Denoising of phonocardiogram signals using variational mode decomposition

Sujadevi, V. G.; Kumar, Sachin; Mohan, Neethu; Arunjith, A.S.

doi:10.1109/icacci.2017.8126043

Cited by 13 publications

(17 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This constrained optimization problem estimate the unknown n central frequencies and corresponding modes, subject to the constraint that sum of the estimated modes should be equal to the input signal [21,22]. This problem is solved using ADMM algorithm by taking the augmented Lagrangian multiplier.…”

Section: Vmd Decompositionmentioning

confidence: 99%

“…It is observed that the denoising using GS approach, the characteristics are retained. The comparison of the denoising performance of the proposed method with VMD based denoising [21] is shown in Fig. 7.…”

Section: Fixing Of and Group Sparsity Window Size K 1 And Kmentioning

confidence: 99%

“…Further, the denoising performance is compared with adaptive OGS, wavelet denoising and VMD based denoising by taking the normal PCG signal from MHSM database. The results of evaluation and comparison with VMD denoising [21], adaptive OGS [25] and wavelet denoising are tabulated in Table 4. The wavelet denoising is a commonly preferred denoising technique and is performed using db 10 wavelet.…”

Section: Fixing Of and Group Sparsity Window Size K 1 And Kmentioning

confidence: 99%

“…Performance evaluation of group sparsity based PCG denois-Performance comparison on denoising with VMD based denoising[21]. a Input SNR = 10 dB, b Input SNR = 20 dB signal from EGM database, sampled at 11.25 kHz.…”

mentioning

confidence: 99%

See 3 more Smart Citations

A hybrid method for fundamental heart sound segmentation using group-sparsity denoising and variational mode decomposition

et al. 2019

Self Cite

View full text Add to dashboard Cite

Segmentation of fundamental heart sounds-S1 and S2 is important for automated monitoring of cardiac activity including diagnosis of the heart diseases. This paper proposes a novel hybrid method for S1 and S2 heart sound segmentation using group sparsity denoising and variation mode decomposition (VMD) technique. In the proposed method, the measured phonocardiogram (PCG) signals are denoised using group sparsity algorithm by exploiting the group sparse (GS) property of PCG signals. The denoised GS-PCG signals are then decomposed into subsequent modes with specific spectral characteristics using VMD algorithm. The appropriate mode for further processing is selected based on mode central frequencies and mode energy. It is then followed by the extraction of Hilbert envelope (HEnv) and a thresholding on the selected mode to segment S1 and S2 heart sounds. The performance advantage of the proposed method is verified using PCG signals from benchmark databases namely eGeneralMedical, Littmann, Washington, and Michigan. The proposed hybrid algorithm has achieved a sensitivity of 100%, positive predictivity of 98%, accuracy of 98% and detection error rate of 1.5%. The promising results obtained suggest that proposed approach can be considered for automated heart sound segmentation.

show abstract

Section: Vmd Decompositionmentioning

confidence: 99%

Section: Fixing Of and Group Sparsity Window Size K 1 And Kmentioning

confidence: 99%

Section: Fixing Of and Group Sparsity Window Size K 1 And Kmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

A hybrid method for fundamental heart sound segmentation using group-sparsity denoising and variational mode decomposition

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Frequency domain methods are preferred since they contain adequate information on the spectral characteristics of the PCG signal components [ 9 ]. Among the frequency domain approaches, the most commonly used techniques are Empirical Mode Decomposition (EMD) [ 10 , 11 ], Variational Mode Decomposition (VMD) [ 12 ], Singular Spectrum Analysis (SSA) [ 13 ], and Tunable Q-Wavelet Transform [ 14 ]. Although these techniques give an efficient performance, the computational time is high.…”

Section: Introductionmentioning

confidence: 99%

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

Pauline

Dhanalakshmi

Kumar

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

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.

show abstract