Name
Title | ||
|---|---|---|
Recognition of Seizure and Nonseizure EEG Signals Using a Transfer Support Vector Machine |
Abstract | ||
|---|---|---|
Automatic recognition of seizure and nonseizure EEG Signals is an important means for epilepsy detection. In this study, a transfer-learning-based support vector machine (TrSVM) method is proposed for epileptic electroencephalogram recognition. The proposed transfer SVM model aims to significantly improve the recognition performance using the transductive transfer learning mechanism. In this study, we mainly integrate a large-margin-projected mechanism into a classical SVM model, which can be utilized to resist the loss of performances caused by the differences between data distributions. The experimental results indicate that the TrSVM method obtains promising results compared with those of related non-transfer and transfer methods for epileptic EEG recognition. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1166/jmihi.2019.2740 | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS |
Keywords | DocType | Volume |
Epileptic EEG Recognition,Transfer Learning,Transductive Transfer Learning,Support Vector Machine,Large-Margin-Projected Mechanism | Journal | 9 |
Issue | ISSN | Citations |
7 | 2156-7018 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenyu Ping | 1 | 0 | 0.34 |
| Li Liu | 2 | 1 | 1.36 |
| Yun Gao | 3 | 0 | 0.34 |
| Liang Kuang | 4 | 0 | 1.01 |