Classification of Arrhythmia using Wavelet Transform and Neural Network Model

Abstract

Cardiovascular diseases are a major cause of death. Change in normal human heart beat may result in different types of cardiac arrhythmias. An Irreversible damage to the heart is possible. In this paper a method is proposed to classify different arrhythmias and normal sinus rhythm, through a combination of wavelet Transform and Artificial Neural Networks (ANN) accurately and efficiently. Adaptive filtering using Recursive Least squares (RLS) adaptive algorithm is utilized to nullify AC and DC noises from the sample ECG signal set. ECG data’s are collected from MITBIH database. As ECG signal is a non- stationary signal wavelet transform is used to decompose the signal at various resolutions. This allows accurate detection and extraction of features. In our approach, discrete wavelet transforms (DWT) coefficients set is obtained from wavelet decomposition which would contain the maximum information about the arrhythmia. RR interval, QRS duration, PR duration is extracted from the wavelet decomposition. With these parameters classification of arrhythmia is done. Multilayer feed forward ANNs employ error back propagation (EBP) learning algorithm were trained and tested using the extracted parameters are used for training and testing the error back propagation (EBP) algorithm. Multilayer feed forward ANNs are employed through this EBP learning algorithm. This classification is done for 84 patient samples. The overall accuracy of our approach is 98.8%.