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

Mohamed A. ElGammal; Hassan Mostafa; Khaled N. Salama; Ahmed Nader Mohieldin

doi:10.1109/MWSCAS.2019.8884989

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

Mohamed A. ElGammal, Hassan Mostafa, Khaled N. Salama, Ahmed Nader Mohieldin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS)
Publisher	IEEE
Pages	646-649
Number of pages	4
ISBN (Print)	9781728127880
DOIs	https://doi.org/10.1109/MWSCAS.2019.8884989
State	Published - Oct 31 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was partially funded by ONE Lab at Zewail City of Science and Technology and Cairo University, NTRA, ITIDA, ASRT, Mentor Graphics, NSERC.

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10.1109/MWSCAS.2019.8884989

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@inproceedings{858c3f17c0b048daa63b0a44c4b1c0e4,

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

abstract = "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.",

author = "ElGammal, {Mohamed A.} and Hassan Mostafa and Salama, {Khaled N.} and Mohieldin, {Ahmed Nader}",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This work was partially funded by ONE Lab at Zewail City of Science and Technology and Cairo University, NTRA, ITIDA, ASRT, Mentor Graphics, NSERC.",

year = "2019",

month = oct,

day = "31",

doi = "10.1109/MWSCAS.2019.8884989",

language = "English (US)",

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pages = "646--649",

booktitle = "2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS)",

publisher = "IEEE",

}

A Comparison of Artificial Neural Network(ANN) and Support Vector Machine(SVM) Classifiers for Neural Seizure Detection. / ElGammal, Mohamed A.; Mostafa, Hassan; Salama, Khaled N. et al.
2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2019. p. 646-649.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A Comparison of Artificial Neural Network(ANN) and Support Vector Machine(SVM) Classifiers for Neural Seizure Detection

AU - ElGammal, Mohamed A.

AU - Mostafa, Hassan

AU - Salama, Khaled N.

AU - Mohieldin, Ahmed Nader

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This work was partially funded by ONE Lab at Zewail City of Science and Technology and Cairo University, NTRA, ITIDA, ASRT, Mentor Graphics, NSERC.

PY - 2019/10/31

Y1 - 2019/10/31

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/660444

UR - https://ieeexplore.ieee.org/document/8884989/

UR - http://www.scopus.com/inward/record.url?scp=85074984108&partnerID=8YFLogxK

U2 - 10.1109/MWSCAS.2019.8884989

DO - 10.1109/MWSCAS.2019.8884989

M3 - Conference contribution

SN - 9781728127880

SP - 646

EP - 649

BT - 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS)

PB - IEEE

ER -

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

Abstract

Bibliographical note

UN SDGs

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