Kurtosis-based noise estimation and multiscale energy to denoise ECG signal

In this work, a novel wavelet-based denoising method for electrocardiogram signal is proposed. A threshold is derived by considering energy contribution of a wavelet subband, noise variance which is based on a novel Gaussian measure, Kurtosis, and number of samples. The robust noise estimator, median absolute deviation, is scaled by a normalized wavelet subband Kurtosis instead of conventional statistical quantile function for Gaussian distribution. Signal distortion is evaluated using percentage root mean square difference (PRD), wavelet weighted percentage root mean square difference (WWPRD), and wavelet energy-based diagnostic distortion (WEDD) measures. The results are compared with existing standard thresholding methods. The lowest PRD, WWPRD, and WEDD values are achieved as 9.523, 17.743, and 4.000% for lead-V2, lead-V3, and lead-II signal, respectively. For validation, spatially nonhomogeneous functions like Blocks, Bumps, HeaviSine, and Doppler with noise are evaluated.