Cardiac arrhythmia classification based on multi-features and support vector machines.
- Author:
Yong ZHAO
1
;
Wenxue HONG
;
Shibo SUN
Author Information
1. College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China. ertiger@sina.com
- Publication Type:Journal Article
- MeSH:
Algorithms;
Area Under Curve;
Arrhythmias, Cardiac;
classification;
diagnosis;
Electrocardiography;
methods;
Humans;
Principal Component Analysis;
ROC Curve;
Signal Processing, Computer-Assisted;
Support Vector Machine
- From:
Journal of Biomedical Engineering
2011;28(2):292-295
- CountryChina
- Language:Chinese
-
Abstract:
To solve the problem of cardiac arrhythmias classification, we proposed a novel algorithm based on the multi-feature fusion and support vector machines (SVM). Kernel independent component analysis (KICA) was used to extract nonlinear features and wavelet transform (WT) was used to extract time-frequency features. Combining these features could include more information about the disease. We designed the classification model based on SVM combined with error correcting output codes (ECOC). Receiver operating characteristic curve (ROC) and Area Under the ROC curve (AUC) value were used to assess the classification model. The value of AUC is 0.956 against MIT-BIH arrhythmia database. Experimental results showed effectiveness of the proposed method.
