Arrhythmia Recognition Based on EMD and Support Vector Machines

摘要：

According to the non-stationary feature of ECG signal, a new classification method of arrhythmia is introduced. This method combines empirical mode decomposition (EMD) with singular value decomposition (SVD), using support vector machines (SVM) for classifying. First, ECG signal is decomposed into a set of intrinsic mode function (IMF) using empirical mode decomposition method. The initial feature vector matrix is formed by these IMFs. Then, the initial feature vector matrix is decomposed using singular value decomposition, and singular values of the matrix can be calculated. Singular values are regarded as the feature vector of ECG signal, support vector machines used as classifiers are established to identify the condition of arrhythmia. Experimental results show that, this method can classify the types of arrhythmia accurately and effectively, and can be used for the field of ECG pathological auxiliary diagnosis.

作者: Yu-Jing Wang Li-Xin Song Shou-Qiang Kang

作者单位: College of Electrical & Electronic Engineering Harbin University of Science and Technology Harbin, China

会议类型: 国际会议

会议名称: The 4th International Conference on Bioinformatics and Biomedical Engineering(第四届IEEE生物信息与生物医学工程国际会议 iCBBE 2010)

会议地点: 成都

会议语种:英文

页码: 1-4

在线出版日期: 2010-06-18（万方平台首次上网日期，不代表论文的发表时间）