A wavelet based method for denoising of biomedical signal

A wavelet-based method for power-line interference removal in ECG signals

“…The wavelet analysis has been applied to various problems in biomedical engineering including noise removal in ECG signals (Agante and Sa, 1999;AlMahamdy and Riley, 2014;Awal et al, 2014;Bahoura and Ezzaidi, 2010;Chouakri et al, 2006;Garg et al, 2011;Germán-Salló, 2010;Karthikeyan et al, 2012;Li et al, 2009;Patil and Chavan, 2012;Poornachandra and Kumaravel, 2008;Üstündağ et al, 2012). Due to its better time-frequency resolution, it overcomes other classical methods, such as short time Fourier Transform, for instance (Üstündağ et al, 2012).…”

“…Classical methods for ECG denoising based on thresholding techniques present good performances (AlMahamdy and Riley, 2014; Garg et al, 2011;Patil and Chavan, 2012;Poornachandra and Kumaravel, 2008 d n ≤λ, the corresponding value associated to the index n is set to 0. Otherwise, it is preserved.…”

“…It is noteworthy that this is the best configuration for this method (Garg et al, 2011;Patil and Chavan, 2012). The choice of the wavelet function order and the number of decomposition levels are justified since they are the same used by the proposed method.…”

“…Low SNR can difficult the analysis performed by experts or computational applications, since it changes the signal waveform. Typical noise present in ECG signals are due to power-line interference in a frequency band varying from 50 Hz to 60 Hz (Łęski and Henzel, 2005;Patil and Chavan, 2012;Rahman et al, 2010) depending on the country. It occurs due to interferences of electrical equipment as X-ray, air conditioners, elevators (Patil and Chavan, 2012), and also due to the differences in electrode impedances (Bahoura and Ezzaidi, 2010).…”

“…Chouakri et al (2006) compared the performance of Butterworth filters and the multilevel wavelet transform, concluding that improved results were achieved by the wavelet technique. Usually, wavelet-based methods for ECG denoising use thresholding techniques with some additional processing (Agante and Sa, 1999;AlMahamdy and Riley, 2014;Awal et al, 2014;Bahoura and Ezzaidi, 2010;Chouakri et al, 2006;Garg et al, 2011;Germán-Salló, 2010;Karthikeyan et al, 2012;Li et al, 2009;Patil and Chavan, 2012;Poornachandra and Kumaravel, 2008;Üstündağ et al, 2012). Patil and Chavan (2012) compared the PLI removal for different wavelet basis using hard and soft shrinkage functions.…”