dc.contributor.author | Gharehbaghi, Arash | |
dc.contributor.author | Partovi, Elaheh | |
dc.contributor.author | Babic, Ankica | |
dc.date.accessioned | 2023-09-11T11:44:15Z | |
dc.date.available | 2023-09-11T11:44:15Z | |
dc.date.created | 2023-08-28T14:05:21Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 0926-9630 | |
dc.identifier.uri | https://hdl.handle.net/11250/3088650 | |
dc.description.abstract | 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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IOS Press | en_US |
dc.rights | Navngivelse-Ikkekommersiell 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/deed.no | * |
dc.title | Recurrent vs Non-Recurrent Convolutional Neural Networks for Heart Sound Classification | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | Copyright 2023 The authors and IOS Press | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.doi | 10.3233/SHTI230525 | |
dc.identifier.cristin | 2170253 | |
dc.source.journal | Studies in Health Technology and Informatics | en_US |
dc.source.pagenumber | 436-439 | en_US |
dc.identifier.citation | Studies in Health Technology and Informatics. 2023, 305, 436-439. | en_US |
dc.source.volume | 305 | en_US |