Special Issue "Assessing Complexity in Physiological Systems through Biomedical Signals Analysis"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: 31 January 2020.

Special Issue Editors

Prof. Dr. Paolo Castiglioni
E-Mail Website
Guest Editor
IRCCS Fondazione Don Carlo Gnocchi, 20148 Milan, Italy
Interests: complexity in biosignals; physiological time series; fractals in medicine; cardiovascular modeling; physiology in extreme environments; rehabilitation medicine
Prof. Dr. Luca Faes
E-Mail Website
Guest Editor
Department of Engineering, University of Palermo, 90128 Palermo, Italy
Interests: time series analysis; cardiovascular neuroscience; network physiology
Special Issues and Collections in MDPI journals
Prof. Dr. Gaetano Valenza
E-Mail Website
Guest Editor
Department of Information Engineering and Research Center“E. Piaggio”, University of Pisa, 56122 Pisa, Italy
Interests: biomedical signal and image processing; cardiovascular and neural modeling; wearable systems for physiological monitoring

Special Issue Information

Dear Colleague,

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
Guest Editors

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.

Keywords

  • entropy;
  • fractals;
  • multiscale analysis;
  • linear and nonlinear prediction;
  • self-organization;
  • chaos;
  • information dynamics;
  • symbolic dynamics;
  • nonlinearity;
  • heart rate variability;
  • EEG

Published Papers (2 papers)

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Research

Open AccessArticle
Multiscale Entropy of Cardiac and Postural Control Reflects a Flexible Adaptation to a Cognitive Task
Entropy 2019, 21(10), 1024; https://doi.org/10.3390/e21101024 - 21 Oct 2019
Abstract
In humans, physiological systems involved in maintaining stable conditions for health and well-being are complex, encompassing multiple interactions within and between system components. This complexity is mirrored in the temporal structure of the variability of output signals. Entropy has been recognized as a [...] Read more.
In humans, physiological systems involved in maintaining stable conditions for health and well-being are complex, encompassing multiple interactions within and between system components. This complexity is mirrored in the temporal structure of the variability of output signals. Entropy has been recognized as a good marker of systems complexity, notably when calculated from heart rate and postural dynamics. A degraded entropy is generally associated with frailty, aging, impairments or diseases. In contrast, high entropy has been associated with the elevated capacity to adjust to an ever-changing environment, but the link is unknown between entropy and the capacity to cope with cognitive tasks in a healthy young to middle-aged population. Here, we addressed classic markers (time and frequency domains) and refined composite multiscale entropy (MSE) markers (after pre-processing) of heart rate and postural sway time series in 34 participants during quiet versus cognitive task conditions. Recordings lasted 10 min for heart rate and 51.2 s for upright standing, providing time series lengths of 500–600 and 2048 samples, respectively. The main finding was that entropy increased during cognitive tasks. This highlights the possible links between our entropy measures and the systems complexity that probably facilitates a control remodeling and a flexible adaptability in our healthy participants. We conclude that entropy is a reliable marker of neurophysiological complexity and adaptability in autonomic and somatic systems. Full article
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Open AccessArticle
Application of a Speedy Modified Entropy Method in Assessing the Complexity of Baroreflex Sensitivity for Age-Controlled Healthy and Diabetic Subjects
Entropy 2019, 21(9), 894; https://doi.org/10.3390/e21090894 - 14 Sep 2019
Abstract
The percussion entropy index (PEIorginal) was recently introduced to assess the complexity of baroreflex sensitivity. This study aimed to investigate the ability of a speedy modified PEI (i.e., PEINEW) application to distinguish among age-controlled subjects with or without diabetes. [...] Read more.
The percussion entropy index (PEIorginal) was recently introduced to assess the complexity of baroreflex sensitivity. This study aimed to investigate the ability of a speedy modified PEI (i.e., PEINEW) application to distinguish among age-controlled subjects with or without diabetes. This was carried out using simultaneous photo-plethysmo-graphy (PPG) pulse amplitude series and the R wave-to-R wave interval (RRI) series acquired from healthy subjects (Group 1, number = 42), subjects diagnosed as having diabetes mellitus type 2 with satisfactory blood sugar control (Group 2, number = 38), and type 2 diabetic patients with poor blood sugar control (Group 3, number = 35). Results from PEIorginal and multiscale cross-approximate entropy (MCAE) were also addressed with the same datasets for comparison. The results show that optimal prolongation between the amplitude series and RRI series could be delayed by one to three heartbeat cycles for Group 2, and one to four heartbeat cycles for Group 3 patients. Group 1 subjects only had prolongation for one heartbeat cycle. This study not only demonstrates the sensitivity of PEINEW and PEIorginal in differentiating between Groups 2 and 3 compared with MCAE, highlighting the feasibility of using percussion entropy applications in autonomic nervous function assessments, it also shows that PEINEW can considerably reduce the computational time required for such processes. Full article
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