Denoising of Heart Sound Signals Using Discrete Wavelet Transform

Ali, Mohammed N.; El‐Dahshan, El‐Sayed A.; Yahia, Ashraf

doi:10.1007/s00034-017-0524-7

Cited by 78 publications

(34 citation statements)

References 16 publications

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“…According to the standard of mutual information measurement, the most abundant nodes in the wavelet tree were selected, and the noise component from the heart sound signals was suppressed by using the SVD technique to process the coefficients corresponding to the selected nodes. Ali et al [13] selected different DWT families, threshold types, and signal decomposition levels to denoise the heart sound signals, and evaluated the influence of different wavelet functions and wavelet decomposition levels on the efficiency of the denoising algorithm. ey concluded that the Db10 wavelet and the discrete Meyer wavelet with the fourth-order decomposition can obtain the maximum SNR (signal-to-noise ratio) and the minimum RMSE (standard error) of the standard heart sounds.…”

Section: Discussionmentioning

confidence: 99%

A Review of Computer-Aided Heart Sound Detection Techniques

Tang

et al. 2020

BioMed Research International

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Cardiovascular diseases have become one of the most prevalent threats to human health throughout the world. As a noninvasive assistant diagnostic tool, the heart sound detection techniques play an important role in the prediction of cardiovascular diseases. In this paper, the latest development of the computer-aided heart sound detection techniques over the last five years has been reviewed. There are mainly the following aspects: the theories of heart sounds and the relationship between heart sounds and cardiovascular diseases; the key technologies used in the processing and analysis of heart sound signals, including denoising, segmentation, feature extraction and classification; with emphasis, the applications of deep learning algorithm in heart sound processing. In the end, some areas for future research in computer-aided heart sound detection techniques are explored, hoping to provide reference to the prediction of cardiovascular diseases.

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Section: Discussionmentioning

confidence: 99%

A Review of Computer-Aided Heart Sound Detection Techniques

Tang

et al. 2020

BioMed Research International

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“…First, additive means the way in which noise joins in the signal. The noisy signal

is generated by adding the noise components

to the original signal

[ 47 ]. This process is described in Figure 4 and the mathematical expression is described as below:

…”

Section: Methodsmentioning

confidence: 99%

The Optimal Selection of Mother Wavelet Function and Decomposition Level for Denoising of DCG Signal

Jang

Sim

Yang

et al. 2021

Sensors

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The aim of this paper is to find the optimal mother wavelet function and wavelet decomposition level when denoising the Doppler cardiogram (DCG), the heart signal obtained by the Doppler radar sensor system. To select the best suited mother wavelet function and wavelet decomposition level, this paper presents the quantitative analysis results. Both the optimal mother wavelet and decomposition level are selected by evaluating signal-to-noise-ratio (SNR) efficiency of the denoised signals obtained by using the wavelet thresholding method. A total of 115 potential functions from six wavelet families were examined for the selection of the optimal mother wavelet function and 10 levels (1 to 10) were evaluated for the choice of the best decomposition level. According to the experimental results, the most efficient selections of the mother wavelet function are “db9” and “sym9” from Daubechies and Symlets families, and the most suitable decomposition level for the used signal is seven. As the evaluation criterion in this study rates the efficiency of the denoising process, it was found that a mother wavelet function longer than 22 is excessive. The experiment also revealed that the decomposition level can be predictable based on the frequency features of the DCG signal. The proposed selection of the mother wavelet function and the decomposition level could reduce noise effectively so as to improve the quality of the DCG signal in information field.

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“…Therefore, according to the sampling frequency (Fs = 44.1 kHz), WDbased HSs are filtered to obtain the efficient frequency components (21.5 ∼ 689 Hz). The Daubechies wavelet 10 (dB10) has been used to give the maximum signalto-noise ratio and minimum root-mean-square error for HSs [19]. Therefore, dB10 is selected for use as the mother wavelet for preprocessing HSs.…”

Section: Methodsmentioning

confidence: 99%

A Novel Intelligent System Based on Adjustable Classifier Models for Diagnosing Heart Sounds

Sun¹,

Huang

Zhang

et al. 2021

Preprint

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Background: As an eﬃcient method, heart sounds (HSs) analysis by classifying the features extracted from the four-stage sequence consisting of the ﬁrst heart sound (S1), second heart sound (S2), duration from S1 to S2 and duration from S2 to S1, has been widely used to diagnose heart disease and evaluate heart functions. However, the feature is diﬃcult to be extracted with high accuracy due to the four stages segmented from HSs with low accuracy; the ﬁxed classiﬁers achieved by training the features from the old samples cannot better ﬁt the features from the new ones because they are not adjusted with the incremental features. Thus, a novel intelligent diagnostic system, the innovations of which are primarily reﬂected in the automatic feature extraction and adjustable classiﬁer models, is proposed to realize the diagnosis of heart diseases with higher accuracy. Methods: The three stages of the proposed system are summarized as follows. In stage 1, the short time modiﬁed Hilbert transform (STMHT)-based curve is used to segment and extract the ﬁrst complex sound (CS1) and second complex sound (CS2). In stage 2, the envelopes CS1FE and CS2FE for periods CS1 and CS2 are obtained via a novel method, and the frequency features are automatically extracted from CS1FE and CS2FE by setting diﬀerent threshold value (Thv) lines. Finally, the principal component analysis (PCA)-based ﬁrst three principal components γ1, γ2, and γ3 are determined as the diagnostic features. In stage 3, a Gaussian mixture model (GMM)-based objective function fet(x) is generated. Then, the χ2 distribution for component k is determined by calculating the Mahalanobis distance from x to the class mean µk for component k, and the conﬁdence region of component k is determined by adjusting the optimal conﬁdence level βk and used as the criterion to diagnose HSs. Results: The performance evaluation was validated by sounds from online HS databases and clinical heart databases. The accuracy of the proposed method was compared to the accuracies of other well-known classiﬁers, and the highest classiﬁcation accuracies of 99.43%, 98.93%, 99.13%, 99.85%, 98.62%, 99.67% and 99.91% in the detection of MR, MS, ASD, NM, AS, AR and VSD sounds were achieved by setting βk(k = 1,2,...,7) to 0.87,0.65,0.67,0.65,0.67,0.79 and 0.87, respectively. Conclusions: This proposed intelligent diagnosis system provides an eﬃcient way to diagnose seven types of heart diseases. In addition, methods to manage the sounds (such as some ASD sounds) when CS1 and CS2 cannot be segmented and extracted via the STMHT method will be explored in the future to characterize the physical meanings of the frequency components and to build a model of the secondary curve in the frequency domain.

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Denoising of Heart Sound Signals Using Discrete Wavelet Transform

Cited by 78 publications

References 16 publications

A Review of Computer-Aided Heart Sound Detection Techniques

A Review of Computer-Aided Heart Sound Detection Techniques

The Optimal Selection of Mother Wavelet Function and Decomposition Level for Denoising of DCG Signal

A Novel Intelligent System Based on Adjustable Classifier Models for Diagnosing Heart Sounds

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