Fractal and multifractal study of heartbeat interval time series analysis in young patients with metabolic syndrome

A. Muñoz Diosdado, G. Gálvez Coyt

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

A wider range of complex structures and systems of interest have in recent years been quantitatively characterized using the idea of a fractal dimension, in particular, the physiological systems which have extraordinary complexity. The non-stationarity and non-linearity of signals generated by living organisms defy traditional mechanistic approaches based on homeostasis and conventional biostatistical methodologies. This complexity has fueled growing interest in applying concepts and techniques from statistical physics, including chaos theory, to a wide range of biomedical problems from molecular to organismic levels. The data obtained when studying a complex system often appears as a time series, in principle it seems to be that they lack useful information. Nevertheless, a more detailed analysis with methods and techniques originated in statistical physics and non-linear dynamics can show the existence of correlations that are used to characterize complex systems. In this work, we have three objectives; first, we introduce methods from non-linear dynamics used to analyze time series: multifractal analysis, detrended fluctuation analysis (DFA) and the method of the Higuchi's fractal dimension. Second, we describe the construction of a database focused on the study of heart variability of young subjects with metabolic syndrome (MS). The syndrome consists of having 3 or more of the following problems: central obesity, alterations in the metabolism of the glucose (with or without Diabetes Mellitus and resistance to the insulin), arterial hypertension, dyslipidemia with diminished HDL cholesterol and hypertriglyceridemia. The database contains 24 hour electrocardiogram records taken with a Holter and their corresponding RR interval time series. The database currently has 90 subjects data, approximately one third are healthy young and the remaining are young people diagnosed with MS. Third, we examine the RR time series of these young university students (17 to 24 years old) with MS. Starting from the analysis of the heartbeat time series we used the DFA method, the Higuchi's fractal dimension method and the multifractal analysis to locate the possible presence of heart problems. The results show that although the young persons have metabolic syndrome, the majority do not present alterations in the heart dynamics. However, there were cases where the used fractal parameter values differ significantly from the healthy people values. This suggests carrying out a more detailed exam and a plan for those young persons in order to change their feeding habits and their physical activity routines. © 2013 by Nova Science Publishers, Inc. All rights reserved.
Original languageAmerican English
Title of host publicationBiotechnology: Health, Food, Energy and Environment Applications
Number of pages183
ISBN (Electronic)9781620810712
StatePublished - 1 Dec 2012

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Fractals
Nonlinear Dynamics
Physics
Databases
Abdominal Obesity
Hypertriglyceridemia
Dyslipidemias
HDL Cholesterol
Habits
Insulin Resistance
Diabetes Mellitus
Electrocardiography
Homeostasis
Exercise
Students
Hypertension
Glucose

Cite this

Diosdado, A. M., & Coyt, G. G. (2012). Fractal and multifractal study of heartbeat interval time series analysis in young patients with metabolic syndrome. In Biotechnology: Health, Food, Energy and Environment Applications
Diosdado, A. Muñoz ; Coyt, G. Gálvez. / Fractal and multifractal study of heartbeat interval time series analysis in young patients with metabolic syndrome. Biotechnology: Health, Food, Energy and Environment Applications. 2012.
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Diosdado, AM & Coyt, GG 2012, Fractal and multifractal study of heartbeat interval time series analysis in young patients with metabolic syndrome. in Biotechnology: Health, Food, Energy and Environment Applications.

Fractal and multifractal study of heartbeat interval time series analysis in young patients with metabolic syndrome. / Diosdado, A. Muñoz; Coyt, G. Gálvez.

Biotechnology: Health, Food, Energy and Environment Applications. 2012.

Research output: Chapter in Book/Report/Conference proceedingChapter

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AB - A wider range of complex structures and systems of interest have in recent years been quantitatively characterized using the idea of a fractal dimension, in particular, the physiological systems which have extraordinary complexity. The non-stationarity and non-linearity of signals generated by living organisms defy traditional mechanistic approaches based on homeostasis and conventional biostatistical methodologies. This complexity has fueled growing interest in applying concepts and techniques from statistical physics, including chaos theory, to a wide range of biomedical problems from molecular to organismic levels. The data obtained when studying a complex system often appears as a time series, in principle it seems to be that they lack useful information. Nevertheless, a more detailed analysis with methods and techniques originated in statistical physics and non-linear dynamics can show the existence of correlations that are used to characterize complex systems. In this work, we have three objectives; first, we introduce methods from non-linear dynamics used to analyze time series: multifractal analysis, detrended fluctuation analysis (DFA) and the method of the Higuchi's fractal dimension. Second, we describe the construction of a database focused on the study of heart variability of young subjects with metabolic syndrome (MS). The syndrome consists of having 3 or more of the following problems: central obesity, alterations in the metabolism of the glucose (with or without Diabetes Mellitus and resistance to the insulin), arterial hypertension, dyslipidemia with diminished HDL cholesterol and hypertriglyceridemia. The database contains 24 hour electrocardiogram records taken with a Holter and their corresponding RR interval time series. The database currently has 90 subjects data, approximately one third are healthy young and the remaining are young people diagnosed with MS. Third, we examine the RR time series of these young university students (17 to 24 years old) with MS. Starting from the analysis of the heartbeat time series we used the DFA method, the Higuchi's fractal dimension method and the multifractal analysis to locate the possible presence of heart problems. The results show that although the young persons have metabolic syndrome, the majority do not present alterations in the heart dynamics. However, there were cases where the used fractal parameter values differ significantly from the healthy people values. This suggests carrying out a more detailed exam and a plan for those young persons in order to change their feeding habits and their physical activity routines. © 2013 by Nova Science Publishers, Inc. All rights reserved.

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Diosdado AM, Coyt GG. Fractal and multifractal study of heartbeat interval time series analysis in young patients with metabolic syndrome. In Biotechnology: Health, Food, Energy and Environment Applications. 2012