The Cardiac Diagnostic Prediction Applied to a Designed Holter
We have designed a Holter that measures the heart´s activity for over 24 hours, implemented a prediction methodology, and generate alarms as well as indicators to patients and treating physicians. Various diagnostic advances have been developed in clinical cardiology thanks to Holter implementation; however, their interpretation has largely been conditioned to clinical analysis and measurements adjusted to diverse population characteristics, thus turning it into a subjective examination. This, however, requires vast population studies to be validated that, in turn, have not achieved the ultimate goal: mortality prediction. Given this context, our Insight Research Group developed a mathematical methodology that assesses cardiac dynamics through entropy and probability, creating a numerical and geometrical attractor which allows quantifying the normalcy of chronic and acute disease as well as the evolution between such states, and our Tigum Research Group developed a holter device with 12 channels and advanced computer software. This has been shown in different contexts with 100% sensitivity and specificity results.
[1] Miranda JJ, Kinra S, Casas JP, Davey Smith G, Ebrahim S. Non-communicable diseases in low- and middle-income countries: context, determinants and health policy. Trop Med Int Health 2008; 13:1225-34
[2] WHO- Media center. Cardiovascular diseases (CVDs). Fact sheet September 2017. Available on https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(DVDs). Access: March, 2019
[3] Devaney R. A first course in chaotic dynamical systems theory and experiments. Reading Mass: Addison-Wesley 1992.
[4] Peitgen H, Jürgens H, Saupe D. Strange attractors, the locus of chaos. En: Chaos and Fractals: New Frontiers of Science. New York: Springer-Verlag. 1992. pp. 655-768.
[5] Calabrese JL. Ampliando las fronteras del reduccionismo. Deducción y sistemas no lineales. Psicoanálisis APdeBA. 1999; 21(3):431-453.
[6] Feynman RP, Leighton RB, Sands M. Probabilidad. En: Física. Feynman RP, Leighton RB, Sands M. Física. Vol. 1. Primera edición Wilmington: Addison-Wesley Iberoamericana, S. A. México. 1998. p. 6-1, 6-16.
[7] Kostic, M.M. The Elusive Nature of Entropy and Its Physical Meaning. Entropy 2014, 16, 953-967.
[8] Feynman RP, Leighton RB, Sands M. Leyes de la termodinámica. En: Física. Feynman RP, Leighton RB, Sands M. Física. Vol. 1. Primera edición Wilmington: Addison-Wesley Iberoamericana, S. A. México. 1998. p. 44-1, 44-19.
[9] Frodden E, Royo J. Entropía e información, Seminario Final del curso de Termodinámica, Depto. de Física, Facultad de Ciencias, Universidad de Chile, 2004. Disponible en: URL: http://fisica.ciencias.uchile.cl/~gonzalo/cursos/termo_II-04/seminarios/seminarios1.htm. Consultado: febrero 6 2012.
[10] Shore J. Relative Entropy, Probabilistic Inference and AI. 2013. Disponible en: http://arxiv.org/abs/1304.3423
[11] Pineda M, Matiz H, Rozo R. Enfermedad coronaria. Bogotá: Editorial Kimpres Ltda. 2002.
[12] Rodríguez J. Entropía Proporcional de los sistemas dinámicos cardiacos: Predicciones físicas y matemáticas de la dinámica cardiaca de aplicación clínica. Rev Colomb Cardiol. 2010; 17:115-129 J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
[13] Rodríguez, Javier, Signed Prieto, and Leonardo Juan Ramírez López. "A novel heart rate attractor for the prediction of cardiovascular disease." Informatics in Medicine Unlocked 15 (2019): 100174. DOI: https://doi.org/10.1016/j.imu.2019.100174
[14] Rodríguez J. Entropía Proporcional de los sistemas dinámicos cardiacos: Predicciones físicas y matemáticas de la dinámica cardiaca de aplicación clínica. Rev Colomb Cardiol. 2010; 17:115-129.
[15] Javier Rodríguez, Fernán Medoza, Nelly Velásquez. Clinical application to Arrhythmic of entropy proportion. J Nucl Med Radiat Ther. 2015 Ag;6: Insue 4.
[16] Rodríguez J. Mathematical law of chaotic cardiac dynamic: Predictions of clinic application. J Med. Med. Sci. 2011; 2(8):1050-1059.
[17] Rodríguez J, Prieto S, Dominguez D, Correa C, Melo M, Pardo J, Mendoza F, Rodríguez L, Cardona DM, Méndez L. Application of the chaotic power law to cardiac dynamics in patients with arrhythmias. Rev. Fac. Med. 2014;62(4):539-46.
[18] Rodríguez J, Prieto S, Correa C, Oliveros H, Soracipa Y, Méndez L et al. Diagnóstico físico-matemático de la dinámica cardiaca a partir de sistemas dinámicos y geometría fractal: disminución del tiempo de evaluación de la dinámica cardiaca de 21 a 16 horas. Acta Colomb Cuid Intensivo. 2016; 16(1):15-22.
[19] 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):209-220.
[20] Wu GQ, Arzeno NM, Shen LL, Tang DK, Zheng DA, Zhao NQ, Eckberg DL, Poon CS. Chaotic Signatures of Heart Rate Variability and Its Power Spectrum in Health, Aging and Heart Failure. PLoS ONE. 2009; 4(2): e4323.
[21] Braun C, Kowallik P, Freking A, Hadeler D, Kniffki K, Meesmann M. Demonstration of nonlinear components in heart rate variability of healthy persons Am. J. Physiol. 1998; 275, H1577–H1584.
[22] 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 - 2472.
[23] Huikuri HV, Mäkikallio TH, Peng Ch, Goldberger AL, Hintze U, Moller 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: 47-53.
[24] Perkiomaki J, Mäkikallio T, Huikuri H. Fractal and complexity measures of heart rate variability. En: Clinical and Experimental Hypertension. 2005; 27(2-3):149-158.
[25] Voss A, Schulz S, Schroeder R, Baumert M, Caminal P. Methods derived from nonlinear dynamics for analyzing heart rate variability. Philosophical Transactions of Royal Society A. 2009; 367(1887): 277-296.
[26] M. A. Garcia-Gonzalez and R. Pallas-Areny, "A novel robust index to assess beat-to-beat variability in heart rate time-series analysis," in IEEE Transactions on Biomedical Engineering, vol. 48, no. 6, pp. 617-621, June 2001. DOI: 10.1109/10.923779
[27] 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.2014; 6(8): 102-108
[1] Miranda JJ, Kinra S, Casas JP, Davey Smith G, Ebrahim S. Non-communicable diseases in low- and middle-income countries: context, determinants and health policy. Trop Med Int Health 2008; 13:1225-34
[2] WHO- Media center. Cardiovascular diseases (CVDs). Fact sheet September 2017. Available on https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(DVDs). Access: March, 2019
[3] Devaney R. A first course in chaotic dynamical systems theory and experiments. Reading Mass: Addison-Wesley 1992.
[4] Peitgen H, Jürgens H, Saupe D. Strange attractors, the locus of chaos. En: Chaos and Fractals: New Frontiers of Science. New York: Springer-Verlag. 1992. pp. 655-768.
[5] Calabrese JL. Ampliando las fronteras del reduccionismo. Deducción y sistemas no lineales. Psicoanálisis APdeBA. 1999; 21(3):431-453.
[6] Feynman RP, Leighton RB, Sands M. Probabilidad. En: Física. Feynman RP, Leighton RB, Sands M. Física. Vol. 1. Primera edición Wilmington: Addison-Wesley Iberoamericana, S. A. México. 1998. p. 6-1, 6-16.
[7] Kostic, M.M. The Elusive Nature of Entropy and Its Physical Meaning. Entropy 2014, 16, 953-967.
[8] Feynman RP, Leighton RB, Sands M. Leyes de la termodinámica. En: Física. Feynman RP, Leighton RB, Sands M. Física. Vol. 1. Primera edición Wilmington: Addison-Wesley Iberoamericana, S. A. México. 1998. p. 44-1, 44-19.
[9] Frodden E, Royo J. Entropía e información, Seminario Final del curso de Termodinámica, Depto. de Física, Facultad de Ciencias, Universidad de Chile, 2004. Disponible en: URL: http://fisica.ciencias.uchile.cl/~gonzalo/cursos/termo_II-04/seminarios/seminarios1.htm. Consultado: febrero 6 2012.
[10] Shore J. Relative Entropy, Probabilistic Inference and AI. 2013. Disponible en: http://arxiv.org/abs/1304.3423
[11] Pineda M, Matiz H, Rozo R. Enfermedad coronaria. Bogotá: Editorial Kimpres Ltda. 2002.
[12] Rodríguez J. Entropía Proporcional de los sistemas dinámicos cardiacos: Predicciones físicas y matemáticas de la dinámica cardiaca de aplicación clínica. Rev Colomb Cardiol. 2010; 17:115-129 J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
[13] Rodríguez, Javier, Signed Prieto, and Leonardo Juan Ramírez López. "A novel heart rate attractor for the prediction of cardiovascular disease." Informatics in Medicine Unlocked 15 (2019): 100174. DOI: https://doi.org/10.1016/j.imu.2019.100174
[14] Rodríguez J. Entropía Proporcional de los sistemas dinámicos cardiacos: Predicciones físicas y matemáticas de la dinámica cardiaca de aplicación clínica. Rev Colomb Cardiol. 2010; 17:115-129.
[15] Javier Rodríguez, Fernán Medoza, Nelly Velásquez. Clinical application to Arrhythmic of entropy proportion. J Nucl Med Radiat Ther. 2015 Ag;6: Insue 4.
[16] Rodríguez J. Mathematical law of chaotic cardiac dynamic: Predictions of clinic application. J Med. Med. Sci. 2011; 2(8):1050-1059.
[17] Rodríguez J, Prieto S, Dominguez D, Correa C, Melo M, Pardo J, Mendoza F, Rodríguez L, Cardona DM, Méndez L. Application of the chaotic power law to cardiac dynamics in patients with arrhythmias. Rev. Fac. Med. 2014;62(4):539-46.
[18] Rodríguez J, Prieto S, Correa C, Oliveros H, Soracipa Y, Méndez L et al. Diagnóstico físico-matemático de la dinámica cardiaca a partir de sistemas dinámicos y geometría fractal: disminución del tiempo de evaluación de la dinámica cardiaca de 21 a 16 horas. Acta Colomb Cuid Intensivo. 2016; 16(1):15-22.
[19] 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):209-220.
[20] Wu GQ, Arzeno NM, Shen LL, Tang DK, Zheng DA, Zhao NQ, Eckberg DL, Poon CS. Chaotic Signatures of Heart Rate Variability and Its Power Spectrum in Health, Aging and Heart Failure. PLoS ONE. 2009; 4(2): e4323.
[21] Braun C, Kowallik P, Freking A, Hadeler D, Kniffki K, Meesmann M. Demonstration of nonlinear components in heart rate variability of healthy persons Am. J. Physiol. 1998; 275, H1577–H1584.
[22] 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 - 2472.
[23] Huikuri HV, Mäkikallio TH, Peng Ch, Goldberger AL, Hintze U, Moller 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: 47-53.
[24] Perkiomaki J, Mäkikallio T, Huikuri H. Fractal and complexity measures of heart rate variability. En: Clinical and Experimental Hypertension. 2005; 27(2-3):149-158.
[25] Voss A, Schulz S, Schroeder R, Baumert M, Caminal P. Methods derived from nonlinear dynamics for analyzing heart rate variability. Philosophical Transactions of Royal Society A. 2009; 367(1887): 277-296.
[26] M. A. Garcia-Gonzalez and R. Pallas-Areny, "A novel robust index to assess beat-to-beat variability in heart rate time-series analysis," in IEEE Transactions on Biomedical Engineering, vol. 48, no. 6, pp. 617-621, June 2001. DOI: 10.1109/10.923779
[27] 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.2014; 6(8): 102-108
@article{"International Journal of Medical, Medicine and Health Sciences:79634", author = "Leonardo Juan Ramírez López and Javier Oswaldo Rodriguez Velasquez", title = "The Cardiac Diagnostic Prediction Applied to a Designed Holter", abstract = "We have designed a Holter that measures the heart´s activity for over 24 hours, implemented a prediction methodology, and generate alarms as well as indicators to patients and treating physicians. Various diagnostic advances have been developed in clinical cardiology thanks to Holter implementation; however, their interpretation has largely been conditioned to clinical analysis and measurements adjusted to diverse population characteristics, thus turning it into a subjective examination. This, however, requires vast population studies to be validated that, in turn, have not achieved the ultimate goal: mortality prediction. Given this context, our Insight Research Group developed a mathematical methodology that assesses cardiac dynamics through entropy and probability, creating a numerical and geometrical attractor which allows quantifying the normalcy of chronic and acute disease as well as the evolution between such states, and our Tigum Research Group developed a holter device with 12 channels and advanced computer software. This has been shown in different contexts with 100% sensitivity and specificity results.
", keywords = "Entropy, mathematical, prediction, cardiac, holter, attractor.", volume = "14", number = "5", pages = "147-7", }
