Automatic heart sound detection in pediatric patients without electrocardiogram reference via pseudo-affine Wigner–Ville distribution and Haar wavelet lifting

Gavrovska, Ana; Bogdanović, Vesna; Reljin, Irini; Reljin, Branimir

doi:10.1016/j.cmpb.2013.11.018

Cited by 32 publications

(26 citation statements)

References 14 publications

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“…They are compared to additional methods from the literature. We considered standard methods based on signal or its Shannon energy based envelope [5] (such as variance, highest amplitude/envelope value, signal/envelope area, and positive/negative signal area [6,10,11]) and different fractal methods: BCFD, KFD, BFD1, and BFD2 (described by expressions (1)- (3)), VFD [16], Sevcik (SFD) [31], and Higuchi (HFD) [22]. Five of FD methods can be considered highly shape related (KFD, BFD1, BFD2, SFD, and HFD).…”

Section: Resultsmentioning

confidence: 99%

“…High accuracy (above 95%) is obtained for the classification and labeling of the primary sounds regardless of the intersound relationships. In comparison to the identification from [5,11], where highest envelope value ( ) is applied for S1-systole and S2-diastole differentiation, the structure assessment overcomes the errors found due to varying energy in a signal, as presented in Figure 6(a). Recurring sounds are not assumed for the classification model, and thus the methodology may overcome errors found due to nondetected candidates and similar misinterpretations.…”

Section: Discussion and Comparisonmentioning

confidence: 99%

“…Similar auxiliary envelopes are applied using different joint time-frequency representations [8,10,11]. The identification methods for the primary heart sounds usually consider signal/envelope characteristics (e.g., maximum, variance, frequency, and positive/negative area [6,9,12]).…”

Section: Introductionmentioning

confidence: 99%

“…The advantages of fractal framework are also recognized for the heart sounds, where different fractal dimension (FD) methods related to variation and structure of the waveforms are applied. Typical heart sounds S1 and S2, of short duration (20-200 ms) and low frequencies , can be considered fractal in nature [2,11].…”

Section: Introductionmentioning

confidence: 99%

“…In the previous work of our group, initial examination of blanket method is shown as a possible tool for primary heart sounds as well [21]. Moreover, in our previous works [11,12], novel methods are proposed for efficient classification of the auscultatory recordings without the primary heart sound detection using a few multifractal spectrum related features.…”

Section: Introductionmentioning

confidence: 99%

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Identification of S1 and S2 Heart Sound Patterns Based on Fractal Theory and Shape Context

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There has been a sustained effort in the research community over the recent years to develop algorithms that automatically analyze heart sounds. One of the major challenges is identifying primary heart sounds, S1 and S2, as they represent reference events for the analysis. The study presented in this paper analyzes the possibility of improving the structure characterization based on shape context and structure assessment using a small number of descriptors. Particularly, for the primary sound characterization, an adaptive waveform filtering is applied based on blanket fractal dimension for each preprocessed sound candidate belonging to pediatric subjects. This is followed by applying the shape based methods selected for the structure assessment of primary heart sounds. Different methods, such as the fractal ones, are used for the comparison. The analysis of heart sound patterns is performed using support vector machine classifier showing promising results (above 95% accuracy). The obtained results suggest that it is possible to improve the identification process using the shape related methods which are rarely applied. This can be helpful for applications involving automatic heart sound analysis.

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