Name
Title | ||
|---|---|---|
Low-Power Hardware Implementation of a Support Vector Machine Training and Classification for Neural Seizure Detection. |
Abstract | ||
|---|---|---|
In this paper, a low power support vector machine (SVM) training, feature extraction, and classification algorithm are hardware implemented in a neural seizure detection application. The training algorithm used is the sequential minimal optimization (SMO) algorithm. The system is implemented on different platforms: such as field programmable gate array (FPGA), Xilinx Virtex-7 and application speci... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TBCAS.2019.2947044 | IEEE Transactions on Biomedical Circuits and Systems |
Keywords | Field | DocType |
Support vector machines,Training,Feature extraction,Hardware,Electroencephalography,Optimization,Field programmable gate arrays | Kernel (linear algebra),Computer science,Support vector machine,Field-programmable gate array,CMOS,Feature extraction,Application-specific integrated circuit,Computer hardware,Classifier (linguistics),Sequential minimal optimization | Journal |
Volume | Issue | ISSN |
13 | 6 | 1932-4545 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heba Elhosary | 1 | 0 | 0.34 |
| Michael H Zakhari | 2 | 0 | 0.34 |
| Mohamed A ElGammal | 3 | 0 | 0.34 |
| Mohamed Abd Elghany | 4 | 0 | 0.34 |
| Khaled N. Salama | 5 | 345 | 46.11 |
| Hassan Mostafa | 6 | 116 | 51.49 |