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A hybrid machine learning method for detecting cardiac ejection murmurs
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
CAPIS Biomedical Research and Department Center, Mons, Belgium.
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0003-1940-1747
Linköping University, Sweden.
2017 (engelsk)Inngår i: IFMBE Proceedings, Springer Verlag , 2017, s. 787-790Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This paper presents a novel method for detecting cardiac ejection murmurs from other pathological and physiological heart murmurs in children. The proposed method combines a hybrid model and a time growing neural network for an improved detection even in mild condition. Children with aortic stenosis and pulmonary stenosis comprised the patient category against the reference category containing mitral regurgitation, ventricular septal defect, innocent murmur and normal (no murmur) conditions. In total, 120 referrals to a children University hospital participated to the study after giving their informed consent. Confidence interval of the accuracy, sensitivity and specificity is estimated to be 87.2% ̶ 88.8%, 83.4% ̶ 86.9% and 88.3% ̶ 90.0%, respectively. 

sted, utgiver, år, opplag, sider
Springer Verlag , 2017. s. 787-790
HSV kategori
Identifikatorer
URN: urn:nbn:se:mdh:diva-36136DOI: 10.1007/978-981-10-5122-7_197ISI: 000449778900197Scopus ID: 2-s2.0-85021746804ISBN: 9789811051210 (tryckt)OAI: oai:DiVA.org:mdh-36136DiVA, id: diva2:1128721
Konferanse
Joint Conference of the European Medical and Biological Engineering Conference, EMBEC 2017 and Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2107, 11 June 2017 through 15 June 2017
Tilgjengelig fra: 2017-07-27 Laget: 2017-07-27 Sist oppdatert: 2018-11-29bibliografisk kontrollert

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