Denoising of mixed noises in ECG with separate noise estimators based on discrete wavelet transform
Ching-Haur Chang, Tsung-Min Wang, Han-Lin Hsu · 2016
ECG may contain different types of noise. For example, it may be corrupted simultaneously by baseline wander and some kinds of high-frequency noise. A technique capable of removing mixed noises using separate noise estimators based on discrete wavelet transform (DWT) is proposed in this paper. By applying a multilevel DWT to the noisy ECG a set of detail and approximation coefficients was used to estimate different types of noise. The approximation coefficients are used to reconstruct a waveform which is in turn used as the estimate of the low-frequency noise, while parts of the detail coefficients are used to reconstruct a waveform which is in turn used as the estimate of the high-frequency noise. This is useful for the case in which the noise spans over a wide range of frequencies. The number of levels used for each noise estimator is dependent on the noise. Because only parts of detail coefficients are used in the high-frequency noise estimator, significant reduction of computation is obtained compared to previous methods using adaptive thresholding techniques. Simulation results show significant improvement compared to traditional approaches using empirical mode decomposition.