New computational approaches to the analysis of interbeat intervals in human subjects

Tabar, M. Reza Rahimi; Ghasemi, Fatemeh; Peinke, Joachim; Friedrich, Rudolf; Kaviani, Kamran; Taghavi, Farshid; Sadeghi, Sabereh; Bizhani, G.; Sahimi, Muhammad

doi:10.1109/mcse.2006.29

Cited by 21 publications

(24 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, HRV can be described by the Langevin equation X′(t)=D1(X,t)+sqrt(2D2(X,t))Γ(t), where X(t) is time-series data, Γ(t) is Gaussian noise with zero mean value, D1(X,t) and D2(X,t) are first and second order coefficients of Kramers-Moyal expansion (Buchner et al, 2009; Petelczyc et al, 2009). By extracting from the data first D1(X,t) and second D2(X,t) terms, and neglecting the higher order term due to its small value, the HRV can be reconstructed through the Langevin equation (Tabar et al, 2006). It provides an opportunity to use this method for modeling of HRV.…”

Section: Methods Applying For Quantitative Analysis Of Hrv and Rsamentioning

confidence: 99%

Cardiorespiratory coupling in health and disease

Garcia¹,

Koschnitzky²,

Dashevskiy³

et al. 2013

Autonomic Neuroscience

View full text Add to dashboard Cite

Cardiac and respiratory activities are intricately linked both functionally as well as anatomically through highly overlapping brainstem networks controlling these autonomic physiologies that are essential for survival. Cardiorespiratory coupling (CRC) has many potential benefits creating synergies that promote healthy physiology. However, when such coupling deteriorates autonomic dysautonomia may ensue. Unfortunately there is still an incomplete mechanistic understanding of both normal and pathophysiological interactions that respectively give rise to CRC and cardiorespiratory dysautonomia. Moreover, there is also a need for better quantitative methods to assess CRC. This review addresses the current understanding of CRC by discussing: (1) the neurobiological basis of respiratory sinus arrhythmia (RSA); (2) various disease states involving cardiorespiratory dysautonomia; and (3) methodologies measuring heart rate variability and RSA.

show abstract

Section: Methods Applying For Quantitative Analysis Of Hrv and Rsamentioning

confidence: 99%

Cardiorespiratory coupling in health and disease

Garcia¹,

Koschnitzky²,

Dashevskiy³

et al. 2013

Autonomic Neuroscience

View full text Add to dashboard Cite

show abstract

“…The first results 15,16 showed a difference in the functional dependence of the drift and diffusion coefficients between normal subjects and congestive heart failure ͑CHF͒ patients. Below, we extended this analysis to two simultaneous time series: RR interval and respiratory interval data.…”

Section: Methodsmentioning

confidence: 99%

“…We analyzed the polysomnographic recordings of two healthy individuals. We calculated the first two coefficients of the Kramersa͒ Moyal expansion [15][16][17] decomposing each rhythm into a sum of the deterministic drift term and a diffusion term. We show that the functional form of the dependence of the drift term for both dynamic variables is practically linear indicating an oscillatory process in both cases.…”

Section: Introductionmentioning

confidence: 99%

On the nature of heart rate variability in a breathing normal subject: A stochastic process analysis

Buchner

Petelczyc

Żebrowski

et al. 2009

Chaos: An Interdisciplinary Journal of Nonlinear Science

View full text Add to dashboard Cite

Human heart rate is moderated by the autonomous nervous system acting predominantly through the sinus node (the main cardiac physiological pacemaker). One of the dominant factors that determine the heart rate in physiological conditions is its coupling with the respiratory rhythm. Using the language of stochastic processes, we analyzed both rhythms simultaneously taking the data from polysomnographic recordings of two healthy individuals. Each rhythm was treated as a sum of a deterministic drift term and a diffusion term (Kramers-Moyal expansion). We found that normal heart rate variability may be considered as the result of a bidirectional coupling of two nonlinear oscillators: the heart itself and the respiratory system. On average, the diffusion (noise) component measured is comparable in magnitude to the oscillatory (deterministic) term for both signals investigated. The application of the Kramers-Moyal expansion may be useful for medical diagnostics providing information on the relation between respiration and heart rate variability. This interaction is mediated by the autonomous nervous system, including the baroreflex, and results in a commonly observed phenomenon--respiratory sinus arrhythmia which is typical for normal subjects and often impaired by pathology.

show abstract

“…In previous studies, the model based on the Langevin equation was used in the reconstruction of heart rate variability time series from healthy and pathological cases (Petelczyc et al, 2009;Tabar et al, 2006;Kuusela, 2004). In addition, previous analysis showed significant differences in the functional dependence of the drift term on the rescaled magnitude of the RR interval between healthy subjects and congestive heart failure patients.…”

Section: Introductionmentioning

confidence: 99%

A fixed mass method for the Kramers-Moyal expansion—Application to time series with outliers

Petelczyc

Żebrowski

Orłowska−Baranowska

2015

Chaos: An Interdisciplinary Journal of Nonlinear Science

View full text Add to dashboard Cite

Extraction of stochastic and deterministic components from empirical data-necessary for the reconstruction of the dynamics of the system-is discussed. We determine both components using the Kramers-Moyal expansion. In our earlier papers, we obtained large fluctuations in the magnitude of both terms for rare or extreme valued events in the data. Calculations for such events are burdened by an unsatisfactory quality of the statistics. In general, the method is sensitive to the binning procedure applied for the construction of histograms. Instead of the commonly used constant width of bins, we use here a constant number of counts for each bin. This approach-the fixed mass method-allows to include in the calculation events, which do not yield satisfactory statistics in the fixed bin width method. The method developed is general. To demonstrate its properties, here, we present the modified Kramers-Moyal expansion method and discuss its properties by the application of the fixed mass method to four representative heart rate variability recordings with different numbers of ectopic beats. These beats may be rare events as well as outlying, i.e., very small or very large heart cycle lengths. The properties of ectopic beats are important not only for medical diagnostic purposes but the occurrence of ectopic beats is a general example of the kind of variability that occurs in a signal with outliers. To show that the method is general, we also present results for two examples of data from very different areas of science: daily temperatures at a large European city and recordings of traffics on a highway. Using the fixed mass method, to assess the dynamics leading to the outlying events we studied the occurrence of higher order terms of the Kramers-Moyal expansion in the recordings. We found that the higher order terms of the Kramers-Moyal expansion are negligible for heart rate variability. This finding opens the possibility of the application of the Langevin equation to the whole range of empirical signals containing rare or outlying events. Note, however, that the higher order terms are non-negligible for the other data studied here and for it the Langevin equation is not applicable as a model.

show abstract

New computational approaches to the analysis of interbeat intervals in human subjects

Cited by 21 publications

References 33 publications

Cardiorespiratory coupling in health and disease

Cardiorespiratory coupling in health and disease

On the nature of heart rate variability in a breathing normal subject: A stochastic process analysis

A fixed mass method for the Kramers-Moyal expansion—Application to time series with outliers

Contact Info

Product

Resources

About