Choice of the wavelet analyzing in the phonocardiogram signal analysis using the discrete and the packet wavelet transform

Chérif, L.; Debbal, S. M.; Bereksi–Reguig, F.

doi:10.1016/j.eswa.2009.09.036

Cited by 72 publications

(39 citation statements)

References 7 publications

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“…Therefore, one of the most prominent advantages of the à trous algorithm is the approximate independency of its results from sampling frequency. This is because of the main frequency contents of the PCG signal concentrate on the range less than 700 Hz [30][31][32]. After examination of various databases with different sampling frequencies (range between 5 and 50 kHz), it has been concluded that in low sampling frequencies (less than 750 Hz), scales 2 ( = 1, 2, .…”

Section: Dwt Using à Trous Methodsmentioning

confidence: 97%

“…The CWT was found to be the most successful technique for the analysis of the PCG signal. Cherif et al [31], studied the extraction of features out of heart sounds in time-frequency (TF) domain for recognition of heart sounds through TF analysis. They compared DWT and PWT for the analysis of heart sound signals and it was proved that the DWT is more suited than the PWT in filtering of clicks and murmurs.…”

Section: Introductionmentioning

confidence: 99%

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Optimal delineation of PCG sounds via false‐alarm bounded segmentation of a wavelet‐based principal components analyzed metric

Homaeinezhad

Atyabi

Deneshvar

et al. 2011

Numer Methods Biomed Eng

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SUMMARYThe aim of this study is to describe a new false-alarm probability (FAP) bounded unified framework for segmentation of the phonocardiogram (PCG) signal sounds registered by an electronic stethoscope board. To meet this end, first the original PCG signal is pre-processed by application of an appropriate bandpass finite-duration impulse response (FIR) filter and then by implementation of à trous discrete wavelet transform (DWT) to the filtered signal for extracting several dyadic scales. Then, after choosing a proper scale, a fixed sample size sliding window is moved on the selected scale and in each slide, six feature vectors namely summation of the nonlinearly amplified Hilbert transform, summation of absolute first order differentiation, summation of absolute second-order differentiation, curve length, area and variance of the excerpted segment are calculated. Then, all feature trends are normalized and utilized to construct a newly proposed principal components analyzed geometric index (PCAGI) (to be used as the segmentation decision statistic (DS)) by application of a linear orthonormal projection. Next, using an adaptive smoothing filter (ASF), the obtained metric is modulated and freed from the fast fluctuations occurring in the vicinity of events onset and offset locations which consequently results in enhancement of edge detection accuracy. Later, histogram parameters of the filtered DS metric are used for the regulation of the -level Neyman-Pearson classifier for FAP-bounded delineation of the PCG events. To assess the performance quality of the proposed PCG segmentation algorithm, the method was applied to all 85 records of Nursing Student Heart Sounds database including stenosis, insufficiency, regurgitation, gallop, septal defect, sound split, rumble, murmur, clicks, friction rub and snap disorders with different sampling frequencies. The method was also applied to the records obtained from an electronic stethoscope board designed for fulfillment of this study in the presence of high-level power-line noise and external disturbing sounds and as a result, no false positive or false negative errors were detected. High robustness against measurement noises of the electronic stethoscopes, acceptable detection-segmentation accuracy of PCG events in the presence of severe heart valvular and arrhythmic dysfunctions within a tolerable computational burden (processing time) and having no parameter dependency on the acquisition sampling frequency can be mentioned as important merits and capabilities of the proposed PCAGI-based PCG events detection-segmentation algorithm.

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Section: Dwt Using à Trous Methodsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Optimal delineation of PCG sounds via false‐alarm bounded segmentation of a wavelet‐based principal components analyzed metric

Homaeinezhad

Atyabi

Deneshvar

et al. 2011

Numer Methods Biomed Eng

View full text Add to dashboard Cite

show abstract

“…The CWT was found to be the most successful technique for the analysis of the PCG signal. Cherif et al (2010), studied the extraction of features out of heart sounds in time-frequency (TF) domain for recognition of heart sounds through TF analysis, and compared DWT and PWT for analyzing the heart sound signals and proved that the DWT is more suited than the PWT in filtering the clicks and murmurs. Debbal and Bereksi-Reguig (2007), concentrated on analyzing the second heart sound S2 and its two major components A2 and P2 and concerned with the identification and automatic measure of the split in the second heart sound (S2) of the PCG for normal and pathological cases.…”

Section: Introductionmentioning

confidence: 99%

“…In such cases, the detection and delineation of low-power low-frequency sounds might be accompanied by unacceptable false negative (FN) errors, while due to existence of noises and disturbing potentials, large false positive (FP) errors may appear. Heretofore, in order to pre-process the PCG signal for derivation of an appropriate decision statistic, several methods based on Fourier transform (FT), short-time Fourier transform (STFT), Gabor spectrum, Wigner distribution, Hilbert transform (HT), discrete wavelet transform (DWT), continuous wavelet transform (CWT), packet wavelet transform (PWT), soft-computations and probabilistic methods have been introduced by authors (Choi 2008;Debbal and Bereksi-Reguig 2007;Cherif et al 2010;Sepehri et al 2008;Choi and Jiang 2008;Sengur 2008;Yana et al 2010;Dokur andÖ lmez 2008, 2009;Nigam and Priemer 2005;Kumar et al 2007;Ahlstrom et al 2008;Choi and Jiang 2010;Hadjileontiadis and Panas 1998;Babaei and Geranmayeh 2009;Uguz et al 2008;Jin et al 2009). For instance, (Choi 2008), proposed a method for the detection of the valvular heart disorders by the PWT decomposition and the support vector machine techniques as the classifier.…”

Section: Introductionmentioning

confidence: 99%

Discrete Wavelet-Aided Delineation of PCG Signal Events via Analysis of an Area Curve Length-Based Decision Statistic

et al. 2010

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The aim of this study is to describe a robust unified framework for segmentation of the phonocardiogram (PCG) signal sounds based on the false-alarm probability (FAP) bounded segmentation of a properly calculated detection measure. To this end, first the original PCG signal is appropriately pre-processed and then, a fixed sample size sliding window is moved on the pre-processed signal. In each slid, the area under the excerpted segment is multiplied by its curve-length to generate the Area Curve Length (ACL) metric to be used as the segmentation decision statistic (DS). Afterwards, histogram parameters of the nonlinearly enhanced DS metric are used for regulation of the α-level Neyman-Pearson classifier for FAP-bounded delineation of the PCG events. The proposed method was applied to all 85 records of Nursing Student Heart Sounds database (NSHSDB) including stenosis, insufficiency, regurgitation, gallop, septal defect, split sound, rumble, murmur, clicks, friction rub and snap disorders with different sampling frequencies. Also, the method was applied to the records obtained from an electronic stethoscope board designed for fulfillment of this study in the presence of high-level power-line noise and external disturbing sounds and as the results, no false positive (FP) or false negative (FN) errors were detected. High noise robustness, acceptable detection-segmentation accuracy of PCG events in various cardiac system conditions, and having no parameters dependency to the acquisition sampling frequency can be mentioned as the principal virtues and abilities of the proposed ACL-based PCG events detection-segmentation algorithm.

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“…Unser et al [18] proposed to transpose the concept to the wavelet domain by considering a complexifi ed version of the Riesz transform which has the remarkable property of mapping a real -valued (primary) wavelet basis of L2 into a complex one. Cherif et al [19] proposed the extraction of features out of heart sounds in time-frequency (TF) domain for recognition of heart sounds through TF analysis. Bendjama et al [20] proposed a new combined fault diagnosis method that uses Wavelet Transform (WT), Principal Component Analysis(PCA) and Neural Networks(NN) for rotating machinery vibration monitoring and analysis.…”

Section: Introductionmentioning

confidence: 99%

Research on a Signal Analysis Method based on Wavelet Theory and Approximate Entropy Algorithm

Yu¹

2016

IJFGCN

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Choice of the wavelet analyzing in the phonocardiogram signal analysis using the discrete and the packet wavelet transform

Cited by 72 publications

References 7 publications

Optimal delineation of PCG sounds via false‐alarm bounded segmentation of a wavelet‐based principal components analyzed metric

Optimal delineation of PCG sounds via false‐alarm bounded segmentation of a wavelet‐based principal components analyzed metric

Discrete Wavelet-Aided Delineation of PCG Signal Events via Analysis of an Area Curve Length-Based Decision Statistic

Research on a Signal Analysis Method based on Wavelet Theory and Approximate Entropy Algorithm

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