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Extraction of diagnostic information from phonocardiographic signal using time-growing neural network
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
CAPIS Biomedical Research and Development Centre, Mon, Belgium.
2019 (English)In: IFMBE Proceedings, Springer Verlag , 2019, 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 Verlag , 2019. no 3, p. 849-853
Keywords [en]
Deep time-growing neural network, Intelligent phonocardiography, Time-growing neural network, Artificial intelligence, Biomedical engineering, Classification (of information), Data acquisition, Diagnosis, Phonocardiography, Binary classifiers, Good clinical practices, Helsinki, Medical center, Medical information, PCG signal, Primary healthcare, Reference group, Decision support systems
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-39977DOI: 10.1007/978-981-10-9023-3_153Scopus ID: 2-s2.0-85048288883OAI: oai:DiVA.org:mdh-39977DiVA, id: diva2:1222427
Conference
World Congress on Medical Physics and Biomedical Engineering, WC 2018, 3 June 2018 through 8 June 2018
Available from: 2018-06-21 Created: 2018-06-21 Last updated: 2018-06-21Bibliographically approved

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Ghareh Baghi, Arash

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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Output format
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  • asciidoc
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