An Approach for Classifying ECG Arrhythmia Based on Features Extracted from EMD and Wavelet Packet Domains

Any disturbance in the activity of heart can cause irregular heart rhythm known as cardiac arrhythmia. Electrocardiogram (ECG) is one of the most promising tools for classification of different types of arrhythmia, which is necessary until it goes fatal and causes loss of life. For ECG arrhythmia classification, a wide range of signal processing techniques extracting features from time, frequency and time frequency domains have been reported in the literature. Since, ECG is a nonstationary signal, time frequency analysis can perform better than the conventional time or frequency analysis methods. But, development of a multi-class arrhythmia classification method, which is simple yet effective in handling practical conditions such as lack of enough training dataset and random selection of training and testing dataset, is still a challenging task. ECG signals can be well modeled as self-affined fractal sets which vary under different arrhythmia. Thus local fractal dimension (LFD) can be employed as a feature in classifying different ECG arrhythmia. In the empirical mode decomposition (EMD) domain, the basic functions are directly derived from the original signal without the knowledge of any previous value of the signal. Therefore, the Hurst exponent (HE) required for deriving a set of LFD features is calculated from the intrinsic mode functions (IMFs) obtained via EMD of ECG signals. Since, for better approximation of LFD, at least three IMFs are to be determined which is dependent on the length of the ECG signal, time-frequency analysis in the wavelet packet decomposition (WPD) domain is performed for calculating the HE as well as deriving a set of more effective LFD features. Considering the complexity and ease of implementation as an important criterion, a feature set based on energy and entropy of only the 4th level detail WPD coefficients is found to be simple yet the highest capable of solving a multi-class ECG arrhythmia problem. Each of the proposed sets of feature when fed to Euclidean distance based classifier can classify different arrhythmia even with reduced training dataset as well as randomly selected training and testing dataset. Simulations are carried out to evaluate the performance of the proposed method in terms of sensitivity, specificity and accuracy. It is shown that the proposed method outperforms some of the state-of-the-art methods with superior efficacy.