Phonocardiogram (PCG), the graphic recording of heart signals, is analyzed to determine the cardiac mechanical function. In the recording of PCG signals, the major problem encountered is the corruption by surrounding noise signals. The noise-corrupted signal cannot be analyzed and used for advanced processing. Therefore, there is a need to denoise these signals before being employed for further processing. Adaptive Noise Cancellers are best suited for signal denoising applications and can efficiently recover the corrupted PCG signal. This paper introduces an optimal adaptive filter structure using a Sign Error LMS algorithm to estimate a noise-free signal with high accuracy. In the proposed filter structure, a noisy signal is passed through a multistage cascaded adaptive filter structure. The number of stages to be cascaded and the step size for each stage are adjusted automatically. The proposed Variable Stage Cascaded Sign Error LMS (SELMS) adaptive filter model is tested for denoising the fetal PCG signal taken from the SUFHS database and corrupted by Gaussian and colored pink noise signals of different input SNR levels. The proposed filter model is also tested for pathological PCG signals in the presence of Gaussian noise. The simulation results prove that the proposed filter model performs remarkably well and provides 8–10 dB higher SNR values in a Gaussian noise environment and 2-3 dB higher SNR values in the presence of colored noise than the existing cascaded LMS filter models. The MSE values are improved by 75–80% in the case of Gaussian noise. Further, the correlation between the clean signal and its estimate after denoising is more than 0.99. The PSNR values are improved by 7 dB in a Gaussian noise environment and 1-2 dB in the presence of pink noise. The advantage of using the SELMS adaptive filter in the proposed filter model is that it offers a cost-effective hardware implementation of Adaptive Noise Canceller with high accuracy.

https://downloads.hindawi.com/journals/jhe/2022/3039624.pdf

A Low-Cost Multistage Cascaded Adaptive Filter Configuration for Noise Reduction in Phonocardiogram Signal

Effective removal of eye-blink artifacts in EEG signals with semantic segmentation

Abstract Although fetal phonocardiogram (fPCG) signals have become a good indicator for discovered heart disease, they may be contaminated by various noises that reduce the signals quality and the final diagnosis decision. Moreover, the noise may cause the risk of the data to misunderstand the heart signal and to misinterpret it. The main objective of this paper is to effectively remove noise from the fPCG signal to make it clinically feasible. So, we proposed a novel noise reduction method based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), wavelet threshold and Crow Search Algorithm (CSA). This noise reduction method, named ICEEMDAN-DWT-CSA, has three major advantages. They were, (i) A better suppress of mode mixing and a minimized number of IMFs, (ii) A choice of wavelet corresponding to the study signal proven by the literature and (iii) Selection of the optimal threshold value. Firstly, the noisy fPCG signal is decomposed into Intrinsic Mode Functions (IMFs) by the (ICEEMDAN). Each noisy IMFs were decomposed by the Discrete Wavelet Transform (DWT). Then, the optimal threshold value using the (CSA) technique is selected and the thresholding function is carried out in the detail’s coefficients. Secondly, each denoised (IMFs) is reconstructed by applying the Inverse Discrete Wavelet Transform (IDWT). Finally, all these denoised (IMFs) are combined to get the denoised fPCG signal. The performance of the proposed method has been evaluated by Signal to Noise Ratio (SNR), Mean Square Error (MSE) and the Correlation Coefficient (COR). The experiment gave a better result than some standard methods.

Fetal phonocardiogram signals denoising using improved complete ensemble (EMD) with adaptive noise and optimal thresholding of wavelet coefficients

Phonocardiogram (PCG) signals are contaminated with various noise signals, which hinders the accurate diagnostic interpretation of the signal. Discrete wavelet transform (DWT) is a well-known technique used to remove noise from PCG signals and improve signal quality. The performance of DWT-based denoising depends upon several parameters involved in the process, such as mother wavelets used for decomposition, the number of decomposition levels (DLs), thresholding technique used and the threshold estimation rule followed. In this work, an investigative study is carried out to select the optimal parameter values which give the best denoising performance. The metrics such as mean-square error (MSE), normalized-mean-square error (NMSE), root-mean-square error (RMSE), percentage root-mean-square difference (PRD) and signal-to-noise ratio (SNR) are used to evaluate the performance of the denoising in this study. The results obtained show that the fifth-order Coiflet wavelet is best suited for denoising PCG signals when applied with the soft thresholding (ST) function and rigrsure threshold selection rule. Also, the optimum number of DLs resulting in better performance is level 6. SNR value obtained from the studies shows the efficacy of the parameters selected for denoising. The denoised PCG signals provide accurate information to determine various kinds of heart valve-related disorders (HVDs). 

Investigation of discrete wavelet transform domain optimal parametric approach for denoising of phonocardiogram signal

Wearable health monitoring devices based on photoplethysmogram (PPG) can estimate valuable physiological parameters and are often regarded as a touchstone for indicating the cardiovascular status of a person. Severe motion artifacts are generally observed in the acquired PPG signals from the biosensors in the wearables when the PPG acquisition is performed during different physical activities. Classical filters can remove thermal noise and electromagnetic interference from the raw PPG signals, but they fail to handle the motion artifacts that cover a wider range of signal frequencies. This work introduces a new motion artifact removal scheme that exploits the sparsity of PPG signals in the tunable-Q factor wavelet transform (TQWT) domain using the basis pursuit denoising (BPDN) scheme. The proposed scheme deftly removes noise while preserving the original morphology of the PPG signal without using any reference accelerometer sensor data. In particular, a regularized SALSA-based BPDN algorithm is employed for sparse recovery, which utilizes a soft thresholding approach to denoise the noisy PPG signals. TQWT can be tuned according to the oscillatory behavior of the raw PPG signal. At the same time, BPDN exploits the sparsity of the wavelet coefficients to yield an artifact-free reconstruction of PPG. Further, heart rate estimation is performed using the denoised PPG signal with support vector regression to demonstrate the clinical information preservation during the proposed denoising process. The proposed denoising scheme is evaluated on two publicly available databases, and an overall minimum mean absolute error (MAE) of 1.03 and 0.76 beats per minute (bpm) is achieved. The suggested denoising approach has demonstrated better performance with the lowest MAE compared to the previously reported methods. 

Exploiting Tunable Q-factor Wavelet Transform Domain Sparsity to Denoise Wrist PPG Signals

Phonocardiogram (PCG) signal contains significant bio-acoustic information reflecting the operation of the heart, and is used to detect the various diseases related to heart valve. But it is highly susceptible to noise, and the sources of noise includes lung and breath sounds, noise from contact between the recording device and skin, environmental noise, etc. Hence, denoising of PCG signal is very important for the proper diagnosis of heart diseases. In this paper, a discrete wavelet transform (DWT) based threshold tuning is investigated to deliver denoised PCG signal. The performance of the denoising algorithm is evaluated using different metrics such as mean-square error, normalized-mean-square error, root-mean-square error, percentage root-mean-square difference, and signal-to-noise ratio by determining the most suitable parameters (wavelet family, level of decomposition, and thresholding type) for the denoising process. The evaluation results obtained from the different metrics gives the best denoising performance from the reconstructed PCG signal.

Evaluation of Performance Metrics and Denoising of PCG Signal using Wavelet Based Decomposition

In this letter, a transform-based methodology is presented for the compression of fetal phonocardiogram (fPCG) signal. Transform techniques, such as discrete cosine transform (DCT), discrete wavelet transform (DWT), and fast Walsh–Hadamard transform (FWHT), are applied for the decomposition of fPCG signal and a comparative study on the performance of different transforms is made in terms of measures, such as compression ratio (CPR), percent root-mean-square difference (PRD), mean square error (MSE), quality score (QS), and signal-to-noise ratio (SNR). The simulation results reveal the effectiveness of the transform techniques in fPCG signal compression. In comparison, it is observed that FWHT gives a better CPR, whereas DWT yields good fidelity parameters with comparable CPR.

https://ieeexplore.ieee.org/iel7/7782634/9416368/09411736.pdf

Ponnalagu R N

Papers

Evaluation of Performance Metrics and Denoising of PCG Signal using Wavelet Based Decomposition

A Transform Domain Approach for the Compression of Fetal Phonocardiogram Signal