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Structural Risk Evaluation of a Deep Neural Network and a Markov Model in Extracting Medical Information from Phonocardiography
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Department of Biomedical Engineering, Linköping University, Sweden.
2018 (English)In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 251, p. 157-160Article in journal (Refereed) Published
Abstract [en]

This paper presents a method for exploring structural risk of any artificial intelligence-based method in bioinformatics, the A-Test method. This method provides a way to not only quantitate the structural risk associated with a classification method, but provides a graphical representation to compare the learning capacity of different classification methods. Two different methods, Deep Time Growing Neural Network (DTGNN) and Hidden Markov Model (HMM), are selected as two classification methods for comparison. Time series of heart sound signals are employed as the case study where the classifiers are trained to learn the disease-related changes. Results showed that the DTGNN offers a superior performance both in terms of the capacity and the structural risk. The A-Test method can be especially employed in comparing the learning methods with small data size.

Place, publisher, year, edition, pages
IOS Press , 2018. Vol. 251, p. 157-160
Keywords [en]
A-Test method, deep time growing neural network, heart sounds, intelligent phonocardiography
National Category
Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:mdh:diva-40266DOI: 10.3233/978-1-61499-880-8-157Scopus ID: 2-s2.0-85049594861ISBN: 9781614998792 OAI: oai:DiVA.org:mdh-40266DiVA, id: diva2:1233693
Available from: 2018-07-19 Created: 2018-07-19 Last updated: 2018-07-19Bibliographically approved

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CiteExportLink to record
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  • nn-NB
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  • Other locale
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  • asciidoc
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