Abstract
A method for electrocardiogram (ECG) pattern modeling and recognition via deterministic learning theory is presented in this paper. Instead of recognizing ECG signals beat-to-beat, each ECG signal which contains a number of heartbeats is recognized. The method is based entirely on the temporal features (i.e., the dynamics) of ECG patterns, which contains complete information of ECG patterns. A dynamical model is employed to demonstrate the method, which is capable of generating synthetic ECG signals. Based on the dynamical model, the method is shown in the following two phases: the identification (training) phase and the recognition (test) phase. In the identification phase, the dynamics of ECG patterns is accurately modeled and expressed as constant RBF neural weights through the deterministic learning. In the recognition phase, the modeling results are used for ECG pattern recognition. The main feature of the proposed method is that the dynamics of ECG patterns is accurately modeled and is used for ECG pattern recognition. Experimental studies using the Physikalisch-Technische Bundesanstalt (PTB) database are included to demonstrate the effectiveness of the approach.
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This work was supported by the National Science Fund for Distinguished Young Scholars of China (No. 61225014).
Xunde DONG is a Ph.D. candidate at the Center for Control and Optimization, School of Automation, South China University of Technology. His research interest covers adaptive NN control/identification, deterministic learning theory and distributed parameter system.
Cong WANG received the B.E. and M.E. degrees from Beijing University of Aeronautic & Astronautics in 1989 and 1997, respectively, and the Ph.D. degree from the Department of Electrical & Computer Engineering, The National University of Singapore in 2002. From 2001 to 2004, he did his postdoctoral research at the Department of Electronic Engineering, City University of Hong Kong. He has been with the School of Automation, South China University of Technology, Guangzhou, China, since 2004, where he is currently a professor. He has authored and co-authored over 60 international journal and conference papers and the book “Deterministic Learning Theory for Identification, Recognition and Control”. He serves as an associate editor of the IEEE Transactions on Neural Networks and Learning Systems since 2012, and as an Associate Editor for Journal of Control Theory & Applications, and ACTA AUTOMATICA SINICA (two best journals in systems and control area in China) since 2008 and 2011, respectively. He is a member of the Technical Committee on Intelligent Control of the IEEE CSS. His research interest includes intelligent control, neural networks, nonlinear systems and control, dynamical pattern recognition, pattern-based control, dynamical systems, and oscillation fault diagnosis.
Junmin HU is a Ph.D. candidate at the Center for Control and Optimization, School of Automation, South China University of Technology. Her research interest covers adaptive NN control/identification, deterministic learning theory and oscillation fault diagnosis.
Shanxing OU is a doctor of Medicine, and is currently director of the Department of Radiology, General Hospital of Guangzhou Military Command, Guangzhou, China. He is a member of the standing committee of the Chinese People’s Liberation Army society of radiology. He servers as the chairman of committee of Guangzhou Military Command society of radiology. His research interest includes cardiothoracic surgery disease, the imaging diagnosis of heart diseases, coronary heart disease, and myocardial ischemia.
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Dong, X., Wang, C., Hu, J. et al. Electrocardiogram (ECG) pattern modeling and recognition via deterministic learning. Control Theory Technol. 12, 333–344 (2014). https://doi.org/10.1007/s11768-014-4056-4
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DOI: https://doi.org/10.1007/s11768-014-4056-4