Self-entropy (SE) and transfer entropy (TE) are widely utilized in biomedical signal processing to assess the information stored into a system and transferred from a source to a destination respectively. The study proposes a more specific definition of the SE, namely the conditional SE (CSE), and a more flexible definition of the TE based on joint TE (JTE), namely the conditional JTE (CJTE), for the analysis of information dynamics in multivariate time series. In a protocol evoking a gradual sympathetic activation and vagal withdrawal proportional to the magnitude of the orthostatic stimulus, such as the graded head-up tilt, we extracted the beat-to-beat spontaneous variability of heart period (HP), systolic arterial pressure (SAP) and respiratory activity (R) in 19 healthy subjects and we computed SE of HP, CSE of HP given SAP and R, JTE from SAP and R to HP, CJTE from SAP and R to HP given SAP and CJTE from SAP and R to HP given R. CSE of HP given SAP and R was significantly smaller than SE of HP and increased progressively with the amplitude of the stimulus, thus suggesting that dynamics internal to HP and unrelated to SAP and R, possibly linked to sympathetic activation evoked by head-up tilt, might play a role during the orthostatic challenge. While JTE from SAP and R to HP was independent of tilt table angle, CJTE from SAP and R to HP given R and from SAP and R to HP given SAP showed opposite trends with tilt table inclination, thus suggesting that the importance of the cardiac baroreflex increases and the relevance of the cardiopulmonary pathway decreases during head-up tilt. The study demonstrates the high specificity of CSE and the high flexibility of CJTE over real data and proves that they are particularly helpful in disentangling physiological mechanisms and in assessing their different contributions to the overall cardiovascular regulation.

We propose a sympathetic baroreflex (sBR) sequence method for characterizing sBR from spontaneous beat-to-beat fluctuations of muscle sympathetic nerve activity (MSNA) and diastolic arterial pressure (DAP). The method exploits a previously defined MSNA variability quantifying the fluctuations of MSNA burst rate. The method is based on the detection of MSNA and DAP sequences characterized by the contemporaneous DAP increase and MSNA decrease or vice versa. The percentage of sBR sequences (SEQ%sBR) was taken as an indication of the degree of sBR solicitation and the average slope of the regression lines in the (DAP, MSNA) plane was taken as sBR sensitivity (sBRSSEQ) and expressed in bursts.s−1.mmHg−1. sBRSSEQ was compared to a more traditional estimate based on the baroreflex threshold analysis (sBRSBTA). An incremental head-up tilt protocol, carried out in 12 young healthy subjects (age: 20–36 yr, median = 22.5 yr, 9 females) sequentially tilted at 0, 20, 30, 40, 60° table inclinations, was utilized to set the sBR sequence method parameters. Traditional sequence analysis was exploited to estimate cardiac baroreflex (cBR) sensitivity (cBRSSEQ) and percentage of cBR sequences (SEQ%cBR). The head-up tilt induced the progressive increase of SEQ%sBR and SEQ%cBR and gradual decrease of both sBRSSEQ and cBRSSEQ, thus suggesting the gradual rise of the sBR and cBR solicitations and the progressive reduction of their effectiveness with the stimulus. sBRSSEQ was significantly associated with sBRSBTA. sBRSSEQ and cBRSSEQ were significantly correlated as well as SEQ%sBR and SEQ%cBR, even though the correlation was not strong, thus suggesting a certain degree of independence between the baroreflex arms. The proposed sBR sequence approach provides a dynamical characterization of the sBR alternative to more traditional static pharmacological and nonpharmacological methods and fully homogenous with the cBR sequence technique.

Muscle sympathetic nerve activity (MSNA) variability is traditionally computed through a low-pass filtering procedure that requires normalization. We proposed a new beat-to-beat MSNA variability computation that preserves dimensionality typical of an integrated neural discharge (i.e., bursts per unit of time). The calibrated MSNA (cMSNA) variability technique is contrasted with the traditional uncalibrated MSNA (ucMSNA) version. The powers of cMSNA and ucMSNA variabilities in the low-frequency (LF, from 0.04 to 0.15 Hz) band were computed with those of the heart period (HP) of systolic and diastolic arterial pressure (SAP and DAP, respectively) in seven healthy subjects (age, 20-28 years; median, 22 years; 5 women) during a graded head-up tilt. Subjects were sequentially tilted at 0°, 20°, 30°, 40°, and 60° table inclinations. The LF powers of ucMSNA and HP variabilities were expressed in normalized units (LFnu), whereas all remaining spectral markers were expressed in absolute units. We found that 1) the LF power of cMSNA variability was positively correlated with tilt angle, whereas the LFnu power of the ucMSNA series was uncorrelated; 2) the LF power of cMSNA variability was correlated with LF powers of SAP and DAP, LFnu power of HP and noradrenaline concentration, whereas the relationship of the LFnu power of ucMSNA variability to LF powers of SAP and DAP was weaker and that to LFnu power of HP was absent; and 3) the stronger relationship of cMSNA variability to SAP and DAP spectral markers compared with the ucMSNA series was confirmed individually. The cMSNA variability appears to be more suitable in describing sympathetic control in humans than traditional ucMSNA variability.

We conclude that SYNC individuals featured an impaired cerebral autoregulation visible during TILT and were unable to activate cardiac baroreflex to cope with the postural challenge. Traditional frequency domain markers based on transfer function modulus, phase and coherence functions were less powerful or less specific in typifying the CBV and CV controls of SYNC individuals. Conditional transfer entropy approach can identify the impairment of CBV and CV controls and provide specific clues to identify subjects prone to develop postural syncope.

This study shows that different spontaneous BRS indexes have different predictive value in patients with heart failure. It also shows that the prognostic information of BRS estimates is linked to SAP and RR oscillations in the low-frequency band.