Fast and noise-tolerant method of ECG beats classification using wavelet features and fractal dimension

This work proposes an efficient method of Electrocardiogram (ECG) beats classification. ECG is an important biological signal indicating muscular activities of heart. Abnormalities in ECG beats reflect heart malfunctions. Four types of ECG beats including normal (N), left bundle branch block (LBBB), right bundle branch block (RBBB), and premature ventricular contractions (PVC) are intended to be classified. We take two-level wavelet decomposition to decompose ECG beats and extract appropriate features from sub bands of the decomposed signals. According to previous studies, wavelet coefficients seem to be sufficient features for solving the classification problems. However, they do not have suitable performance in high White Gaussian noise (WGN) added to ECG signals. Therefore, we compute fractal dimension using Katz' algorithm besides wavelet coefficients to complement our feature sets. Feature sets are classified by employing probabilistic neural network (PNN) which shows satisfactory results. Final results are indication of highly stable accuracy and high efficiency in different values of added WGN.