title: Detection of systolic ejection click using time growing neural network.
abstract: In this paper, we present a novel neural network for classification of short-duration heart sounds: the time growing neural network (TGNN). The input to the network is the spectral power in adjacent frequency bands as computed in time windows of growing length. Children with heart systolic ejection click (SEC) and normal children are the two groups subjected to analysis. The performance of the TGNN is compared to that of a time delay neural network (TDNN) and a multi-layer perceptron (MLP), using training and test datasets of similar sizes with a total of 614 normal and abnormal cardiac cycles. From the test dataset, the classification rate/sensitivity is found to be 97.0%/98.1% for the TGNN, 85.1%/76.4% for the TDNN, and 92.7%/85.7% for the MLP. The results show that the TGNN performs better than do TDNN and MLP when frequency band power is used as classifier input. The performance of TGNN is also found to exhibit better immunity to noise.
authors.affiliation: Physiological Measurements (IMT), Department of Biomedical Engineering, Linköping University, Linköping, Sweden. Electronic address: arash.ghareh.baghi@liu.se.
authors.forename: Arash
authors.initials: A
authors.lastname: Gharehbaghi
authors.affiliation: Faculty of Polytechnic, Mons University, Mons, Belgium.
authors.forename: Thierry
authors.initials: T
authors.lastname: Dutoit
authors.affiliation: Physiological Measurements (IMT), Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
authors.forename: Per
authors.initials: P
authors.lastname: Ask
authors.affiliation: Department of Electrical and Information Technology, Center for Integrative Electrocardiology, Lund University, Lund, Sweden.
authors.forename: Leif
authors.initials: L
authors.lastname: Sörnmo
mesh_terms: D000465:Algorithms; D001021:Aortic Valve / Q000002:abnormalities / Q000503:physiopathology; D000082882:Bicuspid Aortic Valve Disease; D002648:Child; D002675:Child, Preschool; D003198:Computer Simulation; D006347:Heart Sounds; D006349:Heart Valve Diseases / Q000175:diagnosis / Q000503:physiopathology; D006801:Humans; D016571:Neural Networks, Computer; D012372:ROC Curve; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013599:Systole / Q000502:physiology*; D013997:Time Factors
keywords: Heart sound; Systolic ejection click; Time delay neural network; Time growing neural network