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Cardi-Net: A Deep Neural Network for classification of Cardiac disease using Phonocardiogram Signal

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F20%3APU143882" target="_blank" >RIV/00216305:26220/20:PU143882 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.sciencedirect.com/science/article/abs/pii/S0169260720315832" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0169260720315832</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.cmpb.2022.106727" target="_blank" >10.1016/j.cmpb.2022.106727</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Cardi-Net: A Deep Neural Network for classification of Cardiac disease using Phonocardiogram Signal

  • Original language description

    Background and objectives The lack of medical facilities in isolated areas makes many patients remain aloof from quick and timely diagnosis of cardiovascular diseases, leading to high mortality rates. A deep learning based method for automatic diagnosis of multiple cardiac diseases from Phonocardiogram (PCG) signals is proposed in this paper. Methods The proposed system is a combination of deep learning based convolutional neural network (CNN) and power spectrogram Cardi-Net, which can extract deep discriminating features of PCG signals from the power spectrogram to identify the diseases. The choice of Power Spectral Density (PSD) makes the model extract highly discriminatory features significant for the multi-classification of four common cardiac disorders. Results Data augmentation techniques are applied to make the model robust, and the model undergoes 10-fold cross-validation to yield an overall accuracy of 98.879% on the test dataset to diagnose multi heart diseases from PCG signals. Conclusion The proposed model is completely automatic, where signal pre-processing and feature engineering are not required. The conversion time of power spectrogram from PCG signals is very low range from 0.10 sec to 0.11 sec. This reduces the complexity of the model, making it highly reliable and robust for real-time applications. The proposed architecture can be deployed on cloud and a low cost processor, desktop, android app leading to proper access to the dispensaries in remote areas.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20201 - Electrical and electronic engineering

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

  • ISSN

    0169-2607

  • e-ISSN

    1872-7565

  • Volume of the periodical

    219

  • Issue of the periodical within the volume

    1

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    11

  • Pages from-to

    1-11

  • UT code for WoS article

    000778631600004

  • EID of the result in the Scopus database

    2-s2.0-85126586158