Electrocardiogram (ECG) pattern modeling and recognition via deterministic learning

被引：0
作者：
Dong X. [1 ]
Wang C. [1 ]
Hu J. [1 ]
Ou S. [2 ]
机构：
[1] School of Automation Science and Engineering, South China University of Technology, Guangzhou Guangdong
[2] Department of Radiology, General Hospital of Guangzhou Military Command, Guangzhou Guangdong
来源：
Control Theory and Technology | 2015年 / 12卷 / 04期
关键词：
Deterministic learning; Dynamics; ECG; Pattern recognition; Temporal features;
D O I：
10.1007/s11768-014-4056-4
中图分类号：
学科分类号：
摘要：
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. © 2014, South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg.
引用
收藏
页码：333 / 344
页数：11
相关论文
共 61 条
  • [1] Ince T., Kiranyaz S., Gabbouj M., A generic and robust system for automated patient-specific classification of ECG signals, IEEE Transactions on Biomedical Engineering, 56, 5, pp. 1415-1426, (2009)
  • [2] Li C., Zheng C., Tai C., Detection of ECG characteristic points using wavelet transforms, IEEE Transactions on Biomedical Engineering, 42, 1, pp. 21-28, (1995)
  • [3] Afsar F.A., Arif M., Yang J., Detection of ST segment deviation episodes in ECG using KLT with an ensemble neural classifier, Physiological Measurement, 29, 7, pp. 747-760, (2008)
  • [4] Sahambi J.S., Tandonz S.N., Bhatt R.K.P., Using wavelet transforms for ECG characterization — an on-line digital signal processing system, IEEE Engineering in Medicine and Biology Magazine, 16, 1, pp. 77-83, (1997)
  • [5] Saxena S.C., Kumar V., Hamde S.T., Feature extraction from ECG signals using wavelet transforms for disease diagnostics, International Journal of Systems Science, 33, 13, pp. 1073-1085, (2002)
  • [6] Guler I., Ubeyli E., ECG beat classifier designed by combined neural network model, Pattern Recognition, 38, 2, pp. 199-208, (2005)
  • [7] Bahoura M., Hassani M., Hubin M., DSP implementation of wavelet transform for real time ECG wave forms detection and heart rate analysis, Computer Methods and Programs in Biomedicine, 52, 1, pp. 35-44, (1997)
  • [8] Daamouchea A., Hamamib L., Alajlanc N., Et al., A wavelet optimization approach for ECG signal classification, Biomedical Signal Processing and Control, 7, 4, pp. 342-349, (2012)
  • [9] Banerjee S., Mitra M., ECG beat classification based on discrete wavelet transformation and nearest neighbor classifier, Journal of Medical Engineering & Technology, 37, 4, pp. 264-272, (2013)
  • [10] Martinez J.P., Almeida R., Olmos S., Et al., A wavelet-based ECG delineator: evaluation on standard databases, IEEE Transactions on Biomedical Engineering, 51, 4, pp. 570-581, (2004)
1234567