Identification of fundamental heart sounds from PCG using blanket fractal dimension

Paskas, Milorad P.; Gavrovska, Ana; Reljin, Nataša

doi:10.1109/esgco.2014.6847550

Cited by 6 publications

(7 citation statements)

References 10 publications

(16 reference statements)

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“…Recurring sounds are not assumed for the classification model, and thus the methodology may overcome errors found due to nondetected candidates and similar misinterpretations. Finally, the methodology shows significant improvement of 6% higher accuracy in comparison to the methodology from [21], as presented in Table 2. The obtained ROC curves are presented in Figure 6(b) with 6.5% higher AUC value and 0.08 higher -measure.…”

Section: Discussion and Comparisonmentioning

confidence: 89%

“…When applied to the waveforms, it is described by the kernel function and regularization parameter, based on the trade-off having large normalized margin and less constraint violation. The kernel function is used to train the SVM, where the most common kernel types are the linear and the Gaussian radial basis function (RBF) described by its squared bandwidth [21,30]. SVM based classification is performed using fivefold cross-validation [28], where nine hundred sound sequences are used.…”

Section: Classification and Evaluationmentioning

confidence: 99%

“…In [20], blanket method (BFD) is used for the tidal volume estimation using tracheal sounds. 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

et al. 2017

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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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Section: Discussion and Comparisonmentioning

confidence: 89%

Section: Classification and Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…Among various fractal dimensions, in this study we used blanket fractal dimension (BFD). The BFD was initially proposed for estimating fractal dimension of digital images (2D signals) [ 38 ], and is further extended to 1D signals [ 39 ].…”

Section: Methodsmentioning

confidence: 99%

Tidal Volume Estimation Using the Blanket Fractal Dimension of the Tracheal Sounds Acquired by Smartphone

Reljin

Reyes

Chon

2015

Sensors

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In this paper, we propose the use of blanket fractal dimension (BFD) to estimate the tidal volume from smartphone-acquired tracheal sounds. We collected tracheal sounds with a Samsung Galaxy S4 smartphone, from five (N = 5) healthy volunteers. Each volunteer performed the experiment six times; first to obtain linear and exponential fitting models, and then to fit new data onto the existing models. Thus, the total number of recordings was 30. The estimated volumes were compared to the true values, obtained with a Respitrace system, which was considered as a reference. Since Shannon entropy (SE) is frequently used as a feature in tracheal sound analyses, we estimated the tidal volume from the same sounds by using SE as well. The evaluation of the performed estimation, using BFD and SE methods, was quantified by the normalized root-mean-squared error (NRMSE). The results show that the BFD outperformed the SE (at least twice smaller NRMSE was obtained). The smallest NRMSE error of 15.877% ± 9.246% (mean ± standard deviation) was obtained with the BFD and exponential model. In addition, it was shown that the fitting curves calculated during the first day of experiments could be successfully used for at least the five following days.

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“…Hurst exponent is calculated for each S1/S2 for scale range [4,32], as well as their common used features, like time duration and standard deviation. According to results presented in [18] fractal dimensions of S1/S2 may improve discrimination between S1 and S2 signals in comparison to their variance. Since both fractal dimension and variance consider globally S1/S2 variation, there is a need for introducing features related to S1/S2 signal shape.…”

Section: A Experimentsmentioning

confidence: 96%

On variance based methods in computer-aided phonocardiography

Gavrovska

Paskas

Reljin

et al. 2014

12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)

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New trends in cardiosignal processing show that local fluctuations of the signals may reveal important information for computer-aided analysis. Even though the calculation of variance as a global measure is valuable, variation described locally and in one or more scales can represent a convenient way to describe details found in a signal. In this paper, several experimental results obtained using variance based methods, such as local variance estimation and multifractal detrended fluctuation analysis (MF-DFA), are discussed in interpreting phonocardiograms and their content. The experiments show promising results for computer-aided phonocardiography that can be implemented at low cost.

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Identification of fundamental heart sounds from PCG using blanket fractal dimension

Cited by 6 publications

References 10 publications

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

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

Tidal Volume Estimation Using the Blanket Fractal Dimension of the Tracheal Sounds Acquired by Smartphone

On variance based methods in computer-aided phonocardiography

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