An Efficient Approach for Arrhythmia Classification | Signal Processing Project | MATLAB |Arrhythmia

An Efficient Approach for Arrhythmia Classification using MATLAB.
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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 Discrete Wavelet Transform, Local Binary Pattern and Random Forest accurately and efficiently. Adaptive filtering using Normalized Least Mean Square (NLMS) 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, PR duration is extracted from the wavelet decomposition and mean and standard deviation features extracted from LBP. With these parameters classification of arrhythmia is done. Random Forest algorithm was trained and tested using the extracted parameters are used for training and testing. This classification is done for some patient samples. The overall accuracy of our approach is 99.1%.