Zhao Yong, Hong Wenxue, Sun Shibo
College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2011 Apr;28(2):292-5.
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.
为了解决心律失常分类问题，我们提出了一种基于多特征融合和支持向量机（SVM）的新算法。采用核独立成分分析（KICA）提取非线性特征，采用小波变换（WT）提取时频特征。融合这些特征可以包含更多关于该疾病的信息。我们设计了基于SVM并结合纠错输出码（ECOC）的分类模型。使用受试者工作特征曲线（ROC）和ROC曲线下面积（AUC）值来评估分类模型。针对麻省理工学院-贝斯以色列女执事医疗中心心律失常数据库，AUC值为0.956。实验结果表明了该方法的有效性。
