Sökning: id:"swepub:oai:DiVA.org:liu-174303" >
Distinguishing Sept...
Distinguishing Septal Heart Defects from the Valvular Regurgitation Using Intelligent Phonocardiography
-
- Gharehbaghi, Arash (författare)
- Department of Innovation, Design and Technology, Mälardalen University, Sweden
-
- Sepehri, Amir A. (författare)
- CAPIS Biomedical Research and Development Centre, Mon, Belgium
-
- Babic, Ankica, 1960- (författare)
- Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten,Department of Information Science and Media Studies, University of Bergen, Norway,Linköping University, Sweden
-
visa fler...
-
- Ghareh Baghi, Arash (författare)
- Mälardalens högskola,Inbyggda system
-
visa färre...
-
(creator_code:org_t)
- IOS Press, 2020
- 2020
- Engelska.
-
Ingår i: Digital Personalized Health and Medicine. - : IOS Press. - 9781643680828 - 9781643680835 ; 270, s. 178-182
- Relaterad länk:
-
https://doi.org/10.3...
-
visa fler...
-
https://liu.diva-por... (primary) (Raw object)
-
https://urn.kb.se/re...
-
https://doi.org/10.3...
-
https://urn.kb.se/re...
-
visa färre...
Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering (hsv//eng)
Nyckelord
- A-Test method; Intelligent phonocardiography; Time growing neural network; heart sound signal; septal heart defects
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
Hitta via bibliotek
Till lärosätets databas