title: A Novel Method for Screening Children with Isolated Bicuspid Aortic Valve.
abstract: 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.
authors.affiliation: Division of Intelligent Future Technology, Department of Innovation, Design and Technology, Mälardalen University, Västerås, Sweden. arash.ghareh.baghi@mdh.se.
authors.forename: Arash
authors.initials: A
authors.lastname: Gharehbaghi
authors.affiliation: TCTS Lab, Faculty of Polytechnic, Mons University, Mons, Belgium.
authors.forename: Thierry
authors.initials: T
authors.lastname: Dutoit
authors.affiliation: CAPIS Biomedical Research and Department Center, Mons, Belgium.
authors.forename: Amir A
authors.initials: AA
authors.lastname: Sepehri
authors.affiliation: Department of Pediatrics, Tehran University of Medical Sciences, Tehran, Iran.|Department of Clinical Cardiology, Children's Medical Center, Pediatrics Center of Excellence, Tehran, Iran.
authors.forename: Armen
authors.initials: A
authors.lastname: Kocharian
authors.affiliation: Division of Intelligent Future Technology, Department of Innovation, Design and Technology, Mälardalen University, Västerås, Sweden.
authors.forename: Maria
authors.initials: M
authors.lastname: Lindén
mesh_terms: D000465:Algorithms; D001021:Aortic Valve / Q000002:abnormalities* / Q000000981:diagnostic imaging / Q000503:physiopathology; D000082882:Bicuspid Aortic Valve Disease; D002648:Child; D002675:Child, Preschool; D004452:Echocardiography / Q000379:methods; D005260:Female; D006347:Heart Sounds / Q000502:physiology; D006349:Heart Valve Diseases / Q000151:congenital* / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000503:physiopathology; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D010701:Phonocardiography; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine
keywords: Artificial neural network; Bicuspid aortic valve; Intelligent phonocardiogram; Pediatric heart disease; Phonocardiogram; Support vector machine; Time growing neural network