A Comparison of Artificial Neural Network(ANN) and Support Vector Machine(SVM) Classifiers for Neural Seizure Detection

dc.conference.date4-7 Aug. 2019
dc.conference.locationDallas, TX, USA
dc.conference.name2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS)
dc.contributor.authorElGammal, Mohamed A.
dc.contributor.authorMostafa, Hassan
dc.contributor.authorSalama, Khaled N.
dc.contributor.authorMohieldin, Ahmed Nader
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.institutionElectronics and Communications Engineering Department, Cairo University, Gize, 12613, Egypt
dc.date.accessioned2019-12-08T08:58:42Z
dc.date.available2019-12-08T08:58:42Z
dc.date.issued2019-10-31
dc.date.published-online2019-10-31
dc.date.published-print2019-08
dc.description.abstractIn this paper, two different classifiers are software and hardware implemented for neural seizure detection. The two techniques are support vector machine(SVM) and artificial neural networks(ANN). The two techniques are pretrained on software and only the classifiers are hardware implemented and tested. A comparison of the two techniques is performed on the levels of performance, energy consumption and area. The SVM is pretrained using gradient ascent (GA) algorithm, while the neural network is implemented with single hidden layer. It is found that the ANN consumes more power than the SVM by a factor of 4 with almost the same performance. However, the ANN finishes classification in much less number of clock cycles than the SVM by a factor of 34.
dc.description.sponsorshipThis work was partially funded by ONE Lab at Zewail City of Science and Technology and Cairo University, NTRA, ITIDA, ASRT, Mentor Graphics, NSERC.
dc.eprint.versionPost-print
dc.identifier.citationElgammal, M. A., Mostafa, H., Salama, K. N., & Nader Mohieldin, A. (2019). A Comparison of Artificial Neural Network(ANN) and Support Vector Machine(SVM) Classifiers for Neural Seizure Detection. 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS). doi:10.1109/mwscas.2019.8884989
dc.identifier.doi10.1109/MWSCAS.2019.8884989
dc.identifier.urihttp://hdl.handle.net/10754/660444
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8884989/
dc.relation.urlhttps://ieeexplore.ieee.org/document/8884989/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8884989
dc.rightsArchived with thanks to IEEE
dc.subjectsupport vector machine (SVM)
dc.subjectartificial neural network (ANN)
dc.subjectneural seizure detection
dc.titleA Comparison of Artificial Neural Network(ANN) and Support Vector Machine(SVM) Classifiers for Neural Seizure Detection
dc.typeConference Paper
display.details.left<span><h5>Type</h5>Conference Paper<br><br><h5>Authors</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=ElGammal, Mohamed A.,equals">ElGammal, Mohamed A.</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Mostafa, Hassan,equals">Mostafa, Hassan</a><br><a href="https://repository.kaust.edu.sa/search?query=orcid.id:0000-0001-7742-1282&spc.sf=dc.date.issued&spc.sd=DESC">Salama, Khaled N.</a> <a href="https://orcid.org/0000-0001-7742-1282" 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=Mohieldin, Ahmed Nader,equals">Mohieldin, Ahmed Nader</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=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=Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division,equals">Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division</a><br><br><h5>Online Publication Date</h5>2019-10-31<br><br><h5>Print Publication Date</h5>2019-08<br><br><h5>Date</h5>2019-10-31</span>
display.details.right<span><h5>Abstract</h5>In this paper, two different classifiers are software and hardware implemented for neural seizure detection. The two techniques are support vector machine(SVM) and artificial neural networks(ANN). The two techniques are pretrained on software and only the classifiers are hardware implemented and tested. A comparison of the two techniques is performed on the levels of performance, energy consumption and area. The SVM is pretrained using gradient ascent (GA) algorithm, while the neural network is implemented with single hidden layer. It is found that the ANN consumes more power than the SVM by a factor of 4 with almost the same performance. However, the ANN finishes classification in much less number of clock cycles than the SVM by a factor of 34.<br><br><h5>Citation</h5>Elgammal, M. A., Mostafa, H., Salama, K. N., & Nader Mohieldin, A. (2019). A Comparison of Artificial Neural Network(ANN) and Support Vector Machine(SVM) Classifiers for Neural Seizure Detection. 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS). doi:10.1109/mwscas.2019.8884989<br><br><h5>Acknowledgements</h5>This work was partially funded by ONE Lab at Zewail City of Science and Technology and Cairo University, NTRA, ITIDA, ASRT, Mentor Graphics, NSERC.<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>Conference/Event Name</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.conference=2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS),equals">2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS)</a><br><br><h5>DOI</h5><a href="https://doi.org/10.1109/MWSCAS.2019.8884989">10.1109/MWSCAS.2019.8884989</a><br><br><h5>Additional Links</h5>https://ieeexplore.ieee.org/document/8884989/https://ieeexplore.ieee.org/document/8884989/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8884989</span>
kaust.personSalama, Khaled N.
orcid.authorElGammal, Mohamed A.
orcid.authorMostafa, Hassan
orcid.authorSalama, Khaled N.::0000-0001-7742-1282
orcid.authorMohieldin, Ahmed Nader
orcid.id0000-0001-7742-1282
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