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Intelligent phonocardiography for screening ventricular septal defect using time growing neural network
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
CAPIS Biomedical Research and Development Center, Mon, Belgium.
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0003-1940-1747
Linköping University, Sweden.
2017 (engelsk)Inngår i: Studies in Health Technology and Informatics, vol 238, IOS Press , 2017, Vol. 238, s. 108-111Kapittel i bok, del av antologi (Fagfellevurdert)
Abstract [en]

This paper presents results of a study on the applicability of the intelligent phonocardiography in discriminating between Ventricular Spetal Defect (VSD) and regurgitation of the atrioventricular valves. An original machine learning method, based on the Time Growing Neural Network (TGNN), is employed for classifying the phonocardiographic recordings collected from the pediatric referrals to a children hospital. 90 individuals, 30 VSD, 30 with the valvular regurgitation, and 30 healthy subjects, participated in the study after obtaining the informed consents. The accuracy and sensitivity of the approach is estimated to be 86.7% and 83.3%, respectively, showing a good performance to be used as a decision support system. .

sted, utgiver, år, opplag, sider
IOS Press , 2017. Vol. 238, s. 108-111
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URN: urn:nbn:se:mdh:diva-36151DOI: 10.3233/978-1-61499-781-8-108ISI: 000452510200026Scopus ID: 2-s2.0-85022220015ISBN: 9781614997801 (tryckt)OAI: oai:DiVA.org:mdh-36151DiVA, id: diva2:1128708
Tilgjengelig fra: 2017-07-27 Laget: 2017-07-27 Sist oppdatert: 2018-12-27bibliografisk kontrollert

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