Seizures detection using multimodal signals: a scoping review. (29th July 2022)

• Record Type: Journal Article
• Authors: Chen, Fangyi; Chen, Ina; Zafar, Muhammad; Sinha, Saurabh R; Hu, Xiao
• Is Part Of: Physiological measurement. Volume 43:Number 7(2022)
• Publication Date: 2022-07-29
• Subjects: seizure -- epilepsy -- ML -- wearable -- sensors -- detection -- prediction; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/1361-6579/ac7a8d
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: Introduction . Epileptic seizures are common neurological disorders in the world, impacting 65 million people globally. Around 30% of patients with seizures suffer from refractory epilepsy, where seizures are not controlled by medications. The unpredictability of seizures makes it essential to have a continuous seizure monitoring system outside clinical settings for the purpose of minimizing patients' injuries and providing additional pathways for evaluation and treatment follow-up. Autonomic changes related to seizure events have been extensively studied and attempts made to apply them for seizure detection and prediction tasks. This scoping review aims to depict current research activities associated with the implementation of portable, wearable devices for seizure detection or prediction and inform future direction in continuous seizure tracking in ambulatory settings. Methods . Overall methodology framework includes 5 essential stages: research questions identification, relevant studies identification, selection of studies, data charting and summarizing the findings. A systematic searching strategy guided by systematic reviews and meta-analysis (PRISMA) was implemented to identify relevant records on two databases (PubMed, IEEE). Results . A total of 30 articles were included in our final analysis. Most of the studies were conducted off-line and employed consumer-graded wearable device. ACM is the dominant modality to be used in seizure detection, and widely deployed algorithms entail Support Vector Machine, Random Forest and threshold-based approach. The sensitivity ranged from 33.2% to 100% for single modality with a false alarm rate (FAR) ranging from 0.096 to 14.8 d −1 . Multimodality has a sensitivity ranging from 51% to 100% with FAR ranging from 0.12 to 17.7 d −1 . Conclusion . The overall performance in seizure detection system based on non-cerebral physiological signals is promising, especially for the detection of motor seizures and seizures accompanied with intense ictal autonomic changes.