Information scaling properties of heart rate variability

1998 ◽  
Vol 274 (6) ◽  
pp. H1970-H1978 ◽  
Author(s):  
Daniel E. Roach ◽  
Robert S. Sheldon
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Detection Methods ◽  
Correlated Noise ◽  
Heart Period ◽  
Scaling Properties ◽  
Chaos Detection ◽  
Chaotic Signals ◽  
Global Organization ◽  
Low Dimensional

Many chaos detection methods have proven inherently ambiguous in that they yield similar results for chaotic signals and correlated noise. The purpose of this work was to determine whether human resting heart period sequences have global properties characteristic of chaotic systems. We investigated the inherent global organization of heart period sequences by quantifying how the information content of the embedded sequences varied as a function of scale. We compared the information scaling characteristics of 60-min heart period sequences obtained from 10 healthy resting volunteers with those obtained from numerous periodic and chaotic control sequences. The information scaling properties of the heart period sequences were significantly different from those obtained for the controls, particularly at the coarsest scales ( P = 0.0003 vs. low-dimensional periodic controls; P = 0.0005 vs. low-dimensional chaotic controls; P = 0.0003 vs. low-dimensional periodic and chaotic controls). We also showed that nondeterministic components, such as large tachycardic (or bradycardic) events or aperiodic fluctuations, can lead to scaling characteristics similar to those observed for the resting heart period sequences. This, in addition to previous evidence from spectral, nonlinear predictability and lexical studies, favors an events-based approach to understanding heart rate variability.

ON CHAOS DETECTION METHODS

1996 ◽  
Vol 06 (03) ◽  
pp. 529-543 ◽  
Author(s):  
KARIN VIBE ◽  
JEAN-MARC VESIN
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Scientific Community ◽  
Detection Methods ◽  
Nonlinear Prediction ◽  
Attractor Dimension ◽  
Chaos Detection ◽  
Different Types ◽  
Data Length

Reliable chaos detection in real-world time series is attracting increasing attention in the scientific community. This work shows that it is possible to use chaos analysis methods such as attractor dimension estimation, Lyapunov exponents estimation and nonlinear prediction, under the condition that the limitations and drawbacks of the algorithms used are kept in mind. Three existing algorithms for chaos characterization are analyzed in terms of classification performances and robustness with respect to noise and data length. It is shown that all three help detect chaos and even classify different types of signals, but that their results are not devoid of ambiguity. An illustrative example is given, in which the algorithms presented are applied to heart rate variability signals, and directions of research are proposed for the design of a straightforward and simple chaos detection methodology.

Incongruous Changes in Heart Period and Heart Rate Variability with Vagotonic Atropine: Implications for Rehabilitation Medicine

PM&R ◽  
2009 ◽  
Vol 1 (9) ◽  
pp. 820-826 ◽  
Author(s):  
Glen Picard ◽  
Can Ozan Tan ◽  
Ross Zafonte ◽  
J. Andrew Taylor
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Rehabilitation Medicine ◽  
Heart Period

Large-magnitude, transient, bradycardic events in rabbits

1999 ◽  
Vol 277 (1) ◽  
pp. R243-R249
Author(s):  
Daniel Roach ◽  
Ela Thakore ◽  
Robert S. Sheldon
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Adult Female ◽  
Heart Period ◽  
Shape Parameters ◽  
Large Magnitude ◽  
Central Statistical ◽  
Distinct Word ◽  
Transient Bradycardia

We propose that heart period sequences are organized similarly to sentences, with a lexicon of recurrent, similarly shaped words. These words should fulfill four criteria: universality, nonrandomness, central statistical tendencies, and specific associated physiology. Here we describe a large-magnitude, transient bradycardia (LMTB) and assess whether it constitutes a word. LMTBs were seen in 11 of 12 adult female rabbits. All shape parameters were different than those of the beat-randomized and phase-randomized surrogate sequences ( P < 0.05–0.001). LMTBs were 8.4 ± 2.9 beats and 2.64 ± 0.87 s long and were characterized by bradycardia of 77 ± 49 ms over 1.09 ± 0.49 s with a recovery to baseline over 1.56 ± 0.61 s. The LMTBs had a slower recovery than onset in 9 of 11 rabbits and were highly peaked in 10 of 11 rabbits ( P< 0.05). Scalar, magnitude, and shape parameters had values with central statistical tendencies. About 76% of LMTBs were accompanied by hypotension (mean −6.1 ± 3.9 mmHg) that lagged 2 beats behind the onset of the bradycardia and that correlated with the bradycardia (−10.5 ± 4.1 ms/mmHg). Thus transient bradycardic events are a distinct “word” in the lexicon of heart rate variability.

The Pattern of Sympathovagal Balance Explored in the Frequency Domain

Physiology ◽  
1999 ◽  
Vol 14 (3) ◽  
pp. 111-117 ◽  
Author(s):  
Alberto Malliani
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Frequency Domain ◽  
High Frequency ◽  
Low Frequency ◽  
Sympathovagal Balance ◽  
Heart Period ◽  
Reciprocal Regulation ◽  
Physiological Conditions

In most physiological conditions, sympathetic and vagal activities modulating heart period undergo a reciprocal regulation, leading to the concept of sympathovagal balance. This pattern can be indirectly quantified by computing the spectral powers of the oscillatory components corresponding to respiratory acts (high frequency) and to vasomotor waves (low frequency) present in heart rate variability.

scholarly journals A computational physiology approach to personalized treatment models: the beneficial effects of slow breathing on the human cardiovascular system

2014 ◽  
Vol 307 (7) ◽  
pp. H1073-H1091 ◽  
Author(s):  
Maria Fonoberova ◽  
Igor Mezić ◽  
Jennifer F. Buckman ◽  
Vladimir A. Fonoberov ◽  
Adriana Mezić ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Cardiovascular System ◽  
Empirical Support ◽  
System Level ◽  
Heart Period ◽  
Heart Rate Variability Biofeedback ◽  
Human Cardiovascular System ◽  
Slow Breathing

Heart rate variability biofeedback intervention involves slow breathing at a rate of ∼6 breaths/min (resonance breathing) to maximize respiratory and baroreflex effects on heart period oscillations. This intervention has wide-ranging clinical benefits and is gaining empirical support as an adjunct therapy for biobehavioral disorders, including asthma and depression. Yet, little is known about the system-level cardiovascular changes that occur during resonance breathing or the extent to which individuals differ in cardiovascular benefit. This study used a computational physiology approach to dynamically model the human cardiovascular system at rest and during resonance breathing. Noninvasive measurements of heart period, beat-to-beat systolic and diastolic blood pressure, and respiration period were obtained from 24 healthy young men and women. A model with respiration as input was parameterized to better understand how the cardiovascular processes that control variability in heart period and blood pressure change from rest to resonance breathing. The cost function used in model calibration corresponded to the difference between the experimental data and model outputs. A good match was observed between the data and model outputs (heart period, blood pressure, and corresponding power spectral densities). Significant improvements in several modeled cardiovascular functions (e.g., blood flow to internal organs, sensitivity of the sympathetic component of the baroreflex, ventricular elastance) were observed during resonance breathing. Individual differences in the magnitude and nature of these dynamic responses suggest that computational physiology may be clinically useful for tailoring heart rate variability biofeedback interventions for the needs of individual patients.

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