medigraphic.com
SPANISH

Revista Mexicana de Cardiología

ISSN 0188-2198 (Print)
En 2019, la Revista Mexicana de Cardiología cambió a Cardiovascular and Metabolic Science

Ver Cardiovascular and Metabolic Science


  • Contents
  • View Archive
  • Information
    • General Information        
    • Directory
  • Publish
    • Instructions for authors        
    • Send manuscript
  • medigraphic.com
    • Home
    • Journals index            
    • Register / Login
  • Mi perfil

2018, Number 2

<< Back Next >>

Rev Mex Cardiol 2018; 29 (2)

Diagnostic methodology of cardiac dynamics based on the Zipf-Mandelbrot law: evaluation with 50 patients

Rodríguez VJ, Oliveros AD, Rodríguez CD, Sosa GJ, Prieto BS, Correa HC
Full text How to cite this article

Language: English
References: 42
Page: 83-89
PDF size: 245.54 Kb.


Key words:

Cardiac dynamics, heart rate, Zipf-Mandelbrot law, fractal, complexity.

ABSTRACT

Objective: Zipf-Mandelbrot law has been used to assess the complexity of cardiac systems. The objective of this work is to corroborate the clinical applicability of a diagnostic methodology developed from Zipf-Mandelbrot law, in the differentiation of normality and acute cardiac disease. Material and methods: there were taken 50 continuous electrocardiographic Holter monitoring records, 20 normal and 30 with acute alterations of the cardiac dynamics. The frequencies of occurrence of heart rates in ranges of 15 lat/min were organized hierarchically to demonstrate the hyperbolic behavior of dynamics and to apply the Zipf-Mandelbrot law. A linearization was performed and the statistical fractal dimension of each dynamic was obtained, giving rise to the mathematical diagnosis. Sensitivity, specificity and Kappa coefficient were calculated. Results: The values of the statistical fractal dimension of the acute cardiac dynamics were between 0.7123 and 0.9327, whereas for the normal dynamics were found between 0.4253 and 0.6698, evidencing quantitative differences between states of normality and disease. Sensitivity and specificity values of 100% were found and the kappa coefficient was 1. Conclusions: The clinical and diagnostic utility of the mathematical methodology based on Zipf-Mandelbrot law was verified, observing a decrease of dynamics complexity in cases of acute heart disease.


REFERENCES

  1. Mandelbrot B. Scaling and power laws without geometry. In: The fractal geometry of nature. San Francisco: W. H. Freeman and Co.; 1972. pp. 344-348.

  2. Zipf GK. The human behavior and the principle of least effort. Cambridge, M.A.: Addison-Wesley Press; 1949.

  3. Mandelbrot B. Information theory and psycholinguistics: a theory of words frequencies. In: Lazafeld P, Henry N (eds.). Readings in mathematical social science. Cambridge, MA: MIT Press; 1966.

  4. Mandelbrot B. Structure formelle des textes et comunication. World. 1954; 10: 1-27.

  5. OMS. Centro de prensa. Enfermedades cardiovasculares. Disponible en: http://www.who.int/mediacentre/factsheets/fs317/es/

  6. Carvajal R, Vallverdú M, Caminal P. Análisis no lineal de la variabilidad de la frecuencia cardiaca en casos normales y cardiopatías. Rev Mex Ing Biomed. 2000; 21: 29-33.

  7. Shahbazi F, Asl BM. Generalized discriminant analysis for congestive heart failure risk assessment based on long-term heart rate variability. Comput Methods Programs Biomed. 2015; 122 (2): 191-198.

  8. Lakusic N, Mahovic D, Kruzliak P, Cerkez Habek J, Novak M, Cerovec D. Changes in heart rate variability after coronary artery bypass grafting and clinical importance of these findings. Biomed Res Int. 2015; 2015: 680515. doi: 10.1155/2015/680515. Epub 2015 May 20.

  9. Gallo J, Farbiarz J, Alvarez D. Análisis espectral de la variabilidad de la frecuencia cardiaca. IATREIA. 1999; 12 (2): 61-71.

  10. Goldberger AL, Amaral LA, Hausdorff JM, Ivanov PCh, Peng CK, Stanley HE. Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci U S A. 2002; 99 Suppl 1: 2466-2472.

  11. Rodríguez JO, Prieto SE, Correa C, Bernal PA, Puerta GE, Vitery S et al. Theoretical generalization of normal and sick coronary arteries with fractal dimensions and the arterial intrinsic mathematical harmony. BMC Med Phys. 2010; 10: 1.

  12. Rodríguez L, Prieto S, Correa C, Bernal P, Álvarez L, Forero G et al. Diagnóstico fractal del ventriculograma cardiaco izquierdo. Geometría fractal del ventriculograma durante la dinámica cardiaca. Rev Colomb Cardiol. 2012; 19 (1): 18-24.

  13. Goldberger AL, Rigney DR, Mietus J, Antman EM, Greenwald S. Nonlinear dynamics in sudden cardiac death syndrome: heartrate oscillations and bifurcations. Experientia. 1988; 44 (11-12): 983-987.

  14. Godoy J, Aniño M, Torres M. Análisis multifractal de la regulación autonómica del ritmo cardíaco en episodios isquémicos. Rev Argent Bioing. 2003; 9 (2): 24-29.

  15. Vanerio-Balbela G, Vidal-Amaral JL, Fernández-Banizi P, López-Achigar G, Banina-Aguerre D, Viana P et al. ¿Se puede predecir el riesgo de muerte súbita luego de sufrir un infarto de miocardio? Rev Méd Urug. 2006; 22 (4): 249-265.

  16. Chang MC, Peng CK, Stanley HE. Emergence of dynamical complexity related to human heart rate variability. Phys Rev E Stat Nonlin Soft Matter Phys. 2014; 90 (6): 062806.

  17. Porta A, Bari V, Marchi A, De Maria B, Cysarz D, Van Leeuwen P et al. Complexity analyses show two distinct types of nonlinear dynamics in short heart period variability recordings. Front Physiol. 2015; 6: 71.

  18. Rodríguez J. Nuevo diagnóstico físico y matemático de la monitoria fetal: predicción de aplicación clínica. Momento Revista de Física. 2012; 44: 49-65.

  19. Rodríguez-Velásquez J, Prieto-Bohórquez S, Ortíz-Salamanca L, Bautista-Charry A, Bernal P, Avilán-Vargas N. Diagnóstico matemático de la monitoría fetal aplicando la ley de Zipf-Mandelbrot. Rev Fac Med. 2006; 54 (2): 96-107.

  20. Rodríguez J. Entropía proporcional de los sistemas dinámicos cardiacos: predicciones físicas y matemáticas de la dinámica cardiaca de aplicación clínica. Rev Colomb Cardiol. 2010; 17 (3): 115-129.

  21. Rodríguez J. Mathematical law of chaotic cardiac dynamic: predictions of clinic application. J Med Med Sci. 2011; 2 (8): 1050-1059.

  22. Rodríguez J, Prieto S, Flórez M, Alarcón C, López R, Aguirre G et al. Physical-mathematical diagnosis of cardiac dynamic on neonatal sepsis: predictions of clinical application. J Med Med Sci. 2014; 5 (5): 102-108.

  23. Rodríguez J, Narváez R, Prieto S, Correa C, Bernal P, Aguirre G et al. The mathematical law of chaotic dynamics applied to cardiac arrhythmias. J Med Med Sci. 2013; 4 (7): 291-300.

  24. Rodríguez, J, Álvarez L, Tapia D, López F, Cardona DM, Mora J et al. Evaluación de la dinámica cardiaca de pacientes con arritmia con base en la teoría de la probabilidad. Medicina. 2012; 34 (1): 7-16.

  25. Rodríguez J, Correa C, Melo M, Domínguez, D, Prieto S, Cardona DM et al. Chaotic cardiac law: developing predictions of clinical application. J Med Med Sci. 2013; 4 (2): 79-84.

  26. Rodríguez J, Prieto S, Domínguez D, Melo M, Mendoza F, Correa C et al. Mathematical-physical prediction of cardiac dynamics using the proportional entropy of dynamic systems. J Med Med Sci. 2013; 4 (8): 370-381.

  27. Rodríguez J, Prieto S, Bernal P et al. Entropía proporcional aplicada a la evolución de la dinámica cardiaca. Predicciones de aplicación clínica. En: Rodríguez LG (coordinador). La emergencia de los enfoques de la complejidad en América Latina: desafíos, contribuciones y compromisos para abordar los problemas complejos del siglo XXI. Tomo 1. Buenos Aires: Comunidad Editora Latinoamericana; 2015. pp. 315-344.

  28. Rodríguez JO, Prieto SE, Correa SC, Mendoza F, Weiz G, Soracipa MY et al. Physical mathematical evaluation of the cardiac dynamic applying the Zipf – Mandelbrot law. Journal of Modern Physics. 2015; 6 (13): 1881-1888.

  29. Rodríguez J, Correa C, Ortiz L, Prieto S, Bernal P, Ayala J. Evaluación matemática de la dinámica cardiaca con la teoría de la probabilidad. Rev Mex Cardiol. 2009; 20 (4): 183-189.

  30. Rodríguez J, Correa C, Prieto S, Bernal P, Germán F, Salazar G et al. Confirmación del método de ayuda diagnóstica de la dinámica cardiaca de aplicación clínica desarrollado con base en la teoría de la probabilidad. Rev Fac Med. 2011; 19 (2): 167-177.

  31. Huikuri HV, Mäkikallio TH, Peng CK, Goldberger AL, Hintze U, Møller M. Fractal correlation properties of R-R interval dynamics and mortality in patients with depressed left ventricular function after an acute myocardial infarction. Circulation. 2000; 101 (1): 47-53.

  32. Voss A, Schulz S, Schroeder R, Baumert M, Caminal P. Methods derived from nonlinear dynamics for analysing heart rate variability. Philos Trans A Math Phys Eng Sci. 2009; 367 (1887): 277-296.

  33. Rodríguez-Velásquez J. Comportamiento fractal del repertorio T específico contra el alergeno Poa P9. Rev Fac Med. 2005; 53 (2): 72-78.

  34. Rodríguez J. Dynamical systems theory and ZIPF – Mandelbrot Law applied to the development of a fetal monitoring diagnostic methodology. XVIII FIGO World Congress of Gynecology and Obstetrics. Kuala Lumpur, MALAYSIA. November 2006.

  35. Borgatta L, Shrout PE, Divon MY. Reliability and reproducibility of nonstress test readings. Am J Obstet Gynecol. 1988; 159 (3): 554-558.

  36. Rodríguez JO, Prieto SE, Correa C, Pérez CE, Mora JT, Bravo J et al. Predictions of CD4 lymphocytes’ count in HIV patients from complete blood count. BMC Med Phys. 2013; 13 (1): 3.

  37. Rodríguez J, Bernal P, Álvarez L, Pabón S, Ibáñez S, Chapuel N et al. Predicción de unión de péptidos de MSP-1 y EBA-140 de plasmodium falciparum al HLA clase II Probabilidad, combinatoria y entropía aplicadas a secuencias peptídicas. Inmunología. 2010; 29 (3): 91-99.

  38. Rodríguez J, Bernal P, Prieto S, Correa C. Teoría de péptidos de alta unión de malaria al glóbulo rojo. Predicciones teóricas de nuevos péptidos de unión y mutaciones teóricas predictivas de aminoácidos críticos. Inmunología. 2010; 29(1):7-19.

  39. Rodríguez J. Método para la predicción de la dinámica temporal de la malaria en los municipios de Colombia. Rev Panam Salud Pública. 2010; 27 (3): 211-218.

  40. Prieto-Bohórquez SE, Velásquez JO, Correa-Herrera SC, Soracipa-Muñoz MY. Diagnosis of cervical cells based on fractal and Euclidian geometrical measurements: Intrinsic Geometric Cellular Organization. BMC Med Phys. 2014; 14: 2.

  41. Velásquez JO, Bohórquez SE, Herrera SC, Cajeli DD, Velásquez DM, de Alonso MM. Geometrical nuclear diagnosis and total paths of cervical cell evolution from normality to cancer. J Cancer Res Ther. 2015; 11 (1): 98-104.

  42. Rodríguez J. Dynamical systems applied to dynamic variables of patients from the Intensive Care Unit (ICU). Physical and mathematical Mortality predictions on ICU. J Med Med Sci. 2015; 6 (8): 102-108.




2020     |     www.medigraphic.com

Mi perfil

C?MO CITAR (Vancouver)

Rev Mex Cardiol. 2018;29