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QRS complex detection and arrhythmia classification using SVM
Published in Institute of Electrical and Electronics Engineers Inc.
2015
Pages: 239 - 243
Abstract
The Electrocardiogram (ECG) is most widely used techniques to detect cardiac diseases. In this paper we propose ECG signal analysis and classification method using wavelet energy histogram method and support vector machine (SVM). The classification of cardiac arrhythmia in the ECG signal consists of three stages including ECG signal preprocessing, feature extraction and heartbeats classification. The discrete wavelet transform is used as preprocessing tool for signal denoising and feature extraction such as R point location, QRS complex detection. Morphological features extracted from the QRS complex are employed as input to the classifier. Binary SVM is used as a classifier to classify the input ECG beat into four classes i.e. Normal, Left bundle branch block, Right bundle branch block and Premature ventricular contraction. MIT-BIH arrhythmia database is used for performance analysis. The proposed classifier performs well with an average sensitivity of 100%, specificity of 99.66%, positive prediction of 99%, false prediction of 0.0033, and average classification rate of 99.75%. © 2015 IEEE.
About the journal
JournalData powered by Typeset2015 Communication, Control and Intelligent Systems (CCIS)
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open AccessNo