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  • Gharehbaghi, ArashMälardalens högskola,Inbyggda system (author)

A Novel Method for Screening Children with Isolated Bicuspid Aortic Valve

  • Article/chapterEnglish2015

Publisher, publication year, extent ...

  • 2015-07-28
  • Springer Science and Business Media LLC,2015
  • printrdacarrier

Numbers

  • LIBRIS-ID:oai:DiVA.org:mdh-29752
  • https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-29752URI
  • https://doi.org/10.1007/s13239-015-0238-6DOI

Supplementary language notes

  • Language:English
  • Summary in:English

Part of subdatabase

Classification

  • Subject category:ref swepub-contenttype
  • Subject category:art swepub-publicationtype

Notes

  • 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 and genre

  • TEKNIK OCH TEKNOLOGIER Medicinteknik hsv//swe
  • ENGINEERING AND TECHNOLOGY Medical Engineering hsv//eng
  • 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

Added entries (persons, corporate bodies, meetings, titles ...)

  • Dutoit, T.Mons University, Mons, Belgium (author)
  • Sepehri, A. A.CAPIS Biomedical Research and Department Center, Mons, Belgium (author)
  • Kocharian, A.Tehran University of Medical Sciences, Tehran, Iran (author)
  • Lindén, MariaMälardalens högskola,Inbyggda system(Swepub:mdh)mln04 (author)
  • Mälardalens högskolaInbyggda system (creator_code:org_t)

Related titles

  • In:Cardiovascular Engineering and Technology: Springer Science and Business Media LLC6:4, s. 546-5561869-408X1869-4098

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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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