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  • 1.
    Gharehbaghi, Arash
    et al.
    School of Information Technology, Halmstad University, Halmstad, Sweden.
    Partovi, Elaheh
    Department of Electrical Engineering, Amirkabir University, Tehran, Iran.
    Babic, Ankica
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Department Information Science and Media Studies, University of Bergen, Bergen, Norway.
    Parralel Recurrent Convolutional Neural Network for Abnormal Heart Sound Classification2023Ingår i: CARING IS SHARING-EXPLOITING THE VALUE IN DATA FOR HEALTH AND INNOVATION-PROCEEDINGS OF MIE 2023, IOS PRESS , 2023, Vol. 302, s. 526-530Konferensbidrag (Refereegranskat)
    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.

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  • 2.
    Gharehbaghi, Arash
    et al.
    Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
    Partovi, Elaheh
    Department of Electrical Engineering, Amir Kabir University, Tehran, Iran.
    Babic, Ankica
    Department of Biomedical Engineering, Linköping University, Linköping, Sweden;Department of Information Science and Media Studies, University of Bergen, Norway.
    Recurrent vs Non-Recurrent Convolutional Neural Networks for Heart Sound Classification2023Ingår i: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365Artikel i tidskrift (Refereegranskat)
    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.

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