Complex systems variability analysis using approximate entropy

Biological systems are highly complex systems, both spatially and temporally. They are rooted in an interdependent, redundant and pleiotropic interconnected dynamic network. The properties of a system are different from those of their parts, and they depend on the integrity of the whole. The systemi...

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Autor principal: Cuestas, Eduardo
Formato: Artículo revista
Lenguaje:Español
Publicado: Universidad Nacional Córdoba. Facultad de Ciencias Médicas. Secretaria de Ciencia y Tecnología 2010
Materias:
complex systems
approximate entropy
variability
sistemas complejos
entropía aproximada
variabilidad
Acceso en línea:https://revistas.unc.edu.ar/index.php/med/article/view/23423
Aporte de:
Revista de la Facultad de Ciencias Médicas de Córdoba de Universidad Nacional de Córdoba
id I10-R327-article-23423
record_format ojs
institution Universidad Nacional de Córdoba
institution_str I-10
repository_str R-327
container_title_str Revista de la Facultad de Ciencias Médicas de Córdoba
language Español
format Artículo revista
topic complex systems
approximate entropy
variability
sistemas complejos
entropía aproximada
variabilidad
spellingShingle complex systems
approximate entropy
variability
sistemas complejos
entropía aproximada
variabilidad
Cuestas, Eduardo
Complex systems variability analysis using approximate entropy
topic_facet complex systems
approximate entropy
variability
sistemas complejos
entropía aproximada
variabilidad
author Cuestas, Eduardo
author_facet Cuestas, Eduardo
author_sort Cuestas, Eduardo
title Complex systems variability analysis using approximate entropy
title_short Complex systems variability analysis using approximate entropy
title_full Complex systems variability analysis using approximate entropy
title_fullStr Complex systems variability analysis using approximate entropy
title_full_unstemmed Complex systems variability analysis using approximate entropy
title_sort complex systems variability analysis using approximate entropy
description Biological systems are highly complex systems, both spatially and temporally. They are rooted in an interdependent, redundant and pleiotropic interconnected dynamic network. The properties of a system are different from those of their parts, and they depend on the integrity of the whole. The systemic properties vanish when the system breaks down, while the properties of its components are maintained. The disease can be understood as a systemic functional alteration of the human body, which present with a varying severity, stability and durability.Biological systems are characterized by measurable complex rhythms, abnormal rhythms are associated with disease and may be involved in its pathogenesis, they are been termed "dynamic disease." Physicians have long time recognized that alterations of physiological rhythms are associated with disease. Measuring absolute values of clinical parameters yields highly significant, clinically useful information, however evaluating clinical parameters the variability provides additionally useful clinical information. The aim of this review was to study one of the most recent advances in the measurement and characterization of biological variability made possible by the development of mathematical models based on chaos theory and nonlinear dynamics, as approximate entropy, has provided us with greater ability to discern meaningful distinctions between biological signals from clinically distinct groups of patients.
publisher Universidad Nacional Córdoba. Facultad de Ciencias Médicas. Secretaria de Ciencia y Tecnología
publishDate 2010
url https://revistas.unc.edu.ar/index.php/med/article/view/23423
work_keys_str_mv AT cuestaseduardo complexsystemsvariabilityanalysisusingapproximateentropy
AT cuestaseduardo analisisdelavariabilidaddelossistemascomplejosutilizandoentropiaaproximada
first_indexed 2024-09-03T21:00:28Z
last_indexed 2024-09-03T21:00:28Z
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spelling I10-R327-article-234232024-08-27T18:24:25Z Complex systems variability analysis using approximate entropy Análisis de la variabilidad de los sistemas complejos utilizando entropía aproximada Cuestas, Eduardo complex systems approximate entropy variability sistemas complejos entropía aproximada variabilidad Biological systems are highly complex systems, both spatially and temporally. They are rooted in an interdependent, redundant and pleiotropic interconnected dynamic network. The properties of a system are different from those of their parts, and they depend on the integrity of the whole. The systemic properties vanish when the system breaks down, while the properties of its components are maintained. The disease can be understood as a systemic functional alteration of the human body, which present with a varying severity, stability and durability.Biological systems are characterized by measurable complex rhythms, abnormal rhythms are associated with disease and may be involved in its pathogenesis, they are been termed "dynamic disease." Physicians have long time recognized that alterations of physiological rhythms are associated with disease. Measuring absolute values of clinical parameters yields highly significant, clinically useful information, however evaluating clinical parameters the variability provides additionally useful clinical information. The aim of this review was to study one of the most recent advances in the measurement and characterization of biological variability made possible by the development of mathematical models based on chaos theory and nonlinear dynamics, as approximate entropy, has provided us with greater ability to discern meaningful distinctions between biological signals from clinically distinct groups of patients. Los sistemas biológicos son sistemas altamente complejos, tanto espacial como temporalmente. Los mismos están cimentados en una red dinámica interconectada marcadamente interdependiente, redundante y pleiotrópica. Las propiedades de un sistema son distintas de las de sus partes, aunque ellas dependen de la integridad del todo. Las propiedades sistémicas desaparecen cuando el sistema se rompe, mientras que las propiedades de sus componentes se mantienen. La enfermedad puede entenderse como una alteración funcional sistémica del organismo humano, y se presenta con una enorme variabilidad en los patrones de severidad, estabilidad y duración. Los sistemas biológicos complejos se caracterizar por presentar ritmos o ciclos medibles y objetivables, los ritmos anormales están asociados a la enfermedad y pueden estar involucrados en su patogénesis, este fenómeno se denomina “enfermedad dinámica”. Los médicos hace más de 20 siglos que reconocen la asociación de las alteraciones de los ritmos fisiológicos con la presencia de enfermedad. La medición de los valores absolutos de los parámetros clínicos es una fuente altamente significativa y relevante de datos sobre el estado de los pacientes, sin embargo la medición de la variabilidad de estos ofrece una muy valiosa información adicional. El objetivo de esta revisión fue estudiar uno de los avances más recientes en la medición y caracterización de la variabilidad biológica posibilitados por el desarrollo de modelos matemáticos basados en la teoría del caos y de las dinámicas no lineales, como el cálculo de la entropía aproximada, que ofrece una herramienta para poder discernir el significado de las diferencias en la variabilidad de los signos biológicos en diferentes grupos de pacientes. Universidad Nacional Córdoba. Facultad de Ciencias Médicas. Secretaria de Ciencia y Tecnología 2010-07-01 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://revistas.unc.edu.ar/index.php/med/article/view/23423 10.31053/1853.0605.v67.n2.23423 Revista de la Facultad de Ciencias Médicas de Córdoba.; Vol. 67 No. 2 (2010); 77-80 Revista de la Facultad de Ciencias Médicas de Córdoba; Vol. 67 Núm. 2 (2010); 77-80 Revista da Faculdade de Ciências Médicas de Córdoba; v. 67 n. 2 (2010); 77-80 1853-0605 0014-6722 10.31053/1853.0605.v67.n2 spa https://revistas.unc.edu.ar/index.php/med/article/view/23423/23145 Derechos de autor 2010 Universidad Nacional de Córdoba https://creativecommons.org/licenses/by-nc/4.0

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