doi: 10.4304/jsw.6.5.880-886
Classification of Bio-potential Surface Electrode based on FKCM and SVM
- 1 School of Textile, Tianjin Polytechnic University, Tianjin, China
- 2 Key Laboratory for Advanced Textile Composite of Ministry of Education, Tianjin Polytechnic University, Tianjin, China
- 3 Institute of Textiles and Clothing, Hong Kong Polytechnic University, Hong Kong, China
Abstract
In this paper, a method which is used for evaluating the performance of bio-potential surface electrode (BSE) with multi-index is presented. The Fuzzy kernel C-means (FKCM) algorithm and KF statistic are employed for classifying the BSE samples and searching an optimal classification amount respectively. Subsequently, a discriminant function is constructed by support vector machines (SVM) for recognizing the new measured samples. Experimental result shows classification correction ratios of improved FKCM algorithm are 96.3% and 85% on the IRIS and BSE dataset according a priori knowledge, furthermore, the recognition correction ratios of SVM algorithm are 96.3% and 90% on the IRIS and BSE dataset.
Keywords
- FKCM
- SVM
- classification
- recognition
How to Cite
Hao Liu, Xiaoming Tao, Pengjun Xu, and Guanxiong Qiu, "Classification of Bio-potential Surface Electrode based on FKCM and SVM," Journal of Software, vol. 6, no. 5, pp. 880-886, 2011. https://doi.org/ 10.4304/jsw.6.5.880-886
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).