Wavelet Phase Coherence Between the Microvascular Pulse Contour and the Respiratory Activity. In: Lhotska L, Sukupova L, Lacković I, Ibbott GS, editors. World Congress on Medical Physics and Biomedical Engineering 2018. Singapore: Springer; 2019. pp. 311-4. [DOI: 10.1007/978-981-10-9038-7_58]

Cardiovascular Dynamics in COVID-19: A Heart Rate Variability Investigation

COVID-19 is known to be a cause of microvascular disease due, for example, to the cytokine storm inflammatory response and the result of blood coagulation. This study reports an investigation on Heart Rate Variability (HRV) extracted from photoplethysmography (PPG) signals measured from healthy subjects and COVID-19 affected patients. We aimed to determine a statistical difference between HRV parameters among subjects' groups. Specifically, statistical analysis through Mann-Whitney U Test (MWUT) was applied to compare 42 dif-ferent parameters extracted from PPG signals of 143 subjects: 50 healthy subjects (i.e. group 0) and 93 affected from COVID-19 patients stratified through increasing COVID severity index (i.e. groups 1 and 2). Results showed significant statistical differences between groups in several HRV parameters. In particular, Multiscale Entropy (MSE) analysis provided the master key in patient stratification assessment. In fact, MSE11, MSE12, MSE15, MSE16, MSE17, MSE18, MSE19 and MSE20 keep statistical significant difference during all the comparisons between healthy subjects and patients from all the pathological groups. Our preliminary results suggest that it could be possible to distinguish between healthy and COVID-19 affected subjects based on cardiovascular dynamics. This study opens to future evaluations in using machine learning models for automatic decision-makers to distinguish between healthy and COVID-19 subjects, as well as within COVID-19 severity levels. Clinical Relevance - This establishes the possibility to distin-guish healthy subjects from COVID-19 affected patients basing on HRV parameters monitored non invasively by PPG.

Linear and Nonlinear Directed Connectivity Analysis of the Cardio-Respiratory System in Type 1 Diabetes

In this study, we explored the possibility of developing non-invasive biomarkers for patients with type 1 diabetes (T1D) by quantifying the directional couplings between the cardiac, vascular, and respiratory systems, treating them as interconnected nodes in a network configuration. Towards this goal, we employed a linear directional connectivity measure, the directed transfer function (DTF), estimated by a linear multivariate autoregressive modelling of ECG, respiratory and skin perfusion signals, and a nonlinear method, the dynamical Bayesian inference (DBI) analysis of bivariate phase interactions. The physiological data were recorded concurrently for a relatively short time period (5 min) from 10 healthy control subjects and 10 T1D patients. We found that, in both control and T1D subjects, breathing had greater influence on the heart and perfusion with respect to the opposite coupling direction and that, by both employed methods of analysis, the causal influence of breathing on the heart was significantly decreased (p < 0.05) in T1D patients compared to the control group. These preliminary results, although obtained from a limited number of subjects, provide a strong indication for the usefulness of a network-based multi-modal analysis for the development of biomarkers of T1D-related complications from short-duration data, as well as their potential in the exploration of the pathophysiological mechanisms that underlie this devastating and very widespread disease.

Effect of orthostasis on the regulation of skin blood flow in upper and lower extremities in human

OBJECTIVE

The research is aimed to investigate interactions between cardiovascular signals and to assess contributions of central and local mechanisms to skin blood flow regulation in upper and lower extremities at rest and under orthostasis.

METHODS

Heart rate variability, respiration, forearm, and foot skin blood flow were assessed at rest and during postural test in 25 healthy volunteers. Spectral analysis was performed. Phase synchronization degree of analyzed signals was determined by group phase wavelet coherence function.

RESULTS

Skin blood flow was lower on foot at rest and during postural test than on forearm. High-frequency component of heart rate variability was higher at ~0.3 Hz during postural test versus rest. Blood flow oscillation amplitudes on the foot were lower in frequency range including respiratory interval at rest than on forearm. Postural exposure increased amplitude of foot blood flow oscillations in respiratory interval and decreased amplitudes in cardiac interval versus rest. Orthostasis increased group wavelet phase coherence between foot blood flow and heart rate variability or respiration, as well as between forearm and foot blood flow at 0.3 Hz corresponding to respiration.

CONCLUSIONS

The contribution of central mechanisms associated with respiration to blood flow regulation increased in lower extremities during orthostasis.