Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor data. (March 2021)

• Record Type: Journal Article
• Authors: Hamlin, Alexandra; Kobylarz, Erik; Lever, James H.; Taylor, Susan; Ray, Laura
• Is Part Of: Computers in biology and medicine. Volume 130(2021)
• Publication Date: 2021-03
• Subjects: Biomedical computing -- Decision-support systems -- Wearable sensors -- Machine learning -- Biomedical signal processing; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104232
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video-electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients. Graphical abstract: Image 1 Highlights: Time-domain data from non-cerebral sensors can separate seizures from other activities. Epileptic seizures are separable using features derived from non-cerebral sensors. Microphone data (vocalizations during seizures) are important for separating seizure from non-seizure data. The study shows that the most important features derived from non-cerebral sensors vary among study patients.