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Reseach Article

ECG signal monitoring using linear PCA

by H.Chaouch, K.Ouni, L.Nabli
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 33 - Number 8
Year of Publication: 2011
Authors: H.Chaouch, K.Ouni, L.Nabli

H.Chaouch, K.Ouni, L.Nabli . ECG signal monitoring using linear PCA. International Journal of Computer Applications. 33, 8 ( November 2011), 48-54. DOI=10.5120/4146-5979

@article{ 10.5120/4146-5979,
author = { H.Chaouch, K.Ouni, L.Nabli },
title = { ECG signal monitoring using linear PCA },
journal = { International Journal of Computer Applications },
issue_date = { November 2011 },
volume = { 33 },
number = { 8 },
month = { November },
year = { 2011 },
issn = { 0975-8887 },
pages = { 48-54 },
numpages = {9},
url = { },
doi = { 10.5120/4146-5979 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T20:19:42.285817+05:30
%A H.Chaouch
%A K.Ouni
%A L.Nabli
%T ECG signal monitoring using linear PCA
%J International Journal of Computer Applications
%@ 0975-8887
%V 33
%N 8
%P 48-54
%D 2011
%I Foundation of Computer Science (FCS), NY, USA

In this paper, we propose an approach to study the biomedical signal of the electro-cardiographic ECG. Our contribution consists in applying the principal component analysis (PCA) to help diagnose the cardiovascular system. Many researches have recently approached the same theme by integrating many tools of signal processing, but the most interesting method is the PCA.Starting from the ECG signal, we define the characteristic parameters of the electrical activity of the heart. The PCA allows detecting and then localizing the defective parameters of the ECG and facilitating the diagnosis of the existing heart disorders. The results we get at the end of this paper show that this approach is reliable compared with data given by the medical expert.

  1. S.D.Pamar, Bhuvan Unhelkar, “On the performance analysis of the ICA Alghorithms with maternal and fetal ECG inputs and contaminated noises”, IJCA, vol 2, n°7, June 2010.
  2. M. M Sheikh Algunaidi, M. A. Mohd Ali and Md. Fokhrul Islam. “Evaluation of an improved algorithm for fetal QRS detection”, International Journal of the Physical Sciences Vol. 6(2), pp. 213-220, 18 January, 2011.
  3. T.M.Nazmy, H.El-Messiry, B.Al-Bokhity, « Classification of cardiac Arrhythmia based on hybrid system », IJCA,vol 2-N°4, june 2010.
  4. H.Chaouch, « Détection du complexe QRS par analyse multi-échelle », mémoire de mastère, INSAT Tunisie,2009.
  5. R.D.Mali, M.S.Khadtare, Dr.U.L Bombale, « Removal of 50 Hz PLI using discrete wavelet Transform for quality diagnosis of biomedical ECG signal, IJCA, vol 23-n°7, June 2011.
  6. J.P.Coudrc, “Analyse Quantitative des composantes temps-échelle de l'ECG à haute résolution moyenne pour l'évaluation du risque du tachycardies ventriculaires et de la mort subite après un infarctus du myocarde”, Thèse de doctorat, Institut National des Sciences Appliquées de Lyon, 2007.
  7. S .Graja, “segmentation et classification de l’onde P d’un électrocardiogramme :détection du risque de fibrillation auriculaire », Thèse de doctorat, L’école nationale supérieure des télécommunications de Bretagne, 2008.
  8. A. Cabasson, “Analyse des périodes P-P et P-R dans les électrocardiogrammes”. Mémoire de Mastère, Université Nice Sophia Antipolis, Septembre 2005.
  9. L. Clavier, J.M. Boucher, R. Lepage, J.J Blanc and J.C Cornily. Automatic P-wave analysis of patients prone to atrial fibrillation. Med bioeng Comp, vol.40 n°1, Jan, 2002.
  10. L. Nabli and K. Ouni., “The indirect supervision of a system of production by the principal components analysis and the average dynamics of the metrics”, International Review of Automatic Control (IREACO), pp. 560-567, November 2008.
  11. L. Nabli, K. Ouni, and H. Messaoud., “ Méthode de surveillance indirecte d’un système de production par l’analyse en composantes principales”, La 5ème conférence internationale JTEA 2008, Journées Tunisiennes de l’Electrotechnique et de l’Automatique, 2-4 Mai.
  12. L. Nabli, K. Ouni, and H.Haj. Salem., “ Approche Multi agents pour la surveillance indirecte d’un système de production par l’analyse en composantes principales”, la Conférence Internationale Francophone d’Automatique CIFA 2008, pp. 128-136, 3-5 Septembre.
  13. D.Pata, M.Kumardas, S.Pradhan, “ Integration of FCM, PCA and Neural Networks for classification of ECG arrhythmias”,IJCS 36-3-05, 2010.
  14. M. F. Harakat, G. Mourot, and J. Ragot., “ Détection de défauts de capteurs d’un réseau de surveillance de la qualité de l’air”, Journal Européen des Systèmes Automatisés. JESA, pp. 417-436, 2005.
  15. Besse P et Ferré L, « Sur l’usage de la validation croisée en analyse en composantes principales. Reveue de statistique appliquée, XLI (1), pp.71-76, 1993.
  16. Ferré L, « Selection of component analysis : A comparition methods”, Computional statics and Data Analysis,pp.669-682, 1995.
  17. Valles S, Weihua L, and Qin S.J, “Selection of number of principal components: The variance of reconstruction error criterion with a comparision to other methods”, Industrial &Engineering Chemisty Research,vol 38, PP.4389-4401, 1999.
  18. T. Kourti, “Recent Developments in Multivariate SPC Methods for Monitoring and Diagnosing Process and Product Performance”, Journal of Quality Technology, vol.28, N° 4, pp. 409-428, 1996.
  19. M.Gregor, Kourti T and Nomikos P, “Analysis, monitoring and fault diagnosis of industrial processes using multivariate statistical projection methods, 13th Triennial Word Congress, pp.145-150, San Francisco, 1996.
  20. Gertler J, “Fault detection and diagnosis systems”, Marcel Dekker edition, New York, 1998.
  21. Dunia R and Qin S, “A subspace approach to multidimentional identification and reconstruction”, AICHE journal, vol.44,pp1813-1831, 1998.
  22. Gertler J. and McAvoy T, “Principal component analysis and parity relations - Astrong duality”. IFAC Conference SAFEPROCESS, Hull, UK, pp. 837-842, 1997.
  23. Gertler J., Weihua L., Yunbing H. and McAvoy T. (1998). Isolation enhanced principal component analysis. 3rd IFAC Workshop on On-line Fault Détection and Supervision in then Chemical Process Industries, Lyon, June 4-5, France.
  24. Dunia R.,Qin S.J. and Edgar T.F,”Identification of faulty sensors using principal component analysis”, AIChE Journal, vol. 42, N±. 10, pp. 2797-2812, 1996.
  25. Kourti T., and MacGregor J. F, “Recent Developments in Multivariate SPC Methods for Monitoring and Diagnosing Process and Product Performance”, Journal Of Quality Technology, vol. 28, N±. 4, pp. 409-428, 1996.
  26. S. J. Qin, S. Valle, et M. Piovoso. "On unifying multi-block analysis with applications to decentralized process monitoring", Journal of Chemometrics, Vol. 15, N°9, pp. 715-742, 2001.
  27. G. Xu et T. Kailath. "Fast estimation of principal eigenspace using lanczos algorithm", SIAM Journal on Matrix Analysis and Applications, Vol. 15, N°3, pp. 975-994, 1994.
Index Terms

Computer Science
Information Sciences


Principal component analysis ECG Diagnosis Detection of defects Calculation of contributions