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A Novel Method for Screening Children with Isolated Bicuspid Aortic Valve

Gharehbaghi, Arash (author)
Mälardalens högskola,Inbyggda system
Dutoit, T. (author)
Mons University, Mons, Belgium
Sepehri, A. A. (author)
CAPIS Biomedical Research and Department Center, Mons, Belgium
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Kocharian, A. (author)
Tehran University of Medical Sciences, Tehran, Iran
Lindén, Maria (author)
Mälardalens högskola,Inbyggda system
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 (creator_code:org_t)
2015-07-28
2015
English.
In: Cardiovascular Engineering and Technology. - : Springer Science and Business Media LLC. - 1869-408X .- 1869-4098. ; 6:4, s. 546-556
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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. 

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Medicinteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Medical Engineering (hsv//eng)

Keyword

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

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ref (subject category)
art (subject category)

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Gharehbaghi, Ara ...
Dutoit, T.
Sepehri, A. A.
Kocharian, A.
Lindén, Maria
About the subject
ENGINEERING AND TECHNOLOGY
ENGINEERING AND ...
and Medical Engineer ...
Articles in the publication
Cardiovascular E ...
By the university
Mälardalen University

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