ankles. The sensor recorded body temperature, electrodermal activity, accelerometry (ACC), and photoplethysmography, which provides blood volume pulse (BVP). We used electroencephalographic seizure onset and offset as determined by a board‐certified epileptologist as a standard comparison. We trained and validated ML for two different algorithms: Algorithm 1, ML methods for developing seizure type‐specific detection models for nine individual seizure types; and Algorithm 2, ML methods for building general seizure type‐agnostic detection, lumping together all seizure types. Results: We included 94 patients (57.4% female, median age = 9.9 years) and 548 epileptic seizures (11 066 h of sensor data) for a total of 930 seizures and nine seizure types. Algorithm 1 detected eight of nine seizure types better than chance (area under the receiver operating characteristic curve [AUC‐ROC] = .648–.976). Algorithm 2 detected all nine seizure types better than chance (AUC‐ROC = .642–.995); a fusion of ACC and BVP modalities achieved the best AUC‐ROC (.752) when combining all seizure types together. Significance: Automatic seizure detection using ML from multimodal wearable sensor data is feasible across a broad spectrum of epileptic seizures. Preliminary results show better than chance seizure detection. The next steps include validation of our results in larger datasets, evaluation of the detection utility tool for additional clinical seizure types, and integration of additional clinical information.