Heart rate variability as a prognostic tool in cardiology. A contribution to the problem from a theoretical point of view.

Moser, M.; Lehofer, Michael; Sedminek, A.; Lux, Marco; Zapotoczky, H. G.; Kenner, Th.; Noordergraaf, Abraham

doi:10.1161/01.cir.90.2.1078

Cited by 100 publications

(62 citation statements)

References 15 publications

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“…It is well known [23][24][25][45][46][47][48]51,52 that the phase of respiration influences the peripheral autonomic nervous system's outflow to the heart. However, till now it has never been shown when exactly this happens inside the cardiac cycle.…”

Section: Discussionmentioning

confidence: 99%

In vivo cardiac phase response curve elucidates human respiratory heart rate variability

Frühwirth²,

et al. 2013

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Recovering interaction of endogenous rhythms from observations is challenging, especially if a mathematical model explaining the behaviour of the system is unknown. The decisive information for successful reconstruction of the dynamics is the sensitivity of an oscillator to external influences, which is quantified by its phase response curve. Here we present a technique that allows the extraction of the phase response curve from a non-invasive observation of a system consisting of two interacting oscillators-in this case heartbeat and respiration-in its natural environment and under free-running conditions. We use this method to obtain the phase-coupling functions describing cardiorespiratory interactions and the phase response curve of 17 healthy humans. We show for the first time the phase at which the cardiac beat is susceptible to respiratory drive and extract the respiratory-related component of heart rate variability. This non-invasive method for the determination of phase response curves of coupled oscillators may find application in many scientific disciplines.

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Section: Discussionmentioning

confidence: 99%

In vivo cardiac phase response curve elucidates human respiratory heart rate variability

Frühwirth²,

et al. 2013

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“…18 Although the physiological significance of various aspects of cardiorespiratory interaction are not completely understood, it has been suggested that RSA enhances pulmonary gas exchange and thereby improves the energy efficiency of pulmonary circulation. 21 The quantification of cardiorespiratory interaction appears to have clinical merit for risk stratification of cardiac mortality, 24,32 and diagnosing obstructive sleep apnea. 3,15,25 In this study, we were interested in a new and relatively simple approach that could also provide us with some additional information about cardiorespiratory interactions.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Cardiorespiratory Interactions Based on Joint Symbolic Dynamics

et al. 2011

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Abstract-Cardiac and respiratory rhythms are highly nonlinear and nonstationary. As a result traditional time-domain techniques are often inadequate to characterize their complex dynamics. In this article, we introduce a novel technique to investigate the interactions between R-R intervals and respiratory phases based on their joint symbolic dynamics. To evaluate the technique, electrocardiograms (ECG) and respiratory signals were recorded in 13 healthy subjects in different body postures during spontaneous and controlled breathing. Herein, the R-R time series were extracted from ECG and respiratory phases were obtained from abdomen impedance belts using the Hilbert transform. Both time series were transformed into ternary symbol vectors based on the changes between two successive R-R intervals or respiratory phases. Subsequently, words of different symbol lengths were formed and the correspondence between the two series of words was determined to quantify the interaction between cardiac and respiratory cycles. To validate our results, respiratory sinus arrhythmia (RSA) was further studied using the phase-averaged characterization of the RSA pattern. The percentage of similarity of the sequence of symbols, between the respective words of the two series determined by joint symbolic dynamics, was significantly reduced in the upright position compared to the supine position (26.4 ± 4.7 vs. 20.5 ± 5.4%, p < 0.01). Similarly, RSA was also reduced during upright posture, but the difference was less significant (0.11 ± 0.02 vs. 0.08 ± 0.01 s, p < 0.05). In conclusion, joint symbolic dynamics provides a new efficient technique for the analysis of cardiorespiratory interaction that is highly sensitive to the effects of orthostatic challenge.

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“…[12] The SDNN reflects all the cyclic components responsible for variability in the period of recording and is regarded to reflect both the sympathetic and the parasympathetic influence on HRV. [13] Therefore we speculate that negative correlation observed between SDNN and DBP could be due to decreased parasympathetic activity owing to sympathetic over activity related to diabetes induced alteration in autonomic cardiovascular control.…”

Section: Discussionmentioning

confidence: 88%

Correlates of SDNN heart rate variability in healthy subjects and subjects with type 2 diabetes mellitus

Nk¹,

Adhikari²,

Poornima³

et al. 2015

Int J of Biomed & Adv Res

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Introduction: Standard deviation of R-R intervals (SDNN) predicts mortality in variety of clinical conditions. But little is known about correlates of SDNN. Objective: To investigate correlates of SDNN in healthy subjects and with type 2 diabetes. Materials and Methods: 115 male subjects with type 2 diabetes and 87 controls were studied. In them heart rate (HR), blood pressure (BP), body mass index (BMI), Q -Tc and SDNN were measured. Pearson correlation coefficient and unpaired t test were used for data analysis. P < 0.05 was considered significant. Results: In controls, SDNN was negatively correlating with age and HR (p < 0.05, <0.001 respectively). In subjects with type 2 diabetes, SDNN was negatively correlating with HR, diastolic blood pressure, Q-Tc and duration of diabetes (p < 0.0001, < 0.01, = 0.014, < 0.05 respectively); and was positively correlating with BMI (p < 0.05). Conclusion: Higher HR, DBP, prolonged Q-T interval and duration of diabetes are associated with reduced SDNN in subjects with type 2 diabetes. In them BMI could be a confounding factor while assessing cardiac autonomic dysfunction employing SDNN. In healthy subjects high HR and age are associated with reduced SDNN.

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Heart rate variability as a prognostic tool in cardiology. A contribution to the problem from a theoretical point of view.

Cited by 100 publications

References 15 publications

In vivo cardiac phase response curve elucidates human respiratory heart rate variability

In vivo cardiac phase response curve elucidates human respiratory heart rate variability

Quantification of Cardiorespiratory Interactions Based on Joint Symbolic Dynamics

Correlates of SDNN heart rate variability in healthy subjects and subjects with type 2 diabetes mellitus

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