Journal Menu► ▼ Journal Menu
Journal Browser► ▼ Journal Browser
Special Issue "Assessing Complexity in Physiological Systems through Biomedical Signals Analysis"
Deadline for manuscript submissions: 31 January 2020.
Department of Engineering, University of Palermo, 90128 Palermo, Italy
Interests: time series analysis; cardiovascular neuroscience; network physiology
Special Issues and Collections in MDPI journals
Special Issue in Entropy: Information Dynamics in Brain and Physiological Networks
In the last few decades, the idea that most physiological systems are complex has become increasingly popular. Complexity is considered a ubiquitous phenomenon in physiology and medicine that allows living systems to adapt to external perturbations preserving homeostasis and that originates from specific features of the system, like fractal structures, self-organization, nonlinearity, 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, to monitor the health conditions over time or to 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, like continuous electroencephalogram and electromyogram recordings, beat-by-beat values of cardiovascular variables, or breath-by-breath values of respiratory variables.
However, important methodological issues on the complexity analysis of biomedical signals are still open. These include, for instance, the development of methods that distinguish randomness from complexity; that provide robust estimates on short series or from multivariate recordings; that allow multivariate and/or multiscale estimates of predictability, entropy, and multifractality; that represent parametrically the stochastic processes describing the data; or that set the analysis parameters automatically.
Therefore, this Special Issue is 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.
Prof. Dr. Paolo Castiglioni
Prof. Dr. Luca Faes
Prof. Dr. Gaetano Valenza
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- multiscale analysis;
- linear and nonlinear prediction;
- information dynamics;
- symbolic dynamics;
- heart rate variability;