Analysis of ECG Signals Using Advanced Wavelet Filtering Approach

Electrocardiogram signal is principally used for the interpretation and assessment of heart's condition. The main criteria in ECG signal analysis is interpretation of QRS complex and obtaining its feature information. R wave is the most significant segment of this QRS complex, which has a prominent role in finding HRV (Heart Rate Variability) features and in determining its characteristic features. This paper intends to propose a novel approach for the analysis of ECG signals. The ECG signal is preprocessed using stationary wavelet transform (SWT) with interval dependent thresholding integrated with the wiener filter and is then subjected to Hilbert transform along with a window to enhance the presence of QRS complexes, to detect R-Peaks by setting a threshold. The proposed algorithm is validated with different parameters like Sensitivity, + Predictivity and Accuracy. The proposed method yields promising results with 99.94 % Sensitivity, 99.92 % + Predictivity, 99.87 % Accuracy. Finally the proposed method is compared with other methods to show the efficiency of the proposed technique for the analysis of ECG Signal.