{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T18:13:03Z","timestamp":1760551983222,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"print","value":"9781643684000"},{"type":"electronic","value":"9781643684017"}],"license":[{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,29]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3233\/shti230525","type":"book-chapter","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T07:53:41Z","timestamp":1688111621000},"source":"Crossref","is-referenced-by-count":2,"title":["Recurrent vs Non-Recurrent Convolutional Neural Networks for Heart Sound Classification"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3413-2859","authenticated-orcid":false,"given":"Arash","family":"Gharehbaghi","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Link\u00f6ping University, Link\u00f6ping, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elaheh","family":"Partovi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Amir Kabir University, Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7532-6828","authenticated-orcid":false,"given":"Ankica","family":"Babic","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Link\u00f6ping University, Link\u00f6ping, Sweden"},{"name":"Department of Information Science and Media Studies, University of Bergen, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Healthcare Transformation with Informatics and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI230525","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T07:53:43Z","timestamp":1688111623000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI230525"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,29]]},"ISBN":["9781643684000","9781643684017"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti230525","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2023,6,29]]}}}