Multiscale, multiorgan and multivariate complexity analyses of cardiovascular regulation

Abstract

Cardiovascular system complexity is confirmed by both its generally variegated structure of physiological modelling and the richness of information detectable from processing of the signals involved in it, with strong linear and nonlinear interactions with other biological systems. In particular, this behaviour may be accordingly described by means of what we call MMM paradigm (i.e. multiscale, multiorgan and multivariate). Such an approach to the cardiovascular system emphasizes where the genesis of its complexity is potentially allocated and how it is possible to detect information from it. No doubt that processing signals from multi-leads of the same system (multivariate), from the interaction of different physiological systems (multiorgan) and integrating all this information across multiple scales (from genes, to proteins, molecules, cells, up to the whole organ) could really provide us with a more complete look at the overall phenomenon of cardiovascular system complexity, with respect to the one which is obtainable from its single constituent parts. In this paper, some examples of approaches are discussed for investigating the cardiovascular system in different time and spatial scales, in studying a different organ involvement (such as sleep, depression and multiple organ dysfunction) and in using a multivariate approach via various linear and nonlinear methods for cardiovascular risk stratification and pathology assessment.

In the Spotlight: Biomedical Signal Processing

“…Such an approach makes the system genesis explicit, where complexity is potentially allocated and how it is possible to detect information from it. No doubt that processing signals from multileads of the same system (multivariate), from the interaction of different physiological systems (multiorgan) and integrating all this information across multiple scales (from genes, to proteins, molecules, cells, up to the whole organ) could really provide a more complete look at the overall phenomenon of physiological system complexity, in respect to the one which is obtainable from its single constituent parts [33], [34].…”

Electrocardiologic and related methods of non-invasive detection and risk stratification in myocardial ischemia: state of the art and perspectives

“…These novel approaches can also complement traditional time- and frequency-domain analyses of cardiovascular variabilities [103]. However, for complex analyses of cardiovascular oscillations, a multivariate approach (combining parameters from different domains) is essential [104]. …”

The Path to Personalized Cardiovascular Medicine

“…Resultant massive complex data are processed, analyzed and integrated using advanced bioinformatics and computational modeling to build mathematical models of physiological or pathophysiological processes. [458][459][460] Typical work-flow of a systems biology approach applied to CVD is schematically shown in Figure 24.10. 440 Recent network-based analysis of cardiovascular development has revealed complex functional interactions between transcription factors Gata4, Mef2a, Nkx2-5, and Srf.…”