Support vector machine-based expert system for reliable heartbeat recognition

This paper presents a new solution to the expert system for reliable heartbeat recognition. The recognition system uses the support vector machine (SVM) working in the classification mode. Two different preprocessing methods for generation of features are applied. One method involves the higher order statistics (HOS) while the second the Hermite characterization of QRS complex of the registered electrocardiogram (ECG) waveform. Combining the SVM network with these preprocessing methods yields two neural classifiers, which have been combined into one final expert system. The combination of classifiers utilizes the least mean square method to optimize the weights of the weighted voting integrating scheme. The results of the performed numerical experiments for the recognition of 13 heart rhythm types on the basis of ECG waveforms confirmed the reliability and advantage of the proposed approach.

[1]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[2]  G. Steinbeck,et al.  Frequency analysis of the electrocardiogram with maximum entropy method for identification of patients with sustained ventricular tachycardia , 1991, IEEE Transactions on Biomedical Engineering.

[3]  G.E. Oien,et al.  ECG rhythm classification using artificial neural networks , 1996, 1996 IEEE Digital Signal Processing Workshop Proceedings.

[4]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[5]  W.J. Tompkins,et al.  A patient-adaptable ECG beat classifier using a mixture of experts approach , 1997, IEEE Transactions on Biomedical Engineering.

[6]  Stanislaw Osowski,et al.  ECG beat recognition using fuzzy hybrid neural network , 2001, IEEE Trans. Biomed. Eng..

[7]  Carsten Peterson,et al.  Clustering ECG complexes using Hermite functions and self-organizing maps , 2000, IEEE Trans. Biomed. Eng..

[8]  Gene H. Golub,et al.  Matrix computations , 1983 .

[9]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[10]  H. Nakajima,et al.  Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network , 1999, IEEE Transactions on Biomedical Engineering.

[11]  W J Tompkins,et al.  Applications of artificial neural networks for ECG signal detection and classification. , 1993, Journal of electrocardiology.

[12]  Athina P. Petropulu,et al.  Higher-order spectral analysis , 2006 .

[13]  John Platt,et al.  Fast training of svms using sequential minimal optimization , 1998 .

[14]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[15]  Chrysostomos L. Nikias,et al.  Higher-order spectral analysis , 1993, Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ.

[16]  Koby Crammer,et al.  On the Learnability and Design of Output Codes for Multiclass Problems , 2002, Machine Learning.

[17]  David R. Musicant,et al.  Lagrangian Support Vector Machines , 2001, J. Mach. Learn. Res..

[18]  Robert J. Schalkoff,et al.  Pattern recognition - statistical, structural and neural approaches , 1991 .

[19]  Christopher J. BISHOPAbstra,et al.  Orthogonal Functions , 2022 .

[20]  Roger G. Mark,et al.  The MIT-BIH Arrhythmia Database on CD-ROM and software for use with it , 1990, [1990] Proceedings Computers in Cardiology.

[21]  Sherif Hashem,et al.  Optimal Linear Combinations of Neural Networks , 1997, Neural Networks.