Nonlinear additive autoregressive model-based analysis of short-term heart rate variability

Wessel, Niels; Malberg, Hagen; Bauernschmitt, Robert; Schirdewan, Alexander; Kurths, Jürgen

doi:10.1007/s11517-006-0038-0

Cited by 17 publications

(8 citation statements)

References 48 publications

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“…44 Our studies support the view that there is a strong interaction between the central nervous sleep regulation and the autonomous nervous system regulation. Until today it is not possible to distinguish those patients with sleep apnea which do develop arterial hypertension and congestive heart failure and those who do not.…”

Section: Discussionsupporting

confidence: 84%

Cardiovascular and respiratory dynamics during normal and pathological sleep

Penzel

Wessel

Riedl

et al. 2007

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Sleep is an active and regulated process with restorative functions for physical and mental conditions. Based on recordings of brain waves and the analysis of characteristic patterns and waveforms it is possible to distinguish wakefulness and five sleep stages. Sleep and the sleep stages modulate autonomous nervous system functions such as body temperature, respiration, blood pressure, and heart rate. These functions consist of a sympathetic tone usually related to activation and to parasympathetic (or vagal) tone usually related to inhibition. Methods of statistical physics are used to analyze heart rate and respiration to detect changes of the autonomous nervous system during sleep. Detrended fluctuation analysis and synchronization analysis and their applications to heart rate and respiration during sleep in healthy subjects and patients with sleep disorders are presented. The observed changes can be used to distinguish sleep stages in healthy subjects as well as to differentiate normal and disturbed sleep on the basis of heart rate and respiration recordings without direct recording of brain waves. Of special interest are the cardiovascular consequences of disturbed sleep because they present a risk factor for cardiovascular disorders such as arterial hypertension, cardiac ischemia, sudden cardiac death, and stroke. New derived variables can help to find indicators for these health risks.

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Section: Discussionsupporting

confidence: 84%

Cardiovascular and respiratory dynamics during normal and pathological sleep

Penzel

Wessel

Riedl

et al. 2007

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…These approaches on the one hand aim at modeling one or more regulation processes, and on the other hand provide model parameters which should be used to answer clinically relevant questions. In this tutorial we have introduced an approach which is very promising in data-driven modeling and modelbased data analysis [Wessel et al, 2000b;Wessel et al, 2004a;Wessel et al, 2004b;Wessel et al, 2006]. We have found that the maximal correlation method is a powerful tool for medical data analyses and for solving mechanical engineering problems.…”

Section: Discussionmentioning

confidence: 99%

“…This makes the result less sensitive to the data distribution. The maximal correlation and optimal transformation approach have recently been applied to nonlinear dynamical systems especially to model river flow data [Chen & Tsay, 1993], to identify delay in lasers [Voss & Kurths, 1997] and partial differential equations in fluid dynamics , to predict thermal displacements in modular tool systems [Wessel et al, 2004a;Wessel et al, 2004b] and to medical data analyses [Wessel et al, 2000b;Wessel et al, 2006]. A more general review on nonlinear system identification is given in .…”

Section: Nonlinear Model Based Data Analysismentioning

confidence: 99%

Nonlinear Methods of Cardiovascular Physics and Their Clinical Applicability

Wessel

Malberg²,

Bauernschmitt

et al. 2007

Int. J. Bifurcation Chaos

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102

107

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In this tutorial we present recently developed nonlinear methods of cardiovascular physics and show their potentials to clinically relevant problems in cardiology. The first part describes methods of cardiovascular physics, especially data analysis and modeling of noninvasively measured biosignals, with the aim to improve clinical diagnostics and to improve the understanding of cardiovascular regulation. Applications of nonlinear data analysis and modeling tools are various and outlined in the second part of this tutorial: monitoring-, diagnosis-, course and mortality prognoses as well as early detection of heart diseases. We show, that these data analyses and modeling methods lead to significant improvements in different medical fields.Keywords: Cardiovascular physics; nonlinear data analysis and modeling; heart rate and blood pressure variability; baroreflex sensitivity. List of Abbreviations

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“…Atrial fibrillation has been characterized with CVRR and CVdRR [60]. In patients with recent myocardial infarction VarIndex was found to be useful [61], PolVar20 with different thresholds was used in risk stratification for acute myocardial infarction [62], for identifying congestive heart failure [63], and for forecasting ventricular tachycardia [51].…”

Section: Other Indices and Their Short Term Usementioning

confidence: 99%

Heart rate variability indices for very short-term (30 beat) analysis. Part 1: survey and toolbox

Smith

Owen

Reynolds

2013

J Clin Monit Comput

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Heart rate variability (HRV) analysis over very short (<60 s) periods may be useful for monitoring dynamic changes in autonomic nervous system activity where steady-state conditions are not maintained (e.g. during drug administration, or the start or end of exercise). From the 1980s there has been a wealth of HRV indices produced in the quest for better measures of the change in parasympathetic and sympathetic activity. Many of the indices have been sparingly used and have not been investigated for application to short-term use. This study surveyed published methods of HRV analysis searching for indices that could be applied to very short time HRV analysis. The survey included measures of time domain, frequency domain, respiratory sinus arrhythmia, Poincaré plot, and heart rate characteristics. Indices were tested with short segments of archived data to remove those that produced invalid results, or were mathematically equivalent to, but less well known than other indices. The survey identified a comprehensive list of 115 indices that were subsequently coded and screened. Of these, 70 were unique and produced a finite number with 60 s data, so are included in the Toolbox. These indices require validation against physiological data before they can be applied to short-term HRV analysis of cardiac autonomic nervous system activity.

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Nonlinear additive autoregressive model-based analysis of short-term heart rate variability

Cited by 17 publications

References 48 publications

Cardiovascular and respiratory dynamics during normal and pathological sleep

Cardiovascular and respiratory dynamics during normal and pathological sleep

Nonlinear Methods of Cardiovascular Physics and Their Clinical Applicability

Heart rate variability indices for very short-term (30 beat) analysis. Part 1: survey and toolbox

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