Please use this identifier to cite or link to this item: https://hdl.handle.net/11681/42562
Title: Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor
Authors: Lever, J. H.
Taylor, Susan
Ray, Laura E.
Hamlin, Alexandra
Kobylarz, Erik
Keywords: Biomedical computing
Decision-support systems
Wearable sensors
Machine learning
Biomedical signal processing
Publisher: Cold Regions Research and Engineering Laboratory (U.S.)
Engineer Research and Development Center (U.S.)
Series/Report no.: Miscellaneous Paper (Engineer Research and Development Center (U.S.)) ; no. ERDC/CRREL MP-21-30
Is Version Of: Hamlin, Alexandra, Erik Kobylarz, James H. Lever, Susan Taylor, and Laura Ray. "Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor data." Computers in Biology and Medicine 130 (2021): 104232. https://doi.org/10.1016/j.compbiomed.2021.104232
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.
Description: Miscellaneous Paper
Gov't Doc #: ERDC/CRREL MP-21-30
Rights: Approved for Public Release; Distribution is Unlimited
URI: https://hdl.handle.net/11681/42562
http://dx.doi.org/10.21079/11681/42562
Appears in Collections:Miscellaneous Paper

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