Assessing Complexity in Physiological Systems through Biomedical Signals Analysis II

Dear Colleagues,

In the last few decades, the idea that most physiological systems are complex has become increasingly popular. Complexity is a ubiquitous phenomenon in physiology and medicine that allows living systems to adapt to external perturbations, preserving homeostasis, and originates from specific features of the system such as fractal structures, self-organization, nonlinearity, the presence of many interdependent components interacting at different hierarchical levels and time scales, and interconnections with other systems through physiological networks.

Biomedical signals generated by such systems may carry information on the system complexity, information that may help to detect physiological states, monitor health conditions over time, or predict pathological events. For this reason, the more recent trends in biomedical signals analysis are aimed at designing tools for extracting information on the system complexity from the derived time series, such as continuous electroencephalogram and electromyogram recordings, beat-by-beat values of cardiovascular variables, or breath-by-breath values of respiratory variables. Entropy has recently dedicated a Special Issue on these themes. Due to the interest it raised, the Special Issue was also published as a printed book in 2021.

However, important methodological issues on the complexity analysis of biomedical signals are still open. These include the development of methods that distinguish randomness from complexity; provide robust estimates on short series or from multivariate recordings; allow multivariate and/or multiscale estimates of predictability, entropy, and multifractality; parametrically represent the stochastic processes describing the data; or set the analysis parameters automatically.

Therefore, we are launching a second volume of the Special Issue aimed at collecting methodological contributions that may improve the use of complexity-based methods of signal analysis in physiological or clinical settings, as well as novel applications on biomedical signals illustrating the value of complexity analysis. Manuscripts reviewing the state-of-the-art of these topics are also welcome.

Dr. Paolo Castiglioni
Dr. Luca Faes
Dr. Gaetano Valenza
Dr. Andrea Faini
Guest Editors

Manuscript Submission Information

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