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A Novel Method for Screening Children with Isolated Bicuspid Aortic Valve
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mons University, Mons, Belgium .
CAPIS Biomedical Research and Department Center, Mons, Belgium.
Tehran University of Medical Sciences, Tehran, Iran.
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2015 (English)In: Cardiovascular Engineering and Technology, ISSN 1869-408X, E-ISSN 1869-4098, Vol. 6, no 4, 546-556 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents a novel processing method for heart sound signal: the statistical time growing neural network (STGNN). The STGNN performs a robust classification by merging supervised and unsupervised statistical methods to overcome non-stationary behavior of the signal. By combining available preprocessing and segmentation techniques and the STGNN classifier, we build an automatic tool for screening children with isolated BAV, the congenital heart malformation which can lead to serious cardiovascular lesions. Children with BAV (22 individuals) and healthy condition (28 individuals) are subjected to the study. The performance of the STGNN is compared to that of a time growing neural network (CTGNN) and a conventional support vector (CSVM) machine, using balanced repeated random sub sampling. The average of the accuracy/sensitivity is estimated to be 87.4/86.5 for the STGNN, 81.8/83.4 for the CTGNN, and 72.9/66.8 for the CSVM. Results show that the STGNN offers better performance and provides more immunity to the background noise as compared to the CTGNN and CSVM. The method is implementable in a computer system to be employed in primary healthcare centers to improve the screening accuracy. 

Place, publisher, year, edition, pages
2015. Vol. 6, no 4, 546-556 p.
Keyword [en]
Artificial neural network, Bicuspid aortic valve, Intelligent phonocardiogram, Pediatric heart disease, Phonocardiogram, Support vector machine, Time growing neural network, Artificial heart, Blood vessels, Cardiology, Diagnosis, Medical computing, Neural networks, Phonocardiography, Support vector machines, Bicuspid aortic valves, Heart disease, Heart sound signal, Non-stationary behaviors, Phonocardiograms, Primary healthcare, Robust classification, Segmentation techniques, Biomedical signal processing
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-29752DOI: 10.1007/s13239-015-0238-6ISI: 000380357700011Scopus ID: 2-s2.0-84946904464OAI: oai:DiVA.org:mdh-29752DiVA: diva2:874626
Available from: 2015-11-27 Created: 2015-11-27 Last updated: 2017-12-01Bibliographically approved

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Lindén, Maria

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