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 -
