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Principal Component Analysis for the Classification of Cardiac Motion Abnormalities Based on Echocardiographic Strain and Strain Rate Imaging

  • Mahdi TabassianEmail author
  • Martino Alessandrini
  • Luca De Marchi
  • Guido Masetti
  • Nicholas Cauwenberghs
  • Tatiana Kouznetsova
  • Jan D’hooge
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9126)

Abstract

Clinical value of the quantitative assessment of regional myocardial function through segmental strain and strain rate has already been demonstrated. Traditional methods for diagnosing heart diseases are based on values extracted at specific time points during the cardiac cycle, known as ‘techno-markers’, and as a consequence they may fail to provide an appropriate description of the strain (rate) characteristics. This study concerns the statistical analysis of the whole cardiac cycle by the Principal Component Analysis (PCA) method and modeling the major patterns of the strain (rate) curves. Experimental outcomes show that the PCA features can outperform their traditional counterparts in categorizing healthy and infarcted myocardial segments and are able to drive considerable benefit to a classification system by properly modeling the complex structure of the strain rate traces.

Keywords

Strain/strain rate classification Principal Component Analysis Feature extraction 

References

  1. 1.
    Aoued, F., Eroglu, E., Herbots, L., Rademakers, F., D’hooge, J.: A statistical model-based approach for the detection of abnormal cardiac deformation. In: Ultrasonics Symposium, vol. 1, pp. 512–515. IEEE (2005)Google Scholar
  2. 2.
    Cerqueira, M.D., Weissman, N.J., Dilsizian, V., Jacobs, A.K., Kaul, S., Laskey, W.K., Pennell, D.J., Rumberger, J.A., Ryan, T., Verani, M.S.: Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart: a statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. Circulation 105, 539–542 (2002)CrossRefGoogle Scholar
  3. 3.
    Clarysse, P., Han, M., Croisille, P., Magnin, I.: Exploratory analysis of the spatio- temporal deformation of the myocardium during systole from tagged MRI. IEEE Trans. Biomed. Eng. 11, 1328–1339 (2002)CrossRefGoogle Scholar
  4. 4.
    Claus, P., D’hooge, J., Langeland, T.M., Bijnens, B., Sutherland, G.R.: SPEQLE (Software Package for Echocardiographic Quantification LEuven) an integrated approach to ultrasound-based cardiac deformation quantification. In: Computers in Cardiology, vol. 29, pp. 69–72. IEEE (2002)Google Scholar
  5. 5.
    Cristianini, N., Shawe-Taylore, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)Google Scholar
  6. 6.
    D’hooge, J., Bijnens, B., Thoen, J., Van de Werf, F., Sutherland, G., Suetens, P.: Echocardiographic strain and strain-rate imaging: a new tool to study regional myocardial function. IEEE Trans. Med. Imaging 21(9), 1022–1030 (2002)CrossRefGoogle Scholar
  7. 7.
    Jamal, F., Kukulski, T., Sutherland, G.R., Weidemann, F., D’hooge, J., Bijnens, B., Derumeaux, G.: Can changes in systolic longitudinal deformation quantify regional myocardial function after an acute infarction? an ultrasonic strain rate and strain study. J. Am. Soc. Echocardiogr. 15(7), 723–730 (2002)CrossRefGoogle Scholar
  8. 8.
    Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer, New York (2002)zbMATHGoogle Scholar
  9. 9.
    Herbots, L., D’hooge, J., Eroglu, E., Thijs, D., Ganame, J., Claus, P., Dubois, C., Theunissen, K., Bogaert, J., Dens, J., Kalantzi, M., Dymarkowski, S., Bijnens, B., Belmans, A., Boogaerts, M., Sutherland, G., Van de Werf, F., Rademakers, F., Janssens, S.: Improved regional function after autologous bone marrow-derived stem cell transfer in patients with acute myocardial infarction: a randomized, double-blind strain rate imaging study. Eur. Heart J. 30, 662–670 (2009)CrossRefGoogle Scholar
  10. 10.
    McMahona, E.M., Korinekb, J., Yoshifukub, S., Senguptaa, P.P., Manducab, A., Belohlaveka, M.: Classification of acute myocardial ischemia by artificial neural network using echocardiographic strain waveforms. Comput. Biol. Med. 38, 416–424 (2008)CrossRefGoogle Scholar
  11. 11.
    Mitani, Y., Hamamoto, Y.: A local mean-based nonparametric classifier. Pattern Recogn. Lett. 27(10), 1151–1159 (2006)CrossRefGoogle Scholar
  12. 12.
    Wold, S., Esbensen, K., Geladi, P.: Principal Component Analysis. Chemometr. Intell. Lab. Syst. 2, 37–52 (1987)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mahdi Tabassian
    • 1
    • 2
    Email author
  • Martino Alessandrini
    • 2
  • Luca De Marchi
    • 1
  • Guido Masetti
    • 1
  • Nicholas Cauwenberghs
    • 3
  • Tatiana Kouznetsova
    • 3
  • Jan D’hooge
    • 2
  1. 1.Department of Electrical, Electronic and Information EngineeringUniversity of BolognaBolognaItaly
  2. 2.Department of Cardiovascular Sciences, Cardiovascular Imaging and Dynamics GroupKU LeuvenLeuvenBelgium
  3. 3.Department of Cardiovascular Sciences, Research Unit of Hypertension and Cardiovascular EpidemiologyKU LeuvenLeuvenBelgium

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