Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/MWSCAS.2019.8884989 | Midwest Symposium on Circuits and Systems Conference Proceedings |
Keywords | Field | DocType |
support vector machine (SVM),artificial neural network (ANN),neural seizure detection | Seizure detection,Read-only memory,Gradient descent,Pattern recognition,Computer science,Support vector machine,Feature extraction,Electronic engineering,Software,Artificial intelligence,Artificial neural network,Energy consumption | Conference |
ISSN | Citations | PageRank |
1548-3746 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamed A. Elgammal | 1 | 0 | 1.01 |
| Hassan Mostafa | 2 | 116 | 51.49 |
| Khaled N. Salama | 3 | 345 | 46.11 |
| Ahmed Nader Mohieldin | 4 | 0 | 1.01 |