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A Novel Method for Automatic Heart Murmur Diagnosis Using Phonocardiogram

Published: 17 October 2019 Publication History

Abstract

Heart sound auscultation is a powerful and noninvasive technique for cardiac examinations. In this study, we propose a novel method for phonocardiogram (PCG) signal processing to enable automatic systolic murmur diagnosis. We formalize a series of analytical stages in this system and investigate the effectiveness of each step with real patient data. Firstly, in the heart sound segmentation step, a novel envelope is generated from short-time Fourier transform (STFT) of PCG signal to determine the positions of the first and second heart sounds. Meanwhile, the noisy cardiac cycle can be detected and deleted in this step. Then, we extract a 6 dimension feature, which can indicate the possibility of the occurrence of pathologic murmurs, by clustering the systolic STFT frames. Finally, a support vector machine (SVM) based classifier is trained to distinguish between heart sounds with and without murmurs. The proposed diagnostic system is evaluated by repeated random sub-sampling in an open PCG database and the results show a relatively high accuracy, sensitivity and specificity of 93.91±6.51%, 93.00±7.48% and 100% respectively for the detection of PCGs with systolic murmur.

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Cited By

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  • (2024)A cardiac audio classification method based on image expression of multidimensional featuresScientific Reports10.1038/s41598-024-73237-714:1Online publication date: 5-Oct-2024
  • (2022)Transfer Learning Models for Detecting Six Categories of Phonocardiogram RecordingsJournal of Cardiovascular Development and Disease10.3390/jcdd90300869:3(86)Online publication date: 16-Mar-2022
  • (2022)Health Monitoring Methods in Heart Diseases Based on Data Mining Approach: A Directional ReviewPrognostic Models in Healthcare: AI and Statistical Approaches10.1007/978-981-19-2057-8_5(115-159)Online publication date: 7-Jul-2022
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  1. A Novel Method for Automatic Heart Murmur Diagnosis Using Phonocardiogram

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    cover image ACM Other conferences
    AIAM 2019: Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing
    October 2019
    418 pages
    ISBN:9781450372022
    DOI:10.1145/3358331
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 17 October 2019

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    Author Tags

    1. aortic stenosis (AS)
    2. heart sound
    3. machine learning
    4. mitral regurgitation (MR)
    5. signal processing

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    Cited By

    View all
    • (2024)A cardiac audio classification method based on image expression of multidimensional featuresScientific Reports10.1038/s41598-024-73237-714:1Online publication date: 5-Oct-2024
    • (2022)Transfer Learning Models for Detecting Six Categories of Phonocardiogram RecordingsJournal of Cardiovascular Development and Disease10.3390/jcdd90300869:3(86)Online publication date: 16-Mar-2022
    • (2022)Health Monitoring Methods in Heart Diseases Based on Data Mining Approach: A Directional ReviewPrognostic Models in Healthcare: AI and Statistical Approaches10.1007/978-981-19-2057-8_5(115-159)Online publication date: 7-Jul-2022
    • (2021)Deep Learning Methods for Heart Sounds Classification: A Systematic ReviewEntropy10.3390/e2306066723:6(667)Online publication date: 26-May-2021
    • (2021)A novel embedded system design for the detection and classification of cardiac disordersComputational Intelligence10.1111/coin.1246937:4(1844-1864)Online publication date: 4-Jun-2021

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