ECG signal classification using support vector machine based on wavelet multiresolution analysis

In this paper we propose a highly reliable ECG analysis and classification approach using discrete wavelet transform multiresolution analysis and support vector machine (SVM). This approach is composed of three stages, including ECG signal preprocessing, feature selection, and classification of ECG beats. Wavelet transform is used for signal preprocessing, denoising, and for extracting the co-efficients of the transform as features of each ECG beat which are employed as inputs to the classifier. SVM is used to construct a classifier to categorize the input ECG beat into one of 14 classes. In this work, 17260 ECG beats, including 14 different beat types, were selected from the MIT/BIH arrhythmia database. The average accuracy of classification for recognition of the 14 heart beat types is 99.2%.