ECG Beat Classification Using Mirrored

Accurate electrocardiogram (ECG) beat classification is essential for automated detection of arrhythmias. A novel classification algorithm of the ECG beats, applying Mirrored Gauss Model (MGM) had been proposed in this paper. The MGM has strong morphological representation ability for QRS complex waves using curve fitting. With the MGM, the width of QRS complex wave could be extracted and applied to ECG beat classification easily, effectively and automatically. It was proved by experiment carrying out using all of ECG records in MIT-BIH Arrhythmia Database that the MGM is a promising algorithm for ECG beat classification. The whole classification accuracy is 93.93% for normal beats and 93.94% for premature ventricular contraction (PVC) beats.

[1]  Rosaria Silipo,et al.  Artificial neural networks for automatic ECG analysis , 1998, IEEE Trans. Signal Process..

[2]  Mahantapas Kundu,et al.  A knowledge-based approach to ECG interpretation using fuzzy logic , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[3]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[4]  Emmanuel Skordalakis,et al.  Syntactic Pattern Recognition of the ECG , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  P.E. Trahanias,et al.  An approach to QRS complex detection using mathematical morphology , 1993, IEEE Transactions on Biomedical Engineering.

[6]  Thomas F. Coleman,et al.  A Subspace, Interior, and Conjugate Gradient Method for Large-Scale Bound-Constrained Minimization Problems , 1999, SIAM J. Sci. Comput..

[7]  Kang-Ping Lin,et al.  QRS feature extraction using linear prediction , 1989, IEEE Transactions on Biomedical Engineering.

[8]  Kap Luk Chan,et al.  Classification of electrocardiogram using hidden Markov models , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).