On the evidence of deterministic chaos in ECG: Surrogate and predictability analysis

Govindan, Rathinaswamy B.; Narayanan, K.; Gopinathan, M. S.

doi:10.1063/1.166330

Cited by 107 publications

(55 citation statements)

References 38 publications

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“…Physiological time series of different origin have been investigated. For previous years, based on the correlation integral approach, it was suggested that there is some evidence of nonlinear structure in normal heart dynamics (Lefebvre and Fallen, 1993;Govindan and Gopinathan, 1998). Moreover it has been established for physiological data sets of different origin that a high degree of variability is often a feature of young healthy hearts, and that an increase in variability of physiological characteristics is common for aging and diseases (Zokhowski.M.…”

Section: Detection Of Dynamical Changes In Arterial Pressure Data Setsmentioning

confidence: 99%

Identification of Complex Processes Based on Analysis of Phase Space Structures

Matcharashvili

Chélidzé

Janiashvili³

Imaging for Detection and Identification

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Abstract. The problem of investigation of temporal and/or spatial behavior of highly nonlinear or complex natural systems has long been of fundamental scientific interest. At the same time it is presently well understood that identification of dynamics of processes in complex natural systems, through their qualitative description and quantitative evaluation, is far from a purely academic question and has an essential practical importance. This is quite understandable as systems with complex dynamics abound in nature and examples can be found in very different areas such as medicine and biology (rhythms, physiological cycles, epidemics), atmosphere (climate and weather change), geophysics (tides, earthquakes, volcanoes, magnetic field variations), economy (financial markets behavior, exchange rates), engineering (friction, fracturing), communication (electronic networks, internet packet dynamics) etc. The past two decades of research on qualitative and especially quantitative investigations of dynamics of real processes of different origin brought significant progress in the understanding of behavior of natural processes. At the same time serious drawbacks have also been revealed. This is why exhaustive investigation of dynamical properties of complex processes for scientific, engineering or practical purposes is now recognized as one of the main scientific challenges. Much attention is paid to elaboration of appropriate methods aiming to measuring the complexity of both global and local dynamical behaviors from the observed data sets -time series. This chapter presents a short overview of modern methods of qualitative and quantitative evaluation of dynamics of complex natural processes such as calculation of Lyapunov exponents and fractal dimensions, recurrence plots and recurrence quantification analysis. Other related methods are also described. The traditional approach to studying dynamical behavior of complex nonlinear systems is to reconstruct from observation scalar time series state or phase space plot. This graph indicates how the systems behavior changes over the time. We focus on the methods of identification and quantitative evaluation of complex dynamics that are based on the testing of evolutionary and geometric properties of phase space graphs as images of investigated complex dynamics. For practical examples of the application c 2006 Springer. Printed in the Netherlands.of nonlinear methods for identification of complex natural processes, our results on medical, geophysical, hydrological, and stick-slip time series analysis are presented.

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Section: Detection Of Dynamical Changes In Arterial Pressure Data Setsmentioning

confidence: 99%

Identification of Complex Processes Based on Analysis of Phase Space Structures

Matcharashvili

Chélidzé

Janiashvili³

Imaging for Detection and Identification

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“…Deterministic chaos defined as a complex, disordered behavior generated by nonlinear systems (Ott, 2002) is a widely studied phenomenon which has been discovered and observed in practice in many different areas like liquids (Procaccia and Meron, 1986;Langenberg et al, 2004), lasers (Yamada and Graham, 1980;Used and Martin, 2010), plasma (Held et al, 1984;Banerjee et al, 2010), mechanics (Chui and Ma, 1982;Ott, 2002), electronics (Gunaratne et al, 1989;Yim et al, 2004), chemistry (Argoul et al, 1987;Cordoba et al, 2006), biology-the action of the human heart (Govindan et al, 1998) and the brain (Andrzejak et al, 2001;Gautama et al, 2003), and in many others.…”

Section: Introductionmentioning

confidence: 99%

Efficiency analysis of control algorithms in spatially distributed systems with chaotic behavior

Korus

2014

International Journal of Applied Mathematics and Computer Science

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The paper presents results of examination of control algorithms for the purpose of controlling chaos in spatially distributed systems like the coupled map lattice (CML). The mathematical definition of the CML, stability analysis as well as some basic results of numerical simulation exposing complex, spatiotemporal and chaotic behavior of the CML were already presented in another paper. The main purpose of this article is to compare the efficiency of controlling chaos by simple classical algorithms in spatially distributed systems like CMLs. This comparison is made based on qualitative and quantitative evaluation methods proposed in the previous paper such as the indirect Lyapunov method, Lyapunov exponents and the net direction phase indicator. As a summary of this paper, some conclusions which can be useful for creating a more efficient algorithm of controlling chaos in spatially distributed systems are made.

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“…The method consists of generating, from the original series, a set of random series that maintain some of the properties of the original series (mean, variance, spectrum, self-correlation structure), but in which any non-linear dependence has been eliminated [214,215]. Once the new set has been generated, a statistical index sensitive to the non-linear dependence in the original time series is calculated both in the original series and in the surrogate series.…”

Section: Surrogates Methodsmentioning

confidence: 99%

Contributions to the Development of Objective Techniques for Presence Measurement in Virtual Environments by means of Brain Activity Analysis

Solaz¹

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AcknowledgmentsMe gustaría expresar mi agradecimiento a todas las personas que me han apoyado para que este trabajo saliera adelante.En primer lugar, quiero agradecerle a mi director de tesis, Mariano Alcañiz, por haber confiado siempre en mí, por toda su ayuda y por haber creído en este proyecto.También me gustaría dar las gracias a todos mis compañeros de LabHuman durante estos años. Ha sido mucho tiempo el que hemos pasado juntos y su presencia siempre ha hecho más llevadero y agradable el trabajo.También ha sido fundamental el apoyo del equipo de LabPsitec, con tantos proyectos juntos, tantos congresos y tantos buenos consejos. Muchas gracias.Por otra parte, quiero transmitir un agradecimiento especial a todas las personas que han participado de una u otra manera en el desarrollo de este trabajo.Sobre todo, quiero agradecer a Pepe Tembl y Vera Parkhutik, doctores del Hospital Universitari la Fe, por las tardes pasadas en el Politécnico para poder llevar a cabo todas las sesiones experimentales y por toda su ayuda.vi También quiero dar las gracias a Valery Naranjo y Xavier Bornàs, por su apoyo y consejos en los análisis de las señales.Otra persona que cabe destacar y sin la cual no se hubiera podido llevar a cabo este proyecto ha sido José Lores, de la empresa Almevan, S.L. Le agradezco todas sus facilidades para poder disponer del equipo de Doppler Transcraneal utilizado en las pruebas.Y, porúltimo, pero no menos importante, quiero transmitir mi más sincero agradecimiento a todas las personas que voluntariamente han pasado por los experimentos que se han llevado a cabo en la presente tesis.También me gustaría dar las gracias a todas las personas de mi entorno personal, amigos y familia, que han estado a mi lado durante este tiempo.Quiero dar las gracias a José María, María José, César y David, por escucharme, por sus consejos y por su apoyo durante toda esta etapa.También me gustaría acordarme en estos agradecimientos de mis abuelos Antonio y Remedios y de mi tía Gloria, a los que tengo tan presentes, por todo su amor y por su capacidad de emocionarse con mis estudios aunque no los comprendieran.Me gustaría dar las gracias especialmente a mis padres Juan y Clotilde, porque han estado siempre a mi lado y todo lo que me han transmitido desde niña ha sido la base que ha hecho posible que esta tesis se llevara a cabo. También quiero darle las gracias a Pablo y Mari Carmen, que comprenden muy bien el camino que he tenido que recorrer para llegar aquí.Finalmente, doy las gracias a Jose, por darme la fuerza para poder llevar a cabo mis proyectos, por estar a mi lado siempre, por comprenderme, por apoyarme... Sencillamente, gracias por estar ahí. vii ResumenEl concepto de presencia, entendida como la sensación de "estar ahí", en un entorno virtual, aunque se esté físicamente en un lugar diferente (por ejemplo, en el laboratorio), ha sido ampliamente estudiado en elámbito de la realidad virtual para comprender mejor los mecanismos psicológicos que acompañan a las experiencias virtuales.En la literatura, se han propue...

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On the evidence of deterministic chaos in ECG: Surrogate and predictability analysis

Cited by 107 publications

References 38 publications

Identification of Complex Processes Based on Analysis of Phase Space Structures

Identification of Complex Processes Based on Analysis of Phase Space Structures

Efficiency analysis of control algorithms in spatially distributed systems with chaotic behavior

Contributions to the Development of Objective Techniques for Presence Measurement in Virtual Environments by means of Brain Activity Analysis

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