An Application of PCA in ECG Classification with Cross - Validation

Authors

  • Nikhil G Kurup  Lecturer, NSS Polytechnic College, Pandalam, Kerala, India

Keywords:

QRS detection; SVM; PCA ; Cross validation

Abstract

Electrocardiogram (ECG)is a non-invasive technique used mainly as a primary diagnostic tool for cardiovascular diseases. A pure ECG signal provides valuable information about the electrophysiology of heart diseases and the occurrence of ischemic changes. It provides necessary information about the functional aspects of the heart and cardiovascular system. The objective of this report is to automatically detect the cardiac problems in ECG signal. Recently developed digital signal processing and pattern recognition technique is used in this report for the detection of cardiac problems. The detection of cardiac abnormalities in ECG signal consists of following steps :detection of QRS complex in ECG signal: feature extraction from detected QRS complexes : classi?cation of beats using extracted feature set from QRS complexes. Automatic classi?cation of heart beats in turn represents the automatic detection of cardiac problems in ECG signal. Hence this report develops an automatic algorithm for classi?cation of heartbeats to detect cardiac abnormalities in ECG signals. SVM is used as a classifier and PCA is used for feature reduction. Also compared the result with out using PCA . Leave one out cross validation is also done.

References

  1. Argyro Kampouraki, George Manis, and Christophoros Nikou, Member, IEEE,"Heart beat time series classification using svm “IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 13, NO. 4, JULY 2009
  2. F. Mormann, R. G. Andrzejak, C. E. Elger, and K. Lenhnertz, "Seizure prediction: the long and winding road," Brain, vol. 130, no. 2, pp. 313-333, 2006.
  3. R. Silipo, G. Deco, R. Vergassola, and C. Gremigni, "A characterization of HRV’s nonlinear hidden dynamics bymeans ofMarkovmodels,"IEEE Trans. Biomed. Eng., vol. 46, no. 8, pp. 978-986, Aug. 1999.
  4. M. Ferrario, M. Signorini, G. Magenes, and S. Cerutti, "Comparison of entropy-based regularity estimators: Application to the fetal heart rate signal for the identi?cation of fetal distress," IEEE Trans. Biomed. Eng.,vol. 53, no. 1, pp. 119-125, Jan. 2006
  6. D. Hoyer, B. Pompe, K. Chon, H. Hardhalt, C. Wicher, and U. Zwiener,"Mutual information function assesses autonomic information ?ow of heart rate dynamics at different time scales," IEEE Trans. Biomed. Eng.,vol. 52, no. 4, pp. 584-592, Apr. 2005
  7. R. Barbieri and E. Brown, "Analysis of heartbeat dynamics by point process adaptive ?ltering,"IEEE Trans. Biomed. Eng., vol. 53, no. 1, pp. 4-12, Jan. 2006.
  8. B. Aysin, L. Chaparro, I. Grave, and V. Shusterman, "Orthonormal basis partitioning and time frequency representation of cardiac rhythm dynamics,"IEEE Trans. Biomed. Eng., vol. 52, no. 5, pp. 878-889, May 2005
  9. J. Mateo and P. Laguna, "Improved heart rate variability signal analysis from the beat occurence times according to the IPFM model," IEEE Trans. Biomed. Eng., vol. 47, no. 8, pp. 997-1009, Aug. 2000.
  10. A. Bezerianos, S. Papadimitriou, and D. Alexopoulos, "Radial basis function neural networks for the characterization of heart rate variability dynamics," Artif. Intell. Med., vol. 15, no. 3, pp. 215-234, 1999.
  11. A. Alexandridi, C. D. Stylios, and G. Manis, "Neural networks and fuzzy logic approximation and prediction for HRV analysis," presented at the Eur. Symp. Intell. Technol., Hybrid Syst. Implement. Smart Adapt. Syst.,Oulu, Finland, Jul. 2003.
  12. C. Cortes and V. N. Vapnik, "Support vector networks," Mach. Learn.,vol. 20, pp. 1-25, 1995.
  13. http://www.physionet.org/physiobank/database/Fantasia.
  14. http://www.physionet.org/physiobank/database/nsr2db.
  15. http://www.physionet.org/physiobank/database/ch2db.
  16. H. Byun and S. W. Lee, "A survey of pattern recognition applications of support vector machines," Int. J. Pattern Recognit. Artif. Intell., vol. 17,no. 3, pp. 459-486, 2003.
  17. I. El-Naqa, Y. Yang, M. Wernick, N. Galatsanos, and R. Nishikawa, "A support vector machine approach for detection of microcalci?cations,"IEEE Trans. Med. Imag., vol. 21, no. 12, pp. 1552-1563, Dec. 2002
  18. S.Osowski,L.T.Hoai, andT.Markiewicz, "Support vector machine based expert system for reliable heartbeat recognition," IEEE Trans. Biomed.Eng., vol. 51, no. 4, pp. 584-589, Apr. 2004
  19. A. Kampouraki, C. Nikou, and G. Manis, "Classi?cation of heart rate signals using support vector machines," in Proc. BioSignal Conf., Brno,Czech Republic, Jun. 2006, pp. 9-11.
  20. J. Pan andW. J. Tompkins, "A real-time QRS detection algorithm," IEEE Trans. Biomed. Eng., vol. 32, no. 3, pp. 230-236, Mar. 1985
  21. http://en.wikipedia.org/wiki/Electrocardiography.
  22. Arnaz Malhi, Robert X. Gao, "PCA-Based Feature Selection Scheme for Machine Defect Classi?cation" IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 53, NO. 6, DECEMBER 2004
    23. A. Malhi and R. Gao, “Feature selection for defect classi?cation in machine condition monitoring," in Proc. 20th IEEE Instrumentation Measurement Technology Conf., vol. 1, Vail, CO, May 2003, pp. 36-41.

International Journal of Scientific Research in Science and Technology

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Published

2017-12-31

Issue

Volume 3, Issue 8, November-December-2017

Section

Research Articles

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How to Cite

[1]
Nikhil G Kurup "An Application of PCA in ECG Classification with Cross - Validation" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 3, Issue 8, pp.394-398, November-December-2017.