A generating model of realistic synthetic heart sounds for performance assessment of phonocardiogram processing algorithms

Jabloun, Meryem; Ravier, Philippe; Buttelli, Olivier; Lédée, Roger; Harba, Rachid; Nguyen, Long-Dang

doi:10.1016/j.bspc.2013.01.002

Cited by 20 publications

(10 citation statements)

References 24 publications

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“…Mechanical actions of heart valves produce heart sounds including fundamental heart sounds (FHSs), S 1 followed by S 2 [16]- [18]. The first heart sound (S 1 ) is heard at the onset of the systolic phase.…”

Section: A Fundamental Heart Sounds -S 1 and Smentioning

confidence: 99%

Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review

2019

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Cardiovascular diseases currently pose the highest threat to human health around the world. Proper investigation of the abnormalities in heart sounds is known to provide vital clinical information that can assist in the diagnosis and management of cardiac conditions. However, despite significant advances in the development of algorithms for automated classification and analysis of heart sounds, the validity of different approaches has not been systematically reviewed. This paper provides an in-depth systematic review and critical analysis of all the existing approaches for automatic identification and classification of the heart sounds. All statements on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2009 Checklist were followed and addressed thoroughly to maintain the quality of the accounted systematic review. Out of 1347 research articles available in the academic databases from 1963 to 2018, 117 peerreviewed articles were found to fall under the search and selection criteria of this paper. Amongst them: 53 articles are focused on segmentation, 72 of the studies are related to the feature extraction approaches and 88 to classification, and 56 reported on the databases and heart sounds acquisition. From this review, it is clear that, although a lot of research has been done in the field of automated analysis, there is still some work to be done to develop robust methods for identification and classification of various events in the cardiac cycle so that this could be effectively used to improve the diagnosis and management of cardiovascular diseases in combination with the wearable mobile technologies.

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Section: A Fundamental Heart Sounds -S 1 and Smentioning

confidence: 99%

Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review

2019

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“…Among the existing PCG models, the ones based on Gabor and Laplace kernels [18,19,7,8,10] seem to be more realistic. The Gabor kernel, which is actually a Gaussian-damped sinusoidal wave, offers the possibility, through five adjustable parameters ( and )…”

Section: B Established Pcg Modelmentioning

confidence: 99%

Cyclic Analysis of Phonocardiogram Signals

Choklati¹,

Sabri²,

Lahlimi³

2017

IJIGSP

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Abstract-Acoustic vibrations of the heart in time domain correspond to phonocardiogram (PCG) signal. A PCG signal, in the healthy case, consists of two fundamental sounds s 1 and s 2 produced by the mechanical functioning of the heart. Abnormalities in the heart valves correspond to other cardiac sounds than s 1 and s 2 . This makes PCG signal a valuable tool related to the track of heart diseases. Actually, the characterization and the analysis of PCG signals is being a fertile area of study and investigation. However, most of the topics which treated this area of research focused only on timefrequency analysis, without exploiting the periodic character of PCG signal due to the limitations of the PCG modeling. In this work, we propose a coherent mathematical model for PCG signals based on cyclostationarity and Gabor kernel. The motivation behind is to define a framework, utilizing cyclic statistic due to noise robustness, for a full description of PCG signals, which leads to an easy and efficient early identification of certain heart abnormalities. The validation of the proposed model and its capacity to reflect the heart functioning is tested over synthetic and real data sets.

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“…Almost researchers focused on the modeling and analysis of the electrocardiogram (ECG) signals over several years [6][7][8]. In contrast, a few studies proposed models for representing the PCG signals [9,10]. However, the analysis of heart sounds and Proceedings of the 10 th ICEENG Conference, 19-21 April, 2016 EE019 -3 murmurs using DWT decompositions were widely investigated in the literature [4,11,12].…”

Section: B Heart Signals Simulationmentioning

confidence: 99%

A Microcontroller-Based Heart Sounds and Murmurs Generator Using Discrete Wavelet Transform

Karar

2016

The International Conference on Electrical Engineering

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A generating model of realistic synthetic heart sounds for performance assessment of phonocardiogram processing algorithms

Cited by 20 publications

References 24 publications

Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review

Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review

Cyclic Analysis of Phonocardiogram Signals

A Microcontroller-Based Heart Sounds and Murmurs Generator Using Discrete Wavelet Transform

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