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Extraction of Diagnostic Information from Phonocardiographic Signal Using Time-Growing Neural Network
Malardalen Univ, Sweden.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Univ Bergen, Norway.
CAPIS Biomed Res and Dev Ctr, Belgium.
2019 (English)In: WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING 2018, VOL 3, SPRINGER , 2019, Vol. 68, no 3, p. 849-853Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents an original method for extracting medical information from a heart sound recording, so called Phonocardiographic (PCG) signal. The extracted information is employed by a binary classifier to distinguish between stenosis and regurgitation murmurs. The method is based on using our original neural network, the Time-Growing Neural Network (TGNN), in an innovative way. Children with an obstruction on their semilunar valve are considered as the patient group (PG) against a reference group (RG) of children with a regurgitation in their atrioventricular valve. PCG signals were collected from 55 children, 25/30 from the PG/RG, who referred to the Children Medical Center of Tehran University. The study was conducted according to the guidelines of Good Clinical Practices and the Declaration of Helsinki. Informed consents were obtained for all the patients prior to the data acquisition. The accuracy and sensitivity of the method was estimated to be 85% and 80% respectively, exhibiting a very good performance to be used as a part of decision support system. Such a decision support system can improve the screening accuracy in primary healthcare centers, thanks to the innovative use of TGNN.

Place, publisher, year, edition, pages
SPRINGER , 2019. Vol. 68, no 3, p. 849-853
Series
IFMBE Proceedings, ISSN 1680-0737
Keywords [en]
Intelligent phonocardiography; Time-growing neural network; Deep time-growing neural network
National Category
Biomedical Laboratory Science/Technology
Identifiers
URN: urn:nbn:se:liu:diva-153145DOI: 10.1007/978-981-10-9023-3_153ISI: 000449744300153ISBN: 978-981-10-9023-3 (electronic)ISBN: 978-981-10-9022-6 (print)OAI: oai:DiVA.org:liu-153145DiVA, id: diva2:1267341
Conference
IUPESM World Congress on Medical Physics and Biomedical Engineering
Note

Funding Agencies|CAPIS Inc., Mons, Belgium; KKS

Available from: 2018-12-01 Created: 2018-12-01 Last updated: 2018-12-01

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • Other style
More styles
Language
  • de-DE
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  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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