{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:27:03Z","timestamp":1772119623320,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683881","type":"print"},{"value":"9781643683898","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T00:00:00Z","timestamp":1684368000000},"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":[[2023,5,18]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3233\/shti230198","type":"book-chapter","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T08:45:26Z","timestamp":1684485926000},"source":"Crossref","is-referenced-by-count":4,"title":["Parralel Recurrent Convolutional Neural Network for Abnormal Heart Sound Classification"],"prefix":"10.3233","author":[{"given":"Arash","family":"Gharehbaghi","sequence":"first","affiliation":[{"name":"School of Information Technology, Halmstad University, Halmstad, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elaheh","family":"Partovi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Amirkabir University, Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ankica","family":"Babic","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Link\u00f6ping University, Link\u00f6ping, Sweden"},{"name":"Department Information Science and Media Studies, University of Bergen, Bergen, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Caring is Sharing \u2013 Exploiting the Value in Data for Health and Innovation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI230198","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T14:59:06Z","timestamp":1685545146000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI230198"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,18]]},"ISBN":["9781643683881","9781643683898"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti230198","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,18]]}}}