Information transfer through the spontaneous baroreflex in healthy humans.

OBJECTIVES: This study assesses the information transfer through the spontaneous baroreflex (i.e. through the pathway linking systolic arterial pressure to heart period) during an experimental condition soliciting baroreflex (i.e. head-up tilt). METHODS: The information transfer was calculated as the conditional entropy of heart period given systolic arterial pressure using a mutual neighbor approach and uniform quantization. The information transfer was monitored as a function of the forecasting time k. RESULTS: We found that during head-up tilt the information transfer at k = 0 decreased but the rate of rise of information transfer as a function of k was faster. CONCLUSIONS: We suggest that the characterization of the information transfer from systolic arterial pressure to heart period might complement the traditional characterization of the spontaneous baroreflex based on transfer function analysis.

Heart Rate Variability and Respiratory Sinus Arrhythmia Assessment of Affective States by Bivariate Autoregressive Spectral Analysis.

The study of emotions elicited by human-computer interactions is a promising field that could lead to the identification of specific patterns of affective states. We present a heart rate variability (HRV) assessment of the autonomic nervous system (ANS) response and respiratory sinus arrhythmia during PC-mediated stimuli by means of standard and multivariate autoregressive spectral methods. 35 healthy volunteers were exposed to computer-mediated tasks during data collection. The stimuli were designed to elicit: relaxation (R), engagement (E) and stress (S); half of the subjects were exposed to E before S (RES) while the other to S before E (RSE). HRV measures clearly separate the ANS response among R, S and E. Less significant differences are found between E and S in RSE, suggesting that S stimuli may cause a lasting response affecting the E period. Results from the bivariate analysis indicate a disruption of the cardio-respiratory coupling during non-relax conditions.

Empirical mode decomposition to assess baroreflex gain from spontaneous variability during exercise in humans.

Estimation of the baroreflex gain has become an important tool in clinical practice in order to assess cardiac autonomic system control. Spectral analysis and sequence analysis techniques based on the spontaneous variability of systolic arterial pressure and heart period have been proposed to evaluate the baroreflex gain. These analyses can be significantly altered by the presence of nonstationarities. Recently, the empirical mode decomposition (EMD), a signal processing technique particularly suitable for nonstationary series, has been proposed as a new tool for data analysis. The aim of this study is to propose EMD-based approaches to the evaluation of the baroreflex gain to account for the possible presence of nonstationarities of systolic arterial pressure and heart period series.

Assessment of the cardiovascular regulation during robotic assisted locomotion in normal subjects: autoregressive spectral analysis vs empirical mode decomposition.

Robotic assisted locomotion systems are recently gaining appreciation as methods to rehabilitate individuals with lost sensory motor function. In the present study we compare autoregressive power spectral analysis and empirical mode decomposition (EMD) applied to the analysis of short-term heart period variability regarding their ability to typify autonomic response during a robotic assisted locomotion session consisting in the following phases: 1) sitting position; 2) standing position; 3) suspension during subject instrumentation; 4) robotic assisted treadmill locomotion with partial body weight support; 5) standing recovery after exercise. Results showed a significant tachycardia during the suspension phase, but no significant changes of spectral indexes. On the contrary, when spectral indexes were derived according to EMD, changes were evidenced during the suspension and walking phases. The EMD method is more powerful than autoregressive spectral analysis in detecting variations of parasympathetic and sympathetic modulations elicited by a robotic-assisted locomotion protocol.

Cardiac response to robotic assisted locomotion in normal subjects: a preliminary study.

Robotic assisted locomotion systems are recently gaining appreciation and diffusion as useful methods to rehabilitate individuals with lost sensorimotor function. Our aim was to evaluate potential changes in the autonomic nervous system activity (by ECG and spectral analysis), due to the experimental protocol, which include suspension of the subject to be instrumented on the system. A group of 10 normal subjects was studied during the rehabilitation protocol. Results showed a significant tachycardia and a reduced variance, during orthostatic stress induced by the suspension phase in comparison with sitting baseline condition but no significant increase of LF normalized power as it would be expected during a sympathetic activation.

Nearly automated analysis of coronary Doppler flow velocity from transthoracic ultrasound images: validation with manual tracings.

Coronary flow velocity reserve is obtained by manual tracings of transthoracic coronary Doppler flow velocity profiles as the ratio of stress versus baseline diastolic peak velocities. This approach introduces subjectivity in the measurements and limits the information which could be exploited from the Doppler velocity profile. Accordingly, our goals were to develop a technique for nearly automated detection of Doppler coronary flow velocity profile, and automatically compute both conventional and additional amplitude, derivative and temporal parameters, and validate it with manual tracings. A total of 100 patients (17 normals, 15 patients with severe coronary stenosis, 41 with connective tissue disease and 27 with diabetes mellitus) were studied. Linear correlation and Bland-Altman analyses showed that the proposed method was highly accurate and repeatable compared to the manual measurements. Comparison between groups evidenced significant differences in some of the automated parameters, thus representing potentially additional indices useful for the noninvasive diagnosis of microcirculatory or coronary artery disease.

Semi-automated analysis of coronary flow Doppler images: validation with manual tracings.

Coronary flow velocity reserve (CFVR) is conventionally obtained by manual tracings of Doppler profiles, as ratio of stress vs baseline diastolic peak velocity. When <1.9, this parameter evidences reduced coronary flow and possible microcirculatory disease. Our goals were: 1) to develop a novel technique for semi-automated detection of Doppler flow velocity profile, allowing the automated computation of CFVR and other parameters; 2) to validate this technique in comparison with conventional measurements obtained by manual tracing; 3) to test for differences between normal (N) subjects and patients with rheumatoid arthritis (RA). Linear correlation and Bland-Altman analyses showed that the proposed method was highly accurate and repeatable compared to the manual measurements. Comparison between N and RA groups evidenced significant differences in some of the automated parameters.

Quantification of coronary flow velocity reserve by means of semiautomated analysis of coronary flow Doppler images.

Coronary flow reserve (CFVR) is conventionally obtained by manual tracings of Doppler profiles, as ratio of control vs stress diastolic peak velocity. This parameter could help in discriminating between normal (N) and microcirculatory pathologic (P) subjects, even the clinical meaning of 1.93) and 15 P (CFVR<1.8) subjects, to assess whose of the new parameters could be able to discriminate between these groups. Results indicated that many of the new parameters were able to evidence significant differences between N and P, thus representing new clinical indices useful for the diagnosis.