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2019, Number 4

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Revista Habanera de Ciencias Médicas 2019; 18 (4)

Clinical applicability of the diagnostic software of cardiac dynamics based on the Zipf-Mandelbrot law

Rodríguez J, Oliveros D, Correa C, Prieto S

Language: Spanish
References: 30
Page: 624-633
PDF size: 601.71 Kb.


ABSTRACT

Introduction: The Zipf-Mandelbrot law allowed the development of a methodology that makes quantitative distinctions between acute and normal cardiac dynamics in an objective and reproducible way.
Objective: To confirm the diagnostic capacity and clinical utility of the software that automates a methodology based on the Zipf-Mandelbrot law that performs objective diagnoses of the cardiac dynamics.
Material and Methods: A blind study was performed with 80 Holter records, 20 normal and 60 with pathological findings. The software organized heart rates in a hierarchical way through their frequencies of occurrence in ranges of 15 beats per min, linearized data, and obtained statistical fractal dimension which allowed the realization of the complexity analysis.
Results: The statistical fractal dimension of the normal Holter records was found between 0,720 and 0,913, and exhibited values between 0,454 and 0,665 in the abnormal Holter records. A Kappa coefficient of 1, and specificity and sensitivity values of 100% were found.
Conclusions: The clinical utility of the Software that automates the methodology based on the Zipf-Mandelbrot law was confirmed, which allowed to evaluate the behavior of normal and acute cardiac systems.


REFERENCES

  1. WHO. [Internet]. 2016. Cardiovascular Diseases. [Citado 21 Jun 2017]. Disponible en: http://www.who.int/cardiovascular_diseases/en /

  2. Abegunde DO, Mathers CD, Adam T, Ortegon M, Strong K. The burden and costs of chronic diseases in low-income and middle-income countries. Lancet. 2007;370:1929-

  3. Ferreira-González I. Epidemiología de la enfermedad coronaria. Revista Española de Cardiología, 2014; 67(2):139-44.

  4. Mandelbrot B. Scaling and power laws without geometry. In: The Fractal Geometry of Nature. San Francisco: Freeman Ed.; 1972, p. 344-8.

  5. Goldberger A, Amaral L, Hausdorff JM, Ivanov P, Peng Ch, Stanley HE. Fractal dynamics in physiology: alterations with disease and aging. PNAS. 2002; 99: 2466-72.

  6. Peitgen H. Length area and dimension. Measuring complexity and scalling properties. En: Chaos and Fractals: New Frontiers of Science. New York: Springer-Verlag; 1992, p. 183-228.

  7. Mandelbrot B. The fractal geometry of nature. Freeman. Barcelona: Tusquets Eds. S.A.; 2000, p. 341-8.

  8. Zipf, GK. The Psycho-Biology of Language: An Introduction to Dynamic Philology. United States: The MIT Press Second Edition. 1965.

  9. Zipf, GK. The human Behavior and the Principle of Least Effort. Cambridge, MA: Addison-Wesley Press; 1949.

  10. Mandelbrot, B. Information theory and psycholinguistics: a theory of words frequencies, in: P. Lazafeld, N. Henry (Eds.), Readings in Mathematical Social Science. Cambridge, MA: MIT Press; 1966.

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

  12. Harris P, Stein PK, Fung GL, Drew BJ. Heart rate variability measured early in patients with evolving acute coronary syndrome and 1-year outcomes of rehospitalization and mortality. Vasc Health Risk Manag. 2014; 10: 451-464.

  13. 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):191-8.

  14. 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. [Internet]. 2015. [Consultado 21/06/017]. 2015:680515. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PM C4452832/pdf/BMRI2015-680515.pdf

  15. Goldberger AL. Is the normal heartbeat chaotic or homeostatic? Physiology. Am Physiological Soc; 1991;6(2):87-91.

  16. Glass L. Chaos and heart rate variability. J Cardiovasc Electrophysiol. 1999;10(10):1358-60.

  17. Lombardi F. Chaos theory, heart rate variability and arrhythmic mortality. Circulation. 2000;101(1):8-10.

  18. Goldberger, AL. & West, BJ. Applications of nonlinear dynamics to clinical cardiology. Ann. N Y Acad. Sci; 1987; 504: 195-213.

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

  20. 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. [internet]. 2015; [Consultado 21/06/017]. 10:6-71. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PM C4354335/pdf/fphys-06-00071.pdf

  21. Nicolini P, Ciulla MM, Malfatto G , Abbate C, Mari D, Rossi PD. Autonomic dysfunction in mild cognitive impairment: evidence from power spectral analysis of heart rate variability in a cross-sectional case-control study. PloS one. [internet]. 2014; [Consultado 21/06/017]. 9(5):e96656. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PM C4011966/pdf/pone.0096656.pdf

  22. Krafty RT, Zhao M, Buysse DJ, Thayer JF, Hall M. Nonparametric spectral analysis of heart rate variability through penalized sum of squares. Statist. Med. 2014; 33(8):1383-94.

  23. Poliakova N, Dionne G, Dubreuil E, Ditto B, Pihl RO, Pérusse D. A methodological comparison of the Porges algorithm, fast Fourier transform, and autoregressive spectral analysis for the estimation of heart rate variability in 5 ‐ month ‐ old infants. Psychophysiology. [internet]. 2014. [Consultado 21/06/017]. 51(6): 579-83. Disponible en: https://onlinelibrary.wiley.com/doi/abs/10.1111 /psyp.12194

  24. Rodríguez J, Prieto S, Correa C, Mendoza F, Weiz G, Soracipa Y, et al. Physical mathematical evaluation of the cardiac dynamic applying the Zipf Mandelbrot law. Journal of Modern Physics. [Internet]. 2015. [Consultado 21/06/017];6:1881- 88. Disponible en: https://www.scirp.org/pdf/JMP_201510291103 2237.pdf

  25. 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 Dec;90(6):062806.

  26. Pincus, SM. Approximate entropy as a measure of system complexity. Proc. Natl Acad. Sci. USA. 1991; 88:2297-2301.

  27. Ksela J, Avbelj V, Kalisnik JM. Multifractality in heartbeat dynamics in patients undergoing beating-heart myocardial revascularization. ComputBiol Med. 2015;60:66-73.

  28. Rodríguez J, Bernal P, Álvarez L, Pabón S, Ibáñez S, Chapuel N, Pérez H, Correa A, Salazar LC, Walteros R. 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.

  29. 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.

  30. Rodríguez J. Método para la predicción de la dinámica temporal de la malaria en los municipios de Colombia. Rev Panam Salud Pública. [Internet]. 2010. [Consultado 21/06/017]; 27(3):211-218. Disponible en: http://www.scielosp.org/scielo.php?script=sci_arttext&pid=S1020-49892010000300008




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