Heart sound classification using wavelet transform and incremental self-organizing map

Dokur, Zümray; Ölmez, Tamer

doi:10.1016/j.dsp.2008.06.001

Cited by 88 publications

(42 citation statements)

References 13 publications

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“…One approach focused on visualization of the PCG signal characteristics using HOC plots, to enable differentiating pathological disorders [6][7][8][9][10][11]13]. For example, Ergen et al [6].…”

Section: Discussionmentioning

confidence: 99%

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Cumulant-based trapezoidal basis selection for heart sound classification

Safara

2015

Med Biol Eng Comput

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Past decades witnessed the expansion of linear signal processing methods in numerous biomedical applications. However, the nonlinear behavior of biomedical signals revived the interest in nonlinear signal processing methods such as higher-order statistics, in particular higher-order cumulants (HOC). In this paper, HOC are utilized toward heart sound classification. Heart sounds are presented by wavelet packet decomposition trees. Information measures are then defined based on HOC of wavelet packet coefficients, and three basis selection methods are proposed to prune the trees and preserve the most informative nodes for feature extraction. In addition, an approach is introduced to reduce the dimensionality of the search space from the whole wavelet packet tree to a trapezoidal sub-tree of it. This approach can be recommended for signals with a short frequency range. HOC features are extracted from the coefficients of selected nodes and fed into support vector machine classifier. Experimental data is a set of 59 heart sounds from different categories: normal heart sounds, mitral regurgitation, aortic stenosis, and aortic regurgitation. The promising results achieved indicate the capabilities of HOC of wavelet packet coefficients to capture nonlinear characteristics of the heart sounds to be used for basis selection.

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Section: Discussionmentioning

confidence: 99%

“…In previous studies on heart sounds, HOC were utilized to provide visualization facilities for interpreting heart sounds by clinicians [6][7][8][9][10][11][12]. Some studies were also reported on using HOC as features extracted for heart sound classification [2,[12][13][14][15].…”

mentioning

confidence: 96%

Cumulant-based trapezoidal basis selection for heart sound classification

Safara

2015

Med Biol Eng Comput

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“…Dokur et al [37] proposed the feature determination method for heart sounds based on divergence analysis in which two feature extraction methods were comparatively examined for representation of different heart sound categories. Dokur et al [38], studied the effect of the wavelet transforms for both segmentation and feature determination of heart sound signals. In this study, using the third, fourth and fifth decomposition-level detail coefficients, the timings of S1-S2 sounds were determined via an adaptive peak-detector.…”

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

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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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“…Dokur and Ö lmez (2009), proposed feature determination for heart sounds based on divergence analysis which two feature extraction methods were comparatively examined for representation of different heart sound categories. Dokur and Ö lmez (2008), studied the effect of the wavelet transforms for both segmentation and feature determination of heart sound signals. In this study Based on the third, fourth and the fifth decomposition-level detail coefficients, the timings of S1-S2 sounds were determined by an adaptive peak-detector.…”

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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Heart sound classification using wavelet transform and incremental self-organizing map

Cited by 88 publications

References 13 publications

Cumulant-based trapezoidal basis selection for heart sound classification

Cumulant-based trapezoidal basis selection for heart sound classification

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

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