Application of wavelet and EMD-based denoising to phonocardiograms

Gavrovska, Ana; Slavković, Marijeta; Reljin, Irini; Reljin, Branimir

doi:10.1109/isscs.2013.6651264

Cited by 29 publications

(12 citation statements)

References 11 publications

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“…Later, the authors investigated EMD decomposition for instantaneous frequency estimation of PCG signals and concluded that EMD being a physical decomposition is more useful than mathematical decompositions like wavelet transform [87]. Likewise, Gavrovska et al [88] employed EMD with wavelet for PCG denoising.…”

Section: Emd and Hilbert-huang Transformmentioning

confidence: 99%

“…Specific measures like mean prediction power and mean accuracy [92], correct recognition rate (CRR) [85], correct and incorrect diagnosis [84], and SNR and ratio R [88] have also been reported. EMD algorithm is compared with other time-frequency algorithms like wavelet and DWT in [87,88,101]. Studies reported in [97,98] used SNR and SNR while [95] employed the average detection rate.…”

Section: Emd and Hilbert-huang Transformmentioning

confidence: 99%

“…Studies reported in [97,98] used SNR and SNR while [95] employed the average detection rate. Gavrovska et al [88] compared DWT, EMD, and EEMD, and concluded that EEMD reports the best performance among the three. Similarly, authors in [101] compared EMD with other denoising techniques and argued that EMD outperforms other methods.…”

Section: Emd and Hilbert-huang Transformmentioning

confidence: 99%

See 2 more Smart Citations

Localization and classification of heart beats in phonocardiography signals —a comprehensive review

Ismail

Siddiqi

Akram

2018

EURASIP J. Adv. Signal Process.

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Phonocardiogram (PCG) signal represents recording of sounds and murmurs resulting from heart auscultation. Analysis of these PCG signals is critical in diagnosis of different heart diseases. Over the years, a variety of methods have been proposed for automatic analysis of PCG signals in time, frequency, and time-frequency domains. This paper presents a comprehensive survey of different methods proposed for automatic analysis of PCG signals with the objective to evaluate the current state-of-the-art and to determine the potential domains of effective analysis. An important aspect of our contribution is that the review is carried out as a function of domains of analysis rather than simply discussing different methods. Our method further splits analysis into pre-processing, localization, and classification, and details are presented in terms of techniques and classifiers used during these phases. Finally, results are summarized for normal heart beat, noisy heart beat, and different pathologies using metrices like accuracy and detection rate. In addition to time and frequency domain, time-frequency based methods including wavelet, empirical mode decomposition (EMD) and time-frequency representation (TFR) are selected for detailed analysis. The review concludes that the time-frequency representations like EMD and wavelets represent areas of exploration in future along with perspective of using different time-frequency techniques together.

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Section: Emd and Hilbert-huang Transformmentioning

confidence: 99%

Section: Emd and Hilbert-huang Transformmentioning

confidence: 99%

Section: Emd and Hilbert-huang Transformmentioning

confidence: 99%

See 1 more Smart Citation

Localization and classification of heart beats in phonocardiography signals —a comprehensive review

Ismail

Siddiqi

Akram

2018

EURASIP J. Adv. Signal Process.

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“…To overcome this problem, we suggested to perform initial analysis of the signal, and to apply lower prediction order using lifting structure in case of the existence of potential abnormality [5]. The concept of filtering in physical domain (by using second generation wavelets and/or empirical mode decomposition [6]) may additionally improve the quality of denoising. …”

Section: A Signal Preprocessingmentioning

confidence: 99%

Review of some new methods for analyzing vital heart signals

Reljin

Gavrovska

Paskas

2014

2014 8th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)

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“…In such situation, heart murmurs overlaps S1 and S2 sounds, leading to difficulty in identifying the boundaries of the sounds. Recently, Gavrovska et al 5 and Papadaniil 19 proposed the application of empirical mode decomposition on HSS. This method seems to be important but it required long computation time.…”

Section: Introductionmentioning

confidence: 99%

An automatic wavelet denoising scheme for heart sounds

Tahar

Bereksi–Reguig

2015

Int. J. Wavelets Multiresolut Inf. Process.

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Phonocardiograms (PCGs), recording of heart sounds, have many advantages over traditional auscultation in that they may be replayed and analyzed for spectral and frequency information. PCG is not a widely used diagnostic tool as it could be. One of the major problems with PCG is noise corruption. Many sources of noise may pollute a PCG signal including lung and breath sounds, environmental noise and blood flow noises which are known as murmurs. These murmurs contain many information on heart hemodynamic which can be used particularly in detecting of heart valve diseases. An automated system for heart murmurs processing can be an important tool in diagnostic of heart diseases using a simple electronic stethoscope. However, the first step before developing any automated system is the segmentation of the PCG signals from which the murmurs can be separated. A robust segmentation algorithm must have a robust denoising technique, where, wavelet transform (WT) is among the ones which exhibits very high satisfactory results in such situations. However, the selection of level of decomposition and the mother wavelet are the major challenges. This paper proposes a novel approach for an automatic selection of mother wavelet and level of decomposition that can be used in heart sounds denoising. The obtained results on both simulative and real PCG signals showed that the proposed approach can successfully select the best level of decomposition with the best mother wavelet that can be used in extraction operation of main PCG sound components (S1 and S2) from various types of murmurs.

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Application of wavelet and EMD-based denoising to phonocardiograms

Cited by 29 publications

References 11 publications

Localization and classification of heart beats in phonocardiography signals —a comprehensive review

Localization and classification of heart beats in phonocardiography signals —a comprehensive review

Review of some new methods for analyzing vital heart signals

An automatic wavelet denoising scheme for heart sounds

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