Kurtosis based multichannel ECG signal denoising and diagnostic distortion measures

Multichannel electrocardiogram (MECG) signal de-noising can be described as a process of removing the clinically unimportant contents present from the signal. Higher order statistics (HOS) can help to retain finer details of an electrocardiogram (ECG) signal which can effectively reduce the noise levels in MECG signal. In this work, it is proposed to evaluate the HOS (kurtosis) in each Wavelet band to denoise an MECG signal. Thresholding levels are derived based on the values of fourth order cumulant, `kurtosis´, of the wavelet coefficients and energy contribution efficiency (ECE) of wavelet sub-bands. The performance of this method for compressed signals is evaluated using percentage root mean square difference (PRD), weighted PRD (WPRD), and wavelet weighted percentage root mean square difference (WWPRD). The proposed algorithm is tested with database of CSE mutlilead measurement library. The results show significant improvement in denoising the MECG signals.