Dynamic Classification of Cellular Transmural TransMembrane Potential (TMP) Activity of the Heart

  • Mohamed Elshrif
  • Linwei Wang
  • Pengcheng Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6666)


Understanding the transmembrane potential (TMP) dynamics of the heart provides an essential guidance to the diagnoses and treatment of cardiac arrhythmias. Most existing methods analyze and classify the TMP signal globally depending on extracting silent features such as the activation time. In consequence, these methods can not characterize the dysfunctions of each cardiac cell dynamically. In order to assess the electrophysiology of the heart considering pathological conditions of each cardiac cell over time, one should analyze and classify the TMP behavior that is differentially expressed in a particular set of time. In this paper, we utilize a spectral co-clustering algorithm to disclose the abnormality of the TMP dynamics over a time sequence. This algorithm is based on the observation that the embedding spectrum structures in the TMP dynamics matrices can be found in their eigenvectors through singular value decomposition (SVD). These eigenvectors correspond to the characteristic patterns across cardiac cells or time sequence. To demonstrate the reliability of this approach, our experimental results show great agreement with the ground truth of the simulated data sets that enable efficient use of this scheme for revealing abnormal behavior in TMP dynamics, at the presence of added Gaussian noise to the simulated TMP dynamics. Furthermore, we compare our results against the k-means clustering algorithm outcomes.


Bipartite Graph Singular Value Decomposition Cardiac Cell Action Potential Duration Infarct Region 
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.


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  1. 1.
    Wang, L., Wong, K., Zhang, H., Liu, H., Shi, P.: Noninvasive Computational Imaging of Cardiac Electrophysiology for 3D Infarct Quantitation. IEEE Transactions on Biomed. Eng. 13 (December 2010)Google Scholar
  2. 2.
    Wang, L., Zhang, H., Wong, K., Liu, H., Shi, P.: Physiological Model Constrained Noninvasive Reconstruction of Volumetric Myocardial Transmembrane Potentials. IEEE Transactions on Biomedical Engineering 57(2) (February 2010)Google Scholar
  3. 3.
    Relan, J., Pop, M., Delingette, H., Wright, G.A., Ayache, N., Sermesant, M.: Estimation of Reaction, Diffusion and Restitution Parameters for a 3D Myocardial Model Using Optical Mapping and MRI. In: Camara, O., Pop, M., Rhode, K., Sermesant, M., Smith, N., Young, A. (eds.) STACOM 2010. LNCS, vol. 6364, pp. 270–280. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Relan, J., Sermesant, M., Pop, M., Delingette, H., Sorine, M., Wright, G.A., Ayache, N.: Volumetric Prediction of Cardiac Electrophysiology using a Heart Model Personalized to Surface Data. In: MICCAI Workshop, pp. 19–27 (2009)Google Scholar
  5. 5.
    Miller III, W.T., Geselowitz, D.B.: Simulation Studies of the Electrocardiogram; II. Ischemia and Infarction. J. ACM. Circ. Res. 43(2), 315–323 (1978)CrossRefGoogle Scholar
  6. 6.
    Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of the the Seventh ACM SIGKDD (2001)Google Scholar
  7. 7.
    Weiling, M., Nerboone, J.: Bipartite spectral graph partitioning to co-cluster varieties and sound correspondence in dialectology. In: Choudhuri, M. (ed.) Proc. Workshop on Graph-based Methods for Natural Lang. Processing, pp. 26–34 (2009)Google Scholar
  8. 8.
    Kluger, Y., Basri, R., Chang, J.T., Gerstein, M.: Spectral biclustering of microarray data: co-clustering genes and conditions. Genome Research 13, 703–716 (2003)CrossRefGoogle Scholar
  9. 9.
    Kardesch, M., Hogancamp, C.E., Bing, R.J.: The effect of complete ischemia on the intracellular electrical activity of the whole mammalian heart. Circ. Res. 6, 715–720 (1958)CrossRefGoogle Scholar
  10. 10.
    Samson, W.E., Scher, A.M.: Mechanism of S-T segment alteration during acute myocardial injury. Circ. Res. 8, 780–787 (1960)CrossRefGoogle Scholar
  11. 11.
    Spach, M.S., Barr, R.C., Lanning, C.F., Tucek, P.C.: Origin of body surface QRS and T-wave potentials from epicardial potential distributions in the intact chimpanzee. Circulation 55, 268–278 (1977)CrossRefGoogle Scholar
  12. 12.
    Ramanathan, C., Jia, P., Ghanem, R., Ryu, K., Rudy, Y.: Activation and repolarization of the normal human heart under complete physiological conditions. PNAS 103(16), 6309–6314 (2006)CrossRefGoogle Scholar
  13. 13.
    Janse, M.J., Wit, A.L.: Electrophysiological mechanisms of ventricular arrhythmias resulting from myocardium ischemia and infarction. Physiol. Rev. 69, 1049–1169 (1989)Google Scholar
  14. 14.
    Taccardi, B.: Distribution of heart potentials on the thoracic surface of normal human subjects. Circ. Res. 12, 341 (1963)CrossRefGoogle Scholar
  15. 15.
    Chung, F.: Spectral Graph Theory. American Mathematical Society Press, Providence (1997)zbMATHGoogle Scholar
  16. 16.
    Nash, M.: Mechanics and material properties of the heart using an anatomically accurate mathematical model. Ph.D. dissertation, Univ. of Auckland (May 1998)Google Scholar
  17. 17.
    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. Circulation 105, 539–542 (2002)CrossRefGoogle Scholar
  18. 18.
    Shuros, A.C., Salo, R.W., Florea, V.G., Pastore, J., Kuskowski, M.A., Chandrashekhar, Y., Anand, I.S.: Ventricular Preexcitation Modulates Strain and Attenuates Cardiac Remodeling in a Swine Model of Myocardial Infarction. Circ. Res. 116, 1162–1169 (2007)CrossRefGoogle Scholar
  19. 19.
    Aliev, R.R., Panfilov, A.V.: A simple two-variable model of cardiac excitation. Chaos, Solitions and Fractals 7(3), 293–301 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mohamed Elshrif
    • 1
  • Linwei Wang
    • 1
  • Pengcheng Shi
    • 1
  1. 1.Golisano College of Computing and Information ScienceRochester Institute of TechnologyRochesterUSA

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