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Recurrent vs Non-Recurrent Convolutional Neural Networks for Heart Sound Classification
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- Gharehbaghi, Arash (författare)
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
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- Partovi, Elaheh (författare)
- Department of Electrical Engineering, Amir Kabir University, Tehran, Iran
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- Babic, Ankica (författare)
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden;Department of Information Science and Media Studies, University of Bergen, Norway
- (creator_code:org_t)
- 2023
- 2023
- Engelska.
- Ingår i: Studies in Health Technology and Informatics. - 0926-9630 .- 1879-8365.
- Tidskriftsartikel (refereegranskat)
Abstract
Ämnesord
Stäng
- 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.
Nyckelord
- Heart sound; convolutional neural network; deep learning; intelligent phonocardiography
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- art (ämneskategori)
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