NDLI: Arrhythmia Recognition Based on EMD and Support Vector Machines

Content Provider	IEEE Xplore Digital Library
Author	Yu-Jing Wang Li-Xin Song Shou-Qiang Kang
Copyright Year	2010
Abstract	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.
Starting Page	1
Ending Page	4
File Size	281394
Page Count	4
File Format	PDF
ISBN	9781424447121
ISSN	21517622
DOI	10.1109/ICBBE.2010.5516574
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Pathology Support vector machine classification Electrocardiography Signal processing Wavelet analysis Feature extraction Matrix decomposition Signal analysis Singular value decomposition
Content Type	Text
Resource Type	Article

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