Detection of systolic ejection click using time growing neural network |
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| Institution: | 1. Physiological Measurements (IMT), Department of Biomedical Engineering, Linköping University, Linköping, Sweden;2. Faculty of Polytechnic, Mons University, Mons, Belgium;3. Department of Electrical and Information Technology, Center for Integrative Electrocardiology, Lund University, Lund, Sweden;1. Department of Computer Science, Università di Torino, Turin, Italy;2. Mechanical Engineering Department, Politecnico di Milano, Milan, Italy;1. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, 710054, China;2. School of Mechanical Engineering, University of Leeds, LS2 9JT, UK;1. Institute of Science and Technology Austria, Am Campus 1, A-3400 Klosterneuburg, Austria;2. Eötvös Loránd University, Pázmány Péter setany 1/A, H-1117 Budapest, Hungary;1. School of Computer and Information, AnHui Polytechnic University, WuHu, AnHui 241000, China;2. Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information, Ministry of Education, Nanjing University of Science and Technology, Nanjing 210094, China;1. Battelle, 505 King Ave., Columbus, Ohio 43201 USA;2. Core Energy, LLC, 1011 Noteware Dr., Traverse City, Michigan 469868 USA |
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| 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. |
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| Keywords: | Systolic ejection click Time growing neural network Time delay neural network Heart sound |
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