A novel method for the elimination of power line frequency in ECG signal using hyper shrinkage function

A New Method to Detect Driver Fatigue Based on EMG and ECG Collected by Portable Non-Contact Sensors

“…EMG is reconstructed by the IMFs within 10-300 Hz [9], and other IMFs are deleted as noises. Similarly, ECG is reconstructed by the IMFs within 0-50Hz [10,11]. Consequently, the noises of original signals can be effectively weakened by the reconstruction of these satisfied frequency spectra, and de-noised EMG and…”

Development of a Mobile Phone Based e-Health Monitoring Application

“…This noise causes problem in interpreting low amplitude waveform like ECG. Hence, many methods have been utilized on the removal of the power line interference in the ECG signals [7]. The wavelet coefficient threshold based hyper shrinkage function to remove power line frequency was used in [7], a nonlinear adaptive method to remove noise was used in [8], and subtraction procedure for power line interference removing from ECG which is extended to almost all possible cases of sampling rate and interference frequency variation was used in [9].…”

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

“…Some noise reduction techniques are based on digital filters, wavelet transform and adaptive filtering (AlMahamdy and Riley, 2014); singular value decomposition (Bandarabadi and Karami-Mollaei, 2010); independent component analysis (Phegade and Mukherji, 2013) and S-transform (Das and Ari, 2013). Among the algorithms for PLI removal there are digital processing methods based on: fuzzy thresholding (Üstündağ et al, 2012); nonlinear filter bank (Łęski and Henzel, 2005); Fast Fourier Transform and adaptive nonlinear noise estimator (Shirbani and Setarehdan, 2013); Empirical Mode Decomposition (Agrawal and Gupta, 2013); neural networks (Mateo et al, 2008) and wavelet transform (Agrawal and Gupta, 2013;Garg et al, 2011;Poornachandra and Kumaravel, 2008;Rahman et al, 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.…”

“…The best configuration was achieved with the Symlet wavelet with ten vanishing moments and five decomposition levels, hard shrinkage function and heuristic sure rule or rigorous sure thresholding. Poornachandra and Kumaravel (2008) proposed to use of hyper shrinkage function in the subbands that contained the PLI noise at some decomposition level. The obtained results were better when compared to those of the state-of-the-art algorithms.…”