Real-time epileptic seizure detection during sleep using passive infrared sensors

Simple item page
buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage6476en_US
dc.citation.issueNumber15en_US
dc.citation.spage6467en_US
dc.citation.volumeNumber19en_US
dc.contributor.authorHanosh, O.en_US
dc.contributor.authorAnsari, R.en_US
dc.contributor.authorYounis, K.en_US
dc.contributor.authorÇetin, A. Enisen_US
dc.date.accessioned2020-02-06T07:01:45Z
dc.date.available2020-02-06T07:01:45Z
dc.date.issued2019
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractThis paper addresses the problem of detecting epileptic seizures experienced by a human subject during sleep. Commonly used solutions to this problem mostly rely on detecting motion due to seizures using contact-based sensors or video-based sensors. We seek a low-cost, low-power alternative that can sense motion without making direct contact with the subject and provides high detection accuracy. We investigate the use of Passive InfraRed (PIR) sensors to sense human body motion caused by epileptic seizures during sleep which makes the body shake and causes the PIR sensor to generate an oscillatory output signal. This signal can be distinguished from that of ordinary motions during sleep using analysis with machine learning algorithms. The supervised hidden Markov model algorithm (HMM) and a 1-D and 2-D convolutional neural network (ConvNet) are used to classify the data set of the PIR sensor output into the occurrence of epileptic seizures, ordinary motions, or absence of motion. The method was tested on the PIR signals captured at 1 m from 33 recruited healthy subjects who, after watching seizure videos, either moved their body on a bed to simulate a seizure, ordinary motion, or lay still. The HMM algorithm attained 97.03% accuracy, while 1D-ConvNet and 2D-ConvNet attained an accuracy of 96.97% and 98.98%, respectively. All simulated seizures were successfully detected, with errors occurring only in distinguishing between ordinary motion and no motion, thereby demonstrating the potential for using PIR sensors in the epileptic seizure detection.en_US
dc.identifier.doi10.1109/JSEN.2019.2907664en_US
dc.identifier.eissn1558-1748
dc.identifier.issn1530-437X
dc.identifier.urihttp://hdl.handle.net/11693/53112
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.isversionofhttps://dx.doi.org/10.1109/JSEN.2019.2907664en_US
dc.source.titleIEEE Sensors Journalen_US
dc.subjectConvNeten_US
dc.subjectEpileptic seizureen_US
dc.subjectHidden Markov Model (HMM)en_US
dc.subjectMax-poolingen_US
dc.subjectPIR sensoren_US
dc.subjectSensing unit (SU)en_US
dc.titleReal-time epileptic seizure detection during sleep using passive infrared sensorsen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Real_time_epileptic_seizure_detection_during_sleep_using_passive_infrared_sensors.pdf
Size:
2.5 MB
Format:
Adobe Portable Document Format
Description:
View / Download
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Department of Electrical and Electronics Engineering