Open this publication in new window or tab >>2020 (English)In: Digital Personalized Health and Medicine / [ed] Louise B. Pape-Haugaard, Christian Lovis, Inge Cort Madsen, Patrick Weber, Per Hostrup Nielsen, Philip Scott, IOS Press , 2020, Vol. 270, p. 178-182Conference paper, Published paper (Refereed)
Abstract [en]
This paper presents an original machine learning method for extracting diagnostic medical information from heart sound recordings. The method is proposed to be integrated with an intelligent phonocardiography in order to enhance diagnostic value of this technology. The method is tailored to diagnose children with heart septal defects, the pathological condition which can bring irreversible and sometimes fatal consequences to the children. The study includes 115 children referrals to an university hospital, consisting of 6 groups of the individuals: atrial septal defects (10), healthy children with innocent murmur (25), healthy children without any murmur (25), mitral regurgitation (15), tricuspid regurgitation (15), and ventricular septal defect (25). The method is trained to detect the atrial or ventricular septal defects versus the rest of the groups. Accuracy/sensitivity and the structural risk of the method is estimated to be 91.6%/88.4% and 9.89%, using the repeated random sub sampling and the A-Test method, respectively.
Place, publisher, year, edition, pages
IOS Press, 2020
Series
Studies in Health Technology and Informatics, ISSN 0926-9630 ; 270
Keywords
A-Test method; Intelligent phonocardiography; Time growing neural network; heart sound signal; septal heart defects
National Category
Cardiac and Cardiovascular Systems
Identifiers
urn:nbn:se:liu:diva-174303 (URN)10.3233/SHTI200146 (DOI)32570370 (PubMedID)2-s2.0-85086906795 (Scopus ID)9781643680828 (ISBN)9781643680835 (ISBN)
Conference
30th Medical Informatics Europe Conference, MIE 2020; Geneva's International Conference CenterGeneva; Switzerland; 28 April 2020 through 1 May 202
Note
Funding agencies: CAPIS Inc., Mons, Belgium and the KKS financed research profile in embedded sensor systems for health at Malardalen University, Vasterås, Sweden.
2021-03-182021-03-182021-03-18