A powerful novel method for ECG signal de-noising using different thresholding and Dual Tree Complex Wavelet Transform

Abstract

In this research, we proposed a new method for noise removal based on Dual Tree Complex Wavelet Transform (DTCWT) in order to maintain diagnostic information for ECG. DTCWT provides significant different levels of information about the nature of the data in terms of time and frequency. It also fights the problem of discrete wavelet transforms (DWT) variance. Signal Energy Contribution Efficiency (ECE) and Kurtosis in wavelet sub-bands is important to evaluate the noise content. Accordingly, a noise removal factor is provided. The proposed method is presented using these factors at baseline levels and Donoho threshold in other remaining levels. The performance of proposed method was evaluated and compared with other methods. Filtered signal quality was analyzed using the percentage root mean square difference (PRD), signal to noise (SNR) and mean square error (MSE) criteria. It is observed that the proposed method not only filters the signal better than the most prominent methods, but also effectively helps to maintain diagnostic information.