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Recurrent vs Non-Recurrent Convolutional Neural Networks for Heart Sound Classification
Department of Biomedical Engineering, Linköping University, Linköping, Sweden.ORCID iD: 0000-0002-3413-2859
Department of Electrical Engineering, Amir Kabir University, Tehran, Iran.
Department of Biomedical Engineering, Linköping University, Linköping, Sweden;Department of Information Science and Media Studies, University of Bergen, Norway.ORCID iD: 0000-0002-7532-6828
2023 (English)In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365Article in journal (Refereed) Published
Abstract [en]

Convolutional Neural Network (CNN) has been widely proposed for different tasks of heart sound analysis. This paper presents the results of a novel study on the performance of a conventional CNN in comparison to the different architectures of recurrent neural networks combined with CNN for the classification task of abnormal-normal heart sounds. The study considers various combinations of parallel and cascaded integration of CNN with Gated Recurrent Network (GRN) as well as Long- Short Term Memory (LSTM) and explores the accuracy and sensitivity of each integration independently, using the Physionet dataset of heart sound recordings. The accuracy of the parallel architecture of LSTM-CNN reached 98.0% outperforming all the combined architectures, with a sensitivity of 87.2%. The conventional CNN offered sensitivity/accuracy of 95.9%/97.3% with far less complexity. Results show that a conventional CNN can appropriately perform and solely employed for the classification of heart sound signals.

Place, publisher, year, edition, pages
2023.
Keywords [en]
Heart sound; convolutional neural network; deep learning; intelligent phonocardiography
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-197240DOI: 10.3233/shti230525OAI: oai:DiVA.org:liu-197240DiVA, id: diva2:1792372
Available from: 2023-08-29 Created: 2023-08-29 Last updated: 2023-09-08

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Gharehbaghi, ArashBabic, Ankica
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  • de-DE
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  • en-US
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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