Autonomic differentiation map: A novel statistical tool for interpretation of Heart Rate Variability

In spite of the large body of evidence suggesting Heart Rate Variability (HRV) alone or combined with blood pressure variability (providing an estimate of baroreflex gain) as a useful technique to assess the autonomic regulation of the cardiovascular system, there is still an ongoing debate about methodology, interpretation, and clinical applications. In the present investigation, we hypothesize that non-parametric and multivariate exploratory statistical manipulation of HRV data could provide a novel informational tool useful to differentiate normal controls from clinical groups, such as athletes, or subjects affected by obesity, hypertension, or stress. With a data-driven protocol in 1,352 ambulant subjects, we compute HRV and baroreflex indices from short-term data series as proxies of autonomic (ANS) regulation. We apply a three-step statistical procedure, by first removing age and gender effects. Subsequently, by factor analysis, we extract four ANS latent domains that detain the large majority of information (86.94%), subdivided in oscillatory (40.84%), amplitude (18.04%), pressure (16.48%), and pulse domains (11.58%). Finally, we test the overall capacity to differentiate clinical groups vs. control. To give more practical value and improve readability, statistical results concerning individual discriminant ANS proxies and ANS differentiation profiles are displayed through peculiar graphical tools, i.e., significance diagram and ANS differentiation map, respectively. This approach, which simultaneously uses all available information about the system, shows what domains make up the difference in ANS discrimination. e.g., athletes differ from controls in all domains, but with a graded strength: maximal in the (normalized) oscillatory and in the pulse domains, slightly less in the pressure domain and minimal in the amplitude domain. The application of multiple (non-parametric and exploratory) statistical and graphical tools to ANS proxies defines differentiation profiles that could provide a better understanding of autonomic differences between clinical groups and controls. ANS differentiation map permits to rapidly and simply synthesize the possible difference between clinical groups and controls, evidencing the ANS latent domains that have at least a medium strength of discrimination, while the significance diagram permits to identify the single ANS proxies inside each ANS latent domain that resulted in significant comparisons according to statistical tests.