An AI-Powered Tool for Automatic Heart Sound Quality Assessment and Segmentation

Valentina Roquemen-Echeverri, Peter G. Jacobs, Stephen Heitner, Peter M. Schulman, Bethany Wilson, Jorge Mahecha, Clara Mosquera-Lopez

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Objective: To design an AI-powered tool to automatically assess the quality of phonocardiogram (PCG) recordings, and then identify S1 and S2 heart sounds using PCG recordings only. Methods We used PCG recordings from two datasets; a publicly available dataset (the 2016 PhysioNet/CinC Challenge), and a dataset that we collected as part of a clinical study we are conducting at Oregon Health Science University (OHSU). We developed a logistic regression classifier to score PCG signal quality using semi-supervised learning and a two-layer perceptron artificial neural network classifier with con textual time-and frequency-domain input features to detect fundamental S1 and S2 heart sounds. We also analyzed the impact of input features on the accuracy of S1 and S2 segmentation. Results: Our segmentation method detects fundamental S1 and S2 heart sounds with a precision of 93% and distinguishes S1/S2 heart sounds with area under the curve (AUC) of 97.1%. Conclusions: Implementing a signal quality assessment tool allows for better segmentation performance as only suitable signals are processed by the S1/S2 sound detection and classification algorithms. Distance between sounds in time-domain are able to distinguish between S1 and S2 with accuracy of 87.4%; however, by adding the frequency-domain features, the accuracy significantly improved to 9 2.4%. Significance: S1 an d S2 heart sound segmentation is the first step in the processes of detecting and classifying heart abnormalities from a PCG. Our proposed method is simple and effective for segmentation for this task. Consequently, it can facilitate the performance of subsequent tasks including the detection of heart murmurs.

    Original languageEnglish (US)
    Title of host publicationProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
    EditorsYufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages3065-3074
    Number of pages10
    ISBN (Electronic)9781665401265
    DOIs
    StatePublished - 2021
    Event2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Virtual, Online, United States
    Duration: Dec 9 2021Dec 12 2021

    Publication series

    NameProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

    Conference

    Conference2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
    Country/TerritoryUnited States
    CityVirtual, Online
    Period12/9/2112/12/21

    Keywords

    • artificial neural networks (ANN)
    • heart sound segmentation
    • machine learning
    • multi-layer perceptron (MLP)
    • phonocardiogram (PCG)

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Science Applications
    • Biomedical Engineering
    • Health Informatics
    • Information Systems and Management

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