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Parralel Recurrent Convolutional Neural Network for Abnormal Heart Sound Classification
School of Information Technology, Halmstad University, Halmstad, Sweden.ORCID iD: 0000-0002-3413-2859
Department of Electrical Engineering, Amirkabir University, Tehran, Iran.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Department Information Science and Media Studies, University of Bergen, Bergen, Norway.
2023 (English)In: CARING IS SHARING-EXPLOITING THE VALUE IN DATA FOR HEALTH AND INNOVATION-PROCEEDINGS OF MIE 2023, IOS PRESS , 2023, Vol. 302, p. 526-530Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents the results of a study performed on Parallel Convolutional Neural Network (PCNN) toward detecting heart abnormalities from the heart sound signals. The PCNN preserves dynamic contents of the signal in a parallel combination of the recurrent neural network and a Convolutional Neural Network (CNN). The performance of the PCNN is evaluated and compared to the one obtained from a Serial form of the Convolutional Neural Network (SCNN) as well as two other baseline studies: a Long- and Short-Term Memory (LSTM) neural network and a Conventional CNN (CCNN). We employed a well-known public dataset of heart sound signals: the Physionet heart sound. The accuracy of the PCNN, was estimated to be 87.2% which outperforms the rest of the three methods: the SCNN, the LSTM, and the CCNN by 12%, 7%, and 0.5%, respectively. The resulting method can be easily implemented in an Internet of Things platform to be employed as a decision support system for the screening heart abnormalities.

Place, publisher, year, edition, pages
IOS PRESS , 2023. Vol. 302, p. 526-530
Series
Studies in Health Technology and Informatics, ISSN 0926-9630
Keywords [en]
Heart sound; convolutional neural networks; deep learning; intelligent phonocardiography; parallel convolutional neural network
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:liu:diva-197239DOI: 10.3233/shti230198ISI: 001071432900141PubMedID: 37203741ISBN: 9781643683898 (print)OAI: oai:DiVA.org:liu-197239DiVA, id: diva2:1792366
Conference
33rd Medical Informatics Europe Conference (MIE) - Caring is Sharing - Exploiting the Value in Data for Health and Innovation, European Federat Med Informat, Gothenburg, SWEDEN, may 22-25, 2023
Available from: 2023-08-29 Created: 2023-08-29 Last updated: 2024-01-22

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Gharehbaghi, ArashBabic, Ankica

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CiteExportLink to record
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Output format
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