Abstract
The physiological mechanisms related to cardio-vascular (CV), cardio-pulmonary (CP), and vasculo-pulmonary (VP) regulation may be probed through multivariate time series analysis tools. This study applied an information domain approach for the evaluation of non-linear causality to the beat-to-beat variability series of heart period (t), systolic arterial pressure (s), and respiration (r) measured during tilt testing and paced breathing (PB) protocols. The approach quantifies the causal coupling from the series i to the series j (Cij) as the amount of information flowing from i to j. A measure of directionality is also obtained as the difference between two reciprocal causal couplings (Di,j = Cij − Cji). Significant causal coupling and directionality were detected respectively when the median of Cij over subjects was positive (Cij > 0), and when Di,j was statistically different from zero (Di,j > 0 or Di,j < 0). The method was applied on t, s, and r series measured in 15 healthy subjects (22–32 years, 8 males) in the supine (su) and upright (up) positions, and in further 15 subjects (21–29 years, 7 males) during spontaneous (sp) and paced (pa) breathing. In the control condition (su, sp), a significant causal coupling was observed for Crs, Crt, Cst, and Cts, and significant directionality was present only from r to t (Dr,t > 0). During head-up tilt (up, sp), Crs was preserved, Crt decreased to zero median, and Cst and Cts increased significantly; directionality vanished between r and t (Dr,t = 0) and raised from s to t (Ds,t > 0). During PB (su, pa), Crs increased significantly, Crt and Cts were preserved, and Cst decreased to zero median; directionality was preserved from r to t (Dr,t > 0), and raised from r to s (Dr,s > 0). These results suggest that the approach may reflect modifications of CV, CP, and VP mechanisms consequent to altered physiological conditions, such as the baroreflex engagement and the dampening of respiratory sinus arrhythmia induced by tilt, or the respiratory driving on arterial pressure induced by PB. Thus, it could be suggested as a tool for the non-invasive monitoring of CV and cardiorespiratory control systems in normal and impaired conditions.
Highlights
During paced breathing (PB), Crs increased significantly, Crt and Cts were preserved, and Cst decreased to zero median; directionality was preserved from r to t (Dr,t > 0), and raised from r to s (Dr,s > 0). These results suggest that the approach may reflect modifications of CV, CP, and VP mechanisms consequent to altered physiological conditions, such as the baroreflex engagement and the dampening of respiratory sinus arrhythmia induced by tilt, or the respiratory driving on arterial pressure induced by PB
The heart period (HP), measured from the ECG as the duration of the temporal interval occurring between two consecutive R waves in the ECG (RR interval), and the systolic arterial pressure (SAP), measured from the arterial pressure signal as the maximum pressure value following each R wave (Figure 1A), exhibit spontaneous beat-to-beat fluctuations around their mean value, which are clearly visible in time series recordings of few hundred beats (Figures 1B–D)
TIME DOMAIN ANALYSIS A representative example of HP, SAP, and respiratory flow (RF) variability series recorded in the different experimental conditions during the TT and PB protocols is reported in Figure 1 (Figure 1B: su, sp; Figure 1C: up, sp; Figure 1D: su, pa)
Summary
The heart period (HP), measured from the ECG as the duration of the temporal interval occurring between two consecutive R waves in the ECG (RR interval), and the systolic arterial pressure (SAP), measured from the arterial pressure signal as the maximum pressure value following each R wave (Figure 1A), exhibit spontaneous beat-to-beat fluctuations around their mean value, which are clearly visible in time series recordings of few hundred beats (Figures 1B–D) A common approach to quantify the causal coupling between two variability series is the causal coherence (Porta et al, 2002), which quantifies causality from the frequency domain representation of a linear parametric bivariate model fitted to the two considered series This method has been exploited to study causality between HP and SAP in physiological and impaired conditions (Nollo et al, 2005; Faes et al, 2006). Information theoretic tools based on conditional entropy (CE) estimation have been framed in the so-called information domain (Porta et al, 2000b), and have been exploited to detect causal information transfers in the CV loop in a variety of physiological conditions (Nollo et al, 2002; Faes et al, 2011b; Porta et al, 2011b)
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