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An intelligent method for discrimination between aortic and pulmonary stenosis using phonocardiogram
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
CAPIS Biomedical Research and Department Center, Mons, Belgium.
Tehran University of Medical Sciences, Tehran, Iran .
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-1940-1747
2015 (English)In: IFMBE Proceedings, Springer, 2015, Vol. 51, 1010-1013 p.Conference paper, Published paper (Refereed)
Abstract [en]

This study presents an artificial intelligent-based method for processing phonocardiographic (PCG) signal of the patients with ejection murmur to assess the underlying pathology initiating the murmur. The method is based on our unique method for finding disease-related frequency bands in conjunction with a sophisticated statistical classifier. Children with aortic stenosis (AS), and pulmonary stenosis (PS) were the two patient groups subjected to the study, taking the healthy ones (no murmur) as the control group. PCG signals were acquired from 45 referrals to the children University hospital, comprised of 15 individuals of each group; all were diagnosed by the expert pediatric cardiologists according to the echocardiographic measurements together with the complementary tests. The accuracy of the method is evaluated to be 90% and 93.3% using the 5-fold and leave-one-out validation method, respectively. The accuracy is slightly degraded to 86.7% and 93.3% when a Gaussian noise with signal to noise ratio of 20 dB is added to the PCG signals, exhibiting an acceptable immunity against the noise. The method offered promising results to be used as a decision support system in the primary healthcare centers or clinics.

Place, publisher, year, edition, pages
Springer, 2015. Vol. 51, 1010-1013 p.
Keyword [en]
Aortic stenosis, Decision support system, Phonocardiogram, Primary healthcare centers, Pulmonary stenosis, Artificial intelligence, Biomedical engineering, Blood vessels, Classification (of information), Diseases, Frequency bands, Gaussian noise (electronic), Health care, Medicine, Phonocardiography, Signal to noise ratio, Artificial intelligent, Control groups, Intelligent method, Phonocardiograms, Primary healthcare, Statistical classifier, Decision support systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-29380DOI: 10.1007/978-3-319-19387-8_246ISI: 000381813000246Scopus ID: 2-s2.0-84944314153ISBN: 9783319193878 (print)OAI: oai:DiVA.org:mdh-29380DiVA: diva2:862774
Conference
World Congress on Medical Physics and Biomedical Engineering, 2015, 7 June 2015 through 12 June 2015
Available from: 2015-10-23 Created: 2015-10-23 Last updated: 2016-09-29Bibliographically approved

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Lindén, Maria

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