In physiological conditions, heart period (HP) affects systolic arterial pressure (SAP) through diastolic runoff and Starling's law, but, the reverse relation also holds as a result of the continuous action of baroreflex control. The prevailing mechanism sets the dominant temporal direction in the HP-SAP interactions (i.e., causality). We exploited cross-conditional entropy to assess HP-SAP causality. A traditional approach based on phases was applied for comparison. The ability of the approach to detect the lack of causal link from SAP to HP was assessed on 8 short-term (STHT) and 11 long-term heart transplant (LTHT) recipients (i.e., less than and more than 2 yr after transplantation, respectively). In addition, spontaneous HP and SAP variabilities were extracted from 17 healthy humans (ages 21-36 yr, median age 29 yr; 9 females) at rest and during graded head-up tilt. The tilt table inclinations ranged from 15 to 75° and were changed in steps of 15°. All subjects underwent recordings at every step in random order. The approach detected the lack of causal relation from SAP to HP in STHT recipients and the gradual restoration of the causal link from SAP to HP with time after transplantation in the LTHT recipients. The head-up tilt protocol induced the progressive shift from the prevalent causal direction from HP to SAP to the reverse causality (i.e., from SAP to HP) with tilt table inclination in healthy subjects. Transformation of phases into time shifts and comparison with baroreflex latency supported this conclusion. The proposed approach is highly efficient because it does not require the knowledge of baroreflex latency. The dependence of causality on tilt table inclination suggests that "spontaneous" baroreflex sensitivity estimated using noncausal methods (e.g., spectral and cross-spectral approaches) is more reliable at the highest tilt table inclinations.

The autonomic regulation is non-invasively estimated from heart rate variability (HRV). Many methods utilized to assess autonomic regulation require stationarity of HRV recordings. However, non-stationarities are frequently present even during well-controlled experiments, thus potentially biasing HRV indices. The aim of our study is to quantify the potential bias of spectral, symbolic and entropy HRV indices due to non-stationarities. We analyzed HRV series recorded in healthy subjects during uncontrolled daily life activities typical of 24 h Holter recordings and during predetermined levels of robotic-assisted treadmill-based physical exercise. A stationarity test checking the stability of the mean and variance over short HRV series (about 300 cardiac beats) was utilized to distinguish stationary periods from non-stationary ones. Spectral, symbolic and entropy indices evaluated solely over stationary periods were contrasted with those derived from all the HRV segments. When indices were calculated solely over stationary series, we found that (i) during both uncontrolled daily life activities and controlled physical exercise, the entropy-based complexity indices were significantly larger; (ii) during uncontrolled daily life activities, the spectral and symbolic indices linked to sympathetic modulation were significantly smaller and those associated with vagal modulation were significantly larger; (iii) while during uncontrolled daily life activities, the variance of spectral, symbolic and entropy rate indices was significantly larger, during controlled physical exercise, it was smaller. The study suggests that non-stationarities increase the likelihood to overestimate the contribution of sympathetic control and affect the power of statistical tests utilized to discriminate conditions and/or groups.

We present a preliminary quantitative study aimed at developing an optimal standard protocol for automatic classification of specific affective states as related to human-computer interactions. This goal is mainly achieved by comparing standard psychological test-reports to quantitative measures derived from simultaneous non-invasive acquisition of psychophysiological signals of interest, namely respiration, galvanic skin response, blood volume pulse, electrocardiogram and electroencephalogram. Forty-three healthy students were exposed to computer-mediated stimuli, while wearable non-invasive sensors were applied in order to collect the physiological data. The stimuli were designed to elicit three distinct affective states: relaxation, engagement and stress. In this work we report how our quantitative analysis has helped in redefining important aspects of the protocol, and we show preliminary findings related to the specific psychophysiological patterns correlating with the three target affective states. Results further suggest that some of the quantitative measures might be useful in characterizing specific affective states.