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Related Concept Videos

Heart Sounds01:15

Heart Sounds

4.4K
Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
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Semi-automated Optical Heartbeat Analysis of Small Hearts
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Pediatric heart sound segmentation using hidden Markov model.

Pouye Sedighian, Andrew W Subudhi, Fabien Scalzo

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 9, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study presents a new algorithm for automatic pediatric heart sound segmentation using homomorphic filtering and Hidden Markov Models (HMMs). The method accurately identifies heart sound components, outperforming existing techniques.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Pediatric Cardiology

    Background:

    • Digital stethoscopes enable automatic cardiac auscultation, necessitating advanced algorithms for heart sound segmentation.
    • Pediatric heart sound segmentation is complex due to respiratory influences and other confounding factors.

    Purpose of the Study:

    • To investigate the efficacy of homomorphic filtering and Hidden Markov Models (HMMs) for segmenting pediatric heart sounds.
    • To develop an accurate and computationally efficient algorithm for automatic pediatric heart sound analysis.

    Main Methods:

    • Application of homomorphic filtering combined with Hidden Markov Models (HMMs).
    • Evaluation on the publicly available Pascal Challenge dataset.
    • Comparative analysis against three existing segmentation methods.

    Main Results:

    • Achieved 92.4%±1.1% accuracy for first heart sound component identification.
    • Achieved 93.5%±1.1% accuracy for second heart sound component identification.
    • Demonstrated superiority over existing methods in accuracy and/or computational complexity.

    Conclusions:

    • The proposed homomorphic filtering and HMM approach is effective for pediatric heart sound segmentation.
    • This method offers a promising solution for automated analysis of pediatric cardiac auscultation.
    • The algorithm provides accurate and efficient segmentation, addressing key challenges in pediatric cardiology.