Development and Validation of an Algorithm for Cardiomyocyte Beating Frequency Determination

  • Demián Wassermann
  • Marta Mejail
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


The Chagas disease or Tripanosomiasis Americana affects between 16 and 18 million people in endemic areas. This disease affects the beating rate of infected patients’ cardiomyocytes. At the Molecular Biology of Chagas Disease Laboratory in Argentina the effect of isolated patient’s serum antibodies is studied over rat cardiomyocyte cultures. In this work an image processing application to measure the beating rate of this culture over video sequences is presented. This work is organized as follows. Firstly, a preliminary analysis of the problem is introduced, isolating the main characteristics of the problem. Secondly, a Monte Carlo experiment is designed and used to evaluate the robustness and validity of the algorithm. Finally, an algorithm of order O(T(N log N + N)) for tracking cardiomyocyte membranes is presented, where T is the number of frames and N is the maximum area of the membrane. Its performance is compared against the standard beating rate measure method.


Video Sequence Initial Curve Monte Carlo Experiment Image Processing Application Beating Rate 
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. 1.
    Bonomo, R., Salata, R.: American Trypanosomiasis (Chaga’s Disease: Trypanosoma cruzi). In: Nelson Textbook of Pediatrics. W. B. Saunders (2000)Google Scholar
  2. 2.
    Kirchhoff, L.: Trypanosoma species (American Trypanosomiasis, Disease): Biology of Trypanosomes. In: Principles and Practice of Infectious Diseases, Churchhill, Livingstone (2000)Google Scholar
  3. 3.
    Levin, M.: Proyecto genoma de trypanosoma cruzi asociado al proyecto de genoma humano. In: V Muestra de Ciencia y Técnica, IX Jornadas de Becarios (1996)Google Scholar
  4. 4.
    Sethian, J.A.: A fast marching level set method for monotonically advancing fronts. Proc. Nat. Acad. Sci. 93, 1591–1595 (1996)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Sethian, J.A.: Level Set Methods and Fast Marching Methods, 2nd edn. Cambridge University Press, Cambridge (1999)zbMATHGoogle Scholar
  6. 6.
    Malladi, R., Sethian, J.A.: An o(n log n) algorithm for shape modeling. Proc. Nat. Academy of Sciences, USA, 93, 9389–9392 (1996)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Paragios, N.: Geodesic Active Regions and Level Set Methods: Contributions and Applications in Artificial Vision. PhD thesis, INRIA Sophia Antipolis (2000)Google Scholar
  8. 8.
    Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics 79, 12–49 (1988)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Bland, J.M., Altman, D.G.: Measuring agreement in method comparison studies. Statistical Methods in Medical Research 8, 135–160 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Demián Wassermann
    • 1
  • Marta Mejail
    • 1
  1. 1.Facultad de Ciencias Exactas y Naturales, Intendente Güiraldes 2160Universidad de Buenos AiresCiudad UniversitariaRepública Argentina

Personalised recommendations