Assessing Complexity in Physiological Systems Through Biomedical Signals Analysis, 3rd Edition
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".
Deadline for manuscript submissions: 31 January 2027 | Viewed by 222
Special Issue Editors
Interests: hypertension; blood pressure; heart failure; cardiac function; atrial fibrillation; echocardiography; cardio-vascular medicine; myocardial infarction; atherosclerosis ; clinical cardiology
Special Issues, Collections and Topics in MDPI journals
Interests: time series analysis; information dynamics; network physiology; cardiovascular neuroscience; brain connectivity
Special Issues, Collections and Topics in MDPI journals
Interests: hypertension; echocardiography; signal analysis; cardiovascular medicine; biomedical signal processing; electrocardiography; advanced statistical analysis; heart rate variability
Special Issues, Collections and Topics in MDPI journals
Interests: time series analysis; complex systems; network physiology; EEG
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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 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 two Special Issues on these themes. Due to the interest they raised, the past Special Issues were also published as printed books in 2021 and 2026.
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; robust estimates on short series or from multivariate recordings; multivariate and/or multiscale estimates of predictability, entropy, and multifractality; parametric representation of the stochastic processes describing the data; or the automatic setting of the analysis parameters.
Therefore, we are launching the third volume of this Special Issue, aimed at collecting methodological contributions that may improve the use of complexity-based methods in signal analysis for physiological or clinical settings, as well as novel applications of biomedical signals that illustrate the value of complexity analysis. Manuscripts reviewing the state of the art of these topics are also welcome. In addition to regular contributions, this Special Issue will also include a selection of the best works presented at the 14th meeting of the International Conference of the European Study Group on Cardiovascular Oscillations (ESGCO), scheduled between 22 and 26 October 2026, in Erice (Italy).
Dr. Paolo Castiglioni
Prof. Dr. Luca Faes
Dr. Andrea Faini
Dr. Yuri Antonacci
Guest Editors
Manuscript Submission Information
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Keywords
- entropy
- fractals
- multiscale analysis
- linear and nonlinear prediction
- self-organization
- chaos
- information dynamics
- symbolic dynamics
- nonlinearity
- heart rate variability
- EEG
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