Rheumatic Heart Disease Detection Using Deep Learning from Spectro-Temporal Representation of Un-segmented Heart Sounds

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  • Additional Information
    • Publication Information:
      IEEE
    • Publication Date:
      2020
    • Abstract:
      Rheumatic Heart Disease (RHD) is an autoimmune response to a bacterial attack which deteriorates the normal functioning of the heart valves. The damage on the valves affects the normal blood flow inside the heart chambers which can be recorded and listened to via a stethoscope as a phonocardiogram. However, the manual method of auscultation is difficult, time consuming and subjective. In this study, a convolutional neural network based deep learning algorithm is used to perform an automatic auscultation and it classifies the heart sound as normal and rheumatic. The classification is done on un-segmented data where the extraction of the first, the second and systolic and diastolic heart sounds are not required. The architecture of the CNN network is formed as an array of layers. Convolutional and batch normalization layers followed by a max pooling layer to down sample the feature maps are used. At the end there is a final max pooling layer which pools the input feature map globally over time and at the end a fully connected layer is included. The network has five convolutional layers. This current work illustrates the use of deep convolutional neural network using a Mel Spectro-temporal representation. For this current study, an RHD heart sound data set is recorded from one hundred seventy subjects from whom one hundred twenty four are confirmed RHD patients. The system has an overall accuracy of 96.1% with 94.0% sensitivity and 98.1% and specificity.
    • Contents Note:
      Conference Acronym: EMBC
    • Author Affiliations:
      KU Leuven,eMedia research, Campus GroepT, KU Leuven, Leuven, Belgium and STADIUS,Leuven,Belgium
    • ISBN:
      978-1-7281-1990-8
    • ISSN:
      1558-4615
    • Relation:
      2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society
    • Accession Number:
      10.1109/EMBC44109.2020.9176544
    • Rights:
      Copyright 2020, IEEE
    • AMSID:
      9176544
    • Conference Acronym:
      EMBC
    • Date of Current Version:
      2020
    • Document Subtype:
      IEEE Conference
    • Notes:
      Conference Location: Montreal, QC, Canada, Canada

      Conference Start Date: 20 July 2020

      Conference End Date: 24 July 2020
    • Accession Number:
      edseee.9176544
  • Citations
    • ABNT:
      ASMARE, M. H. et al. Rheumatic Heart Disease Detection Using Deep Learning from Spectro-Temporal Representation of Un-segmented Heart Sounds. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Engineering in Medicine & Biology Society (EMBC), 2020 42nd Annual International Conference of the IEEE, [s. l.], p. 168–171, 2020. DOI 10.1109/EMBC44109.2020.9176544. Disponível em: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edseee&AN=edseee.9176544. Acesso em: 23 out. 2020.
    • AMA:
      Asmare MH, Woldehanna F, Janssens L, Vanrumste B. Rheumatic Heart Disease Detection Using Deep Learning from Spectro-Temporal Representation of Un-segmented Heart Sounds. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Engineering in Medicine & Biology Society (EMBC), 2020 42nd Annual International Conference of the IEEE. July 2020:168-171. doi:10.1109/EMBC44109.2020.9176544
    • APA:
      Asmare, M. H., Woldehanna, F., Janssens, L., & Vanrumste, B. (2020). Rheumatic Heart Disease Detection Using Deep Learning from Spectro-Temporal Representation of Un-segmented Heart Sounds. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Engineering in Medicine & Biology Society (EMBC), 2020 42nd Annual International Conference of the IEEE, 168–171. https://doi.org/10.1109/EMBC44109.2020.9176544
    • Chicago/Turabian: Author-Date:
      Asmare, Melkamu Hunegnaw, Frehiwot Woldehanna, Luc Janssens, and Bart Vanrumste. 2020. “Rheumatic Heart Disease Detection Using Deep Learning from Spectro-Temporal Representation of Un-Segmented Heart Sounds.” 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Engineering in Medicine & Biology Society (EMBC), 2020 42nd Annual International Conference of the IEEE, July, 168–71. doi:10.1109/EMBC44109.2020.9176544.
    • Harvard:
      Asmare, M. H. et al. (2020) ‘Rheumatic Heart Disease Detection Using Deep Learning from Spectro-Temporal Representation of Un-segmented Heart Sounds’, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Engineering in Medicine & Biology Society (EMBC), 2020 42nd Annual International Conference of the IEEE, pp. 168–171. doi: 10.1109/EMBC44109.2020.9176544.
    • Harvard: Australian:
      Asmare, MH, Woldehanna, F, Janssens, L & Vanrumste, B 2020, ‘Rheumatic Heart Disease Detection Using Deep Learning from Spectro-Temporal Representation of Un-segmented Heart Sounds’, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Engineering in Medicine & Biology Society (EMBC), 2020 42nd Annual International Conference of the IEEE, pp. 168–171, viewed 23 October 2020, .
    • MLA:
      Asmare, Melkamu Hunegnaw, et al. “Rheumatic Heart Disease Detection Using Deep Learning from Spectro-Temporal Representation of Un-Segmented Heart Sounds.” 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Engineering in Medicine & Biology Society (EMBC), 2020 42nd Annual International Conference of the IEEE, July 2020, pp. 168–171. EBSCOhost, doi:10.1109/EMBC44109.2020.9176544.
    • Chicago/Turabian: Humanities:
      Asmare, Melkamu Hunegnaw, Frehiwot Woldehanna, Luc Janssens, and Bart Vanrumste. “Rheumatic Heart Disease Detection Using Deep Learning from Spectro-Temporal Representation of Un-Segmented Heart Sounds.” 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Engineering in Medicine & Biology Society (EMBC), 2020 42nd Annual International Conference of the IEEE, July 1, 2020, 168–71. doi:10.1109/EMBC44109.2020.9176544.
    • Vancouver/ICMJE:
      Asmare MH, Woldehanna F, Janssens L, Vanrumste B. Rheumatic Heart Disease Detection Using Deep Learning from Spectro-Temporal Representation of Un-segmented Heart Sounds. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Engineering in Medicine & Biology Society (EMBC), 2020 42nd Annual International Conference of the IEEE [Internet]. 2020 Jul 1 [cited 2020 Oct 23];168–71. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edseee&AN=edseee.9176544