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Intelligent phonocardiography for screening ventricular septal defect using time growing neural network
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
CAPIS Biomedical Research and Development Center, Mon, Belgium.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-1940-1747
Linköping University, Sweden.
2017 (English)In: Studies in Health Technology and Informatics, vol 238, IOS Press , 2017, Vol. 238, 108-111 p.Chapter in book (Refereed)
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. .

Place, publisher, year, edition, pages
IOS Press , 2017. Vol. 238, 108-111 p.
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-36151DOI: 10.3233/978-1-61499-781-8-108Scopus ID: 2-s2.0-85022220015ISBN: 9781614997801 OAI: oai:DiVA.org:mdh-36151DiVA: diva2:1128708
Available from: 2017-07-27 Created: 2017-07-27 Last updated: 2017-07-27Bibliographically approved

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Lindén, Maria

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Citation style
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  • de-DE
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  • nn-NB
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  • Other locale
More languages
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