ECG Signal Enhancement through Subband Adaptive Soft Thresholding and EMD for Efficient Cardiac Arrhythmia Analysis

Abstract

Among various Bio-medical signals, Electrocardiogram (ECG) is most important signal to diagnose Cardiac Arrhythmia (CA). However the noises added during ECG signal acquisition makes the system less accurate in CA detection. This paper presents a new ECG denoising approach based on noise reduction aspects in Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) domains. The main objective to use DWT is to remove the high frequency noise components like EMG noise which leads to significant cropping in QRS complexes and also lead to distortions at the beginning and ending of QRS complexes. This approach also designs an adaptive threshold which considers the average noise and signal powers to remove the noise form ECG signal. The segmentation of ECG signal involves to focus only on the noise dominant components such that the overall complexity also in the control. Further the EMD preserves the QRS complexes information by adapting the adaptive thresholding for the Intrinsic Mode Functions (IMFs) after finding the noise dominant IMFs from signal dominant IMFs. Simulation results are carried out through MIT-BIH Arrhythmia database and the performance is evaluated with respect to the performance metrics like Mean Square Error and Signal to Noise Ratio. The proposed approach provides better results compared to the conventional state-of the-art techniques.