QuPWM: Feature Extraction Method for Epileptic Spike Classification

dc.contributor.authorChahid, Abderrazak
dc.contributor.authorAlbalawi, Fahad
dc.contributor.authorAlotaiby, Turky
dc.contributor.authorAl-Hameed, Majed Hamad
dc.contributor.authorAlshebeili, Saleh A.
dc.contributor.authorLaleg-Kirati, Taous-Meriem
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentEstimation, Modeling and ANalysis Group
dc.contributor.institutionElectrical Engineering, Taif University, 125895 Taif, Makkah Saudi Arabia 21955
dc.contributor.institutionThe National Center for Artificial Intelligence and Big Data Technology, King Abdulaziz City for Science And Technology, 83527 Riyadh, Al Riyadh Province Saudi Arabia 11442
dc.contributor.institutionNational Institute of Neuroscience, King Fahad Medical City, Riyadh, riyadh Saudi Arabia
dc.contributor.institutionKing Saud University College of Engineering, 48137 Riyadh Saudi Arabia
dc.date.accessioned2020-02-12T12:48:02Z
dc.date.available2020-02-12T12:48:02Z
dc.date.issued2020-02-07
dc.date.published-online2020-02-07
dc.date.published-print2020-10
dc.description.abstractEpilepsy is a neurological disorder ranked as the second most serious neurological disease known to humanity, after stroke. Inter-ictal spiking is an abnormal neuronal discharge after an epileptic seizure. This abnormal activity can originate from one or more cranial lobes, often travels from one lobe to another, and interferes with normal activity from the affected lobe. The common practice for Inter-ictal spike detection of brain signals is via visual scanning of the recordings, which is a subjective and a very time-consuming task. Motivated by that, this paper focuses on using machine learning for epileptic spikes classification in magnetoencephalography (MEG) signals. First, we used the Position Weight Matrix (PWM) method combined with a uniform quantizer to generate useful features from time domain and frequency domain through a Fast Fourier Transform (FFT) of the framed raw MEG signals. Second, the extracted features are fed to standard classifiers for inter-ictel spikes classification. The proposed technique shows great potential in spike classification and reducing the feature vector size. Specifically, the proposed technique achieved average sensitivity up to 87% and specificity up to 97% using 5-folds cross-validation applied to a balanced dataset. These samples are extracted from nine epileptic subjects using a sliding frame of size 95 sample points with a step-size of 8 sample-points.
dc.eprint.versionPost-print
dc.identifier.arxivid1907.02596
dc.identifier.citationChahid, A., Albalawi, F., Alotaiby, T., Al-Hameed, M. H., Alshebeili, S. A., & Laleg-Kirati, T.-M. (2020). QuPWM: Feature Extraction Method for Epileptic Spike Classification. IEEE Journal of Biomedical and Health Informatics, 1–1. doi:10.1109/jbhi.2020.2972286
dc.identifier.doi10.1109/JBHI.2020.2972286
dc.identifier.journalIEEE Journal of Biomedical and Health Informatics
dc.identifier.urihttp://hdl.handle.net/10754/661489
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.issupplementedbyURL:https://github.com/EMANG-KAUST/QuPWM
dc.relation.issupplementedbygithub:EMANG-KAUST/QuPWM
dc.relation.urlhttps://ieeexplore.ieee.org/document/8986627/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8986627
dc.rights(c) 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectmagnetoencephalography (MEG)
dc.subjectPosition Weight Matrix (PWM)
dc.subjectEpileptic spike detection
dc.subjectmachine learning
dc.titleQuPWM: Feature Extraction Method for Epileptic Spike Classification
dc.typeArticle
display.details.left<span><h5>Type</h5>Article<br><br><h5>Authors</h5><a href="https://repository.kaust.edu.sa/search?query=orcid.id:0000-0003-4342-8341&spc.sf=dc.date.issued&spc.sd=DESC">Chahid, Abderrazak</a> <a href="https://orcid.org/0000-0003-4342-8341" target="_blank"><img src="https://repository.kaust.edu.sa/server/api/core/bitstreams/82a625b4-ed4b-40c8-865a-d6a5225a26a4/content" width="16" height="16"/></a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Albalawi, Fahad,equals">Albalawi, Fahad</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Alotaiby, Turky,equals">Alotaiby, Turky</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Al-Hameed, Majed Hamad,equals">Al-Hameed, Majed Hamad</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Alshebeili, Saleh A.,equals">Alshebeili, Saleh A.</a><br><a href="https://repository.kaust.edu.sa/search?query=orcid.id:0000-0001-5944-0121&spc.sf=dc.date.issued&spc.sd=DESC">Laleg-Kirati, Taous-Meriem</a> <a href="https://orcid.org/0000-0001-5944-0121" target="_blank"><img src="https://repository.kaust.edu.sa/server/api/core/bitstreams/82a625b4-ed4b-40c8-865a-d6a5225a26a4/content" width="16" height="16"/></a><br><br><h5>KAUST Department</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Computational Bioscience Research Center (CBRC),equals">Computational Bioscience Research Center (CBRC)</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division,equals">Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Electrical Engineering Program,equals">Electrical Engineering Program</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Estimation, Modeling and ANalysis Group,equals">Estimation, Modeling and ANalysis Group</a><br><br><h5>Online Publication Date</h5>2020-02-07<br><br><h5>Print Publication Date</h5>2020-10<br><br><h5>Date</h5>2020-02-07</span>
display.details.right<span><h5>Abstract</h5>Epilepsy is a neurological disorder ranked as the second most serious neurological disease known to humanity, after stroke. Inter-ictal spiking is an abnormal neuronal discharge after an epileptic seizure. This abnormal activity can originate from one or more cranial lobes, often travels from one lobe to another, and interferes with normal activity from the affected lobe. The common practice for Inter-ictal spike detection of brain signals is via visual scanning of the recordings, which is a subjective and a very time-consuming task. Motivated by that, this paper focuses on using machine learning for epileptic spikes classification in magnetoencephalography (MEG) signals. First, we used the Position Weight Matrix (PWM) method combined with a uniform quantizer to generate useful features from time domain and frequency domain through a Fast Fourier Transform (FFT) of the framed raw MEG signals. Second, the extracted features are fed to standard classifiers for inter-ictel spikes classification. The proposed technique shows great potential in spike classification and reducing the feature vector size. Specifically, the proposed technique achieved average sensitivity up to 87% and specificity up to 97% using 5-folds cross-validation applied to a balanced dataset. These samples are extracted from nine epileptic subjects using a sliding frame of size 95 sample points with a step-size of 8 sample-points.<br><br><h5>Citation</h5>Chahid, A., Albalawi, F., Alotaiby, T., Al-Hameed, M. H., Alshebeili, S. A., & Laleg-Kirati, T.-M. (2020). QuPWM: Feature Extraction Method for Epileptic Spike Classification. IEEE Journal of Biomedical and Health Informatics, 1–1. doi:10.1109/jbhi.2020.2972286<br><br><h5>Publisher</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.publisher=Institute of Electrical and Electronics Engineers (IEEE),equals">Institute of Electrical and Electronics Engineers (IEEE)</a><br><br><h5>Journal</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.journal=IEEE Journal of Biomedical and Health Informatics,equals">IEEE Journal of Biomedical and Health Informatics</a><br><br><h5>DOI</h5><a href="https://doi.org/10.1109/JBHI.2020.2972286">10.1109/JBHI.2020.2972286</a><br><br><h5>arXiv</h5><a href="https://arxiv.org/abs/1907.02596">1907.02596</a><br><br><h5>Additional Links</h5>https://ieeexplore.ieee.org/document/8986627/<br>https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8986627<br><br><h5>Relations</h5><b>Is Supplemented By:</b><br/> <ul><li><i>[Software]</i> <br/> Title: EMANG-KAUST/QuPWM:. Publication Date: 2019-01-28. github: <a href="https://github.com/EMANG-KAUST/QuPWM" >EMANG-KAUST/QuPWM</a> Handle: <a href="http://hdl.handle.net/10754/667145" >10754/667145</a></a></li></ul></span>
display.relations<b>Is Supplemented By:</b><br/> <ul><li><i>[Software]</i> <br/> Title: EMANG-KAUST/QuPWM:. Publication Date: 2019-01-28. github: <a href="https://github.com/EMANG-KAUST/QuPWM" >EMANG-KAUST/QuPWM</a> Handle: <a href="http://hdl.handle.net/10754/667145" >10754/667145</a></a></li></ul>
kaust.personChahid, Abderrazak
kaust.personLaleg-Kirati, Taous-Meriem
orcid.authorChahid, Abderrazak::0000-0003-4342-8341
orcid.authorAlbalawi, Fahad
orcid.authorAlotaiby, Turky
orcid.authorAl-Hameed, Majed Hamad
orcid.authorAlshebeili, Saleh A.
orcid.authorLaleg-Kirati, Taous-Meriem::0000-0001-5944-0121
orcid.id0000-0001-5944-0121
orcid.id0000-0003-4342-8341
refterms.dateFOA2021-01-31T06:14:18Z
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