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 |
Files in This Item:
File | Description | Size | Format | |
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ERDC-CRREL MP-21-30.pdf | 1.24 MB | Adobe PDF | View/Open |