A Generic and Patient-Specific Electrocardiogram Signal Classification System

  • Turker Ince
  • Serkan Kiranyaz
  • Moncef Gabbouj


A generic and patient-specific classification system designed for robust and accurate detection of electrocardiogram (ECG) heartbeat patterns is presented. The proposed feature extraction process utilizes morphological wavelet transform features, which are projected onto a lower dimensional feature space using principal component analysis, and temporal features from the ECG data. Due to its time–frequency localization properties, the wavelet transform is an efficient tool for analyzing nonstationary ECG signals which can be used to decompose an ECG signal according to scale, thus allowing separation of the relevant ECG waveform morphology descriptors from the noise, interference, baseline drift, and amplitude variation of the original signal. For the pattern recognition unit, feedforward and fully connected artificial neural networks (ANNs), which are optimally designed for each patient by the multidimensional particle swarm optimization (MD PSO) technique, are employed. Despite many promising ANN-based techniques have been applied to ECG signal classification, these classifier systems have not performed well in practice and their results have generally been limited to relatively small datasets mainly because such systems have in general static (fixed) network structures for classifiers. On the other hand, the proposed algorithm based on patient-adaptive architecture by means of an evolutionary classifier design has demonstrated significant performance improvement over such conventional global classifier systems. By using relatively small common and patient-specific training data, the proposed classification system can adapt to significant interpatient variations in ECG patterns by training the optimal network structure, and thus, achieves higher accuracy over larger datasets. The classification experiments over a benchmark database demonstrate that the proposed system achieves such average accuracies and sensitivities better than most of the current state-of-the-art algorithms for detection of ventricular ectopic beats (VEBs) and supra-VEBs (SVEBs). Finally, due to its parameter-invariant nature, the proposed system is highly generic, and thus, applicable to any ECG dataset.


Particle Swarm Optimization Architecture Space Particle Swarm Optimization Parameter Propose Feature Extraction Hash Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Alfonso, X., Nguyen, T.Q.: ECG beat detection using filter banks. IEEE Trans. Biomed. Eng. 46(2), 192–202 (1999)CrossRefGoogle Scholar
  2. Coast, D.A., Stern, R.M., Cano, G.G., Briller, S.A.: An approach to cardiac arrhythmia analysis using hidden Markov models. IEEE Trans. Biomed. Eng. 37(9), 826–836 (1990)CrossRefGoogle Scholar
  3. de Chazal, P., Reilly, R.B.: A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 53(12), 2535–2543 (2006)CrossRefGoogle Scholar
  4. de Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51(7), 1196–1206, (2004)CrossRefGoogle Scholar
  5. Hoekema, R., Uijen, G.J.H., Oosterom, A.V.: Geometrical aspects of the interindividual variability of multilead ECG recordings. IEEE Trans. Biomed. Eng. 48(5), 551–559 (2001)CrossRefGoogle Scholar
  6. Hu, Y.H., Tompkins, W.J., Urrusti, J.L., Afonso, V.X.: Applications of artificial neural networks for ECG signal detection and classification. J. Electrocardiol. 26, 66–73 (1994)Google Scholar
  7. Hu, Y., Palreddy, S., Tompkins, W.J.: A patient-adaptable ECG beat classifier using a mixture of experts approach. IEEE Trans. Biomed. Eng. 44(9), 891–900 (1997)CrossRefGoogle Scholar
  8. Inan, O.T., Giovangrandi, L., Kovacs, G.T.A.: Robust neural-networkbased classification of premature ventricular contractions using wavelet transform and timing interval features. IEEE Trans. Biomed. Eng. 53(12), 2507–2515 (2006)CrossRefGoogle Scholar
  9. Ince, T., Kiranyaz, S., Gabbouj, M.: Automated patient-specific classification of premature ventricular contractions. In: IEEE Proceedings on International Conference on EMBS, Vancouver, pp. 5474–5477 (2008)Google Scholar
  10. Iwasa, A., Hwa, M., Hassankhani, A., Liu, T., Narayan, S.M.: Abnormal heart rate turbulence predicts the initiation of ventricular arrhythmias. Pacing Clin. Electrophysiol. 28(11), 1189–1197 (2005)CrossRefGoogle Scholar
  11. Jiang, W., Kong, S.G.: Block-based neural networks for personalized ECG signal classification. IEEE Trans. Neural Netw. 18(6), 1750–1761 (2007)CrossRefGoogle Scholar
  12. Kennedy, J., Eberhart, R.: Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, Perth (1995)Google Scholar
  13. Kiranyaz, S., Ince, T., Yildirim A., Gabbouj, M.: Evolutionary artificial neural networks by multi-dimensional particle swarm optimization. Neural Netw. 22, 1448–1462 (2009)CrossRefGoogle Scholar
  14. Kiranyaz, S., Ince, T., Yildirim, A., Gabbouj, M.: Fractional particle swarm optimization in multi-dimensional search space. IEEE Trans. Syst. Man Cybern. – Part B, 40, 298–319 (2010)Google Scholar
  15. Lagerholm, M., Peterson, C., Braccini, G., Edenbrandt, L., Sörnmo, L.: Clustering ECG complexes using Hermite functions and self-organizing maps. IEEE Trans. Biomed. Eng. 47(7), 838–848 (2000)CrossRefGoogle Scholar
  16. Lee, S.C.: Using a translation-invariant neural network to diagnose heart arrhythmia. In: IEEE Proceedings Conference on Neural Information Processing Systems, Seattle (1989)Google Scholar
  17. Li, C., Zheng, C.X., Tai, C.F.: Detection of ECG characteristic points using wavelet transforms. IEEE Trans. Biomed. Eng. 42(1), 21–28 (1995)CrossRefGoogle Scholar
  18. Mallat, S.: A Wavelet Tour of Signal Processing, 2nd edn. Academic Press, San Diego (1999)MATHGoogle Scholar
  19. Mallat, S.G., Zhong, S.: Characterization of signals from multiscale edges. IEEE Trans. Pattern Anal. Mach. Intell. 14, 710–732 (1992)CrossRefGoogle Scholar
  20. Mark, R., Moody, G.: MIT-BIH Arrhythmia Database Directory. Available:
  21. Minami, K., Nakajima, H., Toyoshima, T.: Real-Time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network. IEEE Trans. Biomed. Eng. 46(2), 179–185 (1999)CrossRefGoogle Scholar
  22. Osowski, S., Linh, T.L.: ECG beat recognition using fuzzy hybrid neural network. IEEE Trans. Biomed. Eng. 48, 1265–1271 (2001)CrossRefGoogle Scholar
  23. Osowski, S., Hoai, L.T., Markiewicz, T.: Support vector machine based expert system for reliable heartbeat recognition. IEEE Trans. Biomed. Eng. 51(4), 582–589 (2004)CrossRefGoogle Scholar
  24. Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32(3), 230–236 (1985)CrossRefGoogle Scholar
  25. Pittner, S., Kamarthi, S.V.: Feature extraction from wavelet coefficients for pattern recognition tasks. IEEE Trans. Pattern Anal. Mach. Intell. 21, 83–88 (1999)CrossRefGoogle Scholar
  26. Recommended practice for testing and reporting performance results of ventricular arrhythmia detection algorithms. Association for the Advancement of Medical Instrumentation, Arlington (1987)Google Scholar
  27. Shyu, L.Y., Wu, Y.H., Hu, W.C.: Using wavelet transform and fuzzy neural network for VPC detection from the holter ECG. IEEE Trans. Biomed. Eng. 51(7), 1269–1273 (2004)CrossRefGoogle Scholar
  28. Silipo, R., Marchesi, C.: Artificial neural networks for automatic ECG analysis. IEEE Trans. Signal Process. 46(5), 1417–1425 (1998)CrossRefGoogle Scholar
  29. Silipo, R., Laguna, P., Marchesi, C., Mark, R.G.: ST-T segment change recognition using artificial neural networks and principal component analysis. Computers in Cardiology, 213–216 (1995)Google Scholar
  30. Talmon, J.L.: Pattern Recognition of the ECG. Akademisch Proefscrift, Berlin (1983)Google Scholar
  31. Thakor, N.V., Webster, J.G., Tompkins, W.J.: Estimation of QRS complex power spectra for design of a QRS filter. IEEE Trans. Biomed. Eng. 31, 702–705 (1984)CrossRefGoogle Scholar
  32. Van den Bergh, F.: An analysis of particle swarm optimizers. Ph.D. thesis, Department of Computer Science, University of Pretoria, Pretoria (2002)Google Scholar
  33. Willems, J.L., Lesaffre, E.: Comparison of multigroup logisitic and linear discriminant ECG and VCG classification. J. Electrocardiol. 20, 83–92 (1987)CrossRefGoogle Scholar
  34. Yao, X., Liu, Y.: A new evolutionary system for evolving artificial neural networks. IEEE Trans. Neural Netw. 8(3), 694–713 (1997)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Faculty of Engineering and Computer SciencesIzmir University of EconomicsBalcova-IzmirTurkey
  2. 2.Department of Signal ProcessingTampere University of TechnologyTampereFinland

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