Denoising of ECG signals using dual tree complex wavelet transform

Electrocardiogram (ECG) signals usually corrupted by different types of noise like power line interference, baseline drift due to respiration, electromyogram interference, abrupt baseline shift and their composite noise. Denoising of noisy signal has great clinical importance for the diagnosis of cardiac abnormalities. In this paper, dual tree complex wavelet transform (DTCWT) has been used to denoise noisy ECG signals. DTCWT generates complex coefficient to obtain their real and imaginary part by using wavelet filters of dual tree. Noisy ECG signal has been simulated applying five different noise models to ECG records of MIT-BIH Arrhythmia database. The calculated SNR and MSE after denoising using DTCWT showed better performance for all types of noise than the discrete wavelet transforms (DWT). Dual tree complex wavelet transforms has performed significantly for composite of different noises.