Selection of an optimal mother wavelet basis function for ECG signal denoising

2014 ◽  
Author(s):  
Usman Seljuq ◽  
Faraz Himayun ◽  
Haroon Rasheed
Keyword(s):  
Basis Function ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Wavelet Basis ◽  
Mother Wavelet ◽  
Selection Of

Optimal level and order detection in wavelet decomposition for PCG signal denoising

2019 ◽  
Vol 64 (2) ◽  
pp. 163-176 ◽  
Author(s):  
Mohamed Rouis ◽  
Abdelkrim Ouafi ◽  
Salim Sbaa
Keyword(s):  
Signal To Noise Ratio ◽  
Optimal Level ◽  
Signal Denoising ◽  
Discrete Wavelet ◽  
Clinical Environment ◽  
Mother Wavelet ◽  
New Approach ◽  
The Mean ◽  
Pcg Signals ◽  
Selection Of

Abstract The recorded phonocardiogram (PCG) signal is often contaminated by different types of noises that can be seen in the frequency band of the PCG signal, which may change the characteristics of this signal. Discrete wavelet transform (DWT) has become one of the most important and powerful tools of signal representation, but its effectiveness is influenced by the issue of the selected mother wavelet and decomposition level (DL). The selection of the DL and the mother wavelet are the main challenges. This work proposes a new approach for finding an optimal DL and optimal mother wavelet for PCG signal denoising. Our approach consists of two algorithms designed to tackle the problems of noise and variability caused by PCG acquisition in a real clinical environment for different categories of patients. The results obtained are evaluated by examining the coherence analysie (Coh) correlation coefficient (Corr) and the mean square error (MSE) and signal-to-noise ratio (SNR) in simulated noisy PCG signals. The experimental results show that the proposed method can effectively reduce noise.

scholarly journals Design Methodology of a New Wavelet Basis Function for Fetal Phonocardiographic Signals

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Vijay S. Chourasia ◽  
Anil Kumar Tiwari
Keyword(s):  
Basis Function ◽  
Diagnostic Information ◽  
Successful Implementation ◽  
Wavelet Basis ◽  
Wavelet Theory ◽  
Denoising Method ◽  
Useful Signal ◽  
New Mother ◽  
Selection Of

Fetal phonocardiography (fPCG) based antenatal care system is economical and has a potential to use for long-term monitoring due to noninvasive nature of the system. The main limitation of this technique is that noise gets superimposed on the useful signal during its acquisition and transmission. Conventional filtering may result into loss of valuable diagnostic information from these signals. This calls for a robust, versatile, and adaptable denoising method applicable in different operative circumstances. In this work, a novel algorithm based on wavelet transform has been developed for denoising of fPCG signals. Successful implementation of wavelet theory in denoising is heavily dependent on selection of suitable wavelet basis function. This work introduces a new mother wavelet basis function for denoising of fPCG signals. The performance of newly developed wavelet is found to be better when compared with the existing wavelets. For this purpose, a two-channel filter bank, based on characteristics of fPCG signal, is designed. The resultant denoised fPCG signals retain the important diagnostic information contained in the original fPCG signal.

scholarly journals ECG Signal Denoising and Reconstruction Based on Basis Pursuit

2021 ◽  
Vol 11 (4) ◽  
pp. 1591
Author(s):  
Ruixia Liu ◽  
Minglei Shu ◽  
Changfang Chen
Keyword(s):  
Signal To Noise Ratio ◽  
Heart Diseases ◽  
Gaussian White Noise ◽  
Basis Pursuit ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Penalty Term ◽  
Ecg Signals ◽  
Alternating Direction ◽  
Low Pass

The electrocardiogram (ECG) is widely used for the diagnosis of heart diseases. However, ECG signals are easily contaminated by different noises. This paper presents efficient denoising and compressed sensing (CS) schemes for ECG signals based on basis pursuit (BP). In the process of signal denoising and reconstruction, the low-pass filtering method and alternating direction method of multipliers (ADMM) optimization algorithm are used. This method introduces dual variables, adds a secondary penalty term, and reduces constraint conditions through alternate optimization to optimize the original variable and the dual variable at the same time. This algorithm is able to remove both baseline wander and Gaussian white noise. The effectiveness of the algorithm is validated through the records of the MIT-BIH arrhythmia database. The simulations show that the proposed ADMM-based method performs better in ECG denoising. Furthermore, this algorithm keeps the details of the ECG signal in reconstruction and achieves higher signal-to-noise ratio (SNR) and smaller mean square error (MSE).

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