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Entropy
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Nonlinear Methods for Biomedical Engineering
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Nonlinear Methods for Biomedical Engineering

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

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 2073

Special Issue Editors

Dr. Roberto Di Marco

E-Mail Website
Guest Editor
Department of Computer Science, University of Verona, Strada le Grazie 15, 37134 Verona, Italy
Interests: movement analysis; rehabilitation engineering; sports biomechanics; motion capture; wearable sensors; biomechanics
Dr. Emanuela Formaggio

E-Mail Website
Guest Editor
Department of Neuroscience, Section of Rehabilitation, University of Padova, Via Giustiniani 3, 35128 Padova, Italy
Interests: brain connectivity; EEG; EEG-fMRI; MEG; frequency and time-frequency analysis
Dr. Maria Rubega

E-Mail Website
Guest Editor
Department of Neuroscience, Section of Rehabilitation, University of Padova, Via Giustiniani 3, 35128 Padova, Italy
Interests: entropy; complexity measures; time-series analysis; EEG

Special Issue Information

Dear Colleagues,

Signals collected in human beings during clinical tests and daily life activities, such as electroencephalography (EEG), electromyography (EMG), and accelerometry, can be successfully analyzed through linear (e.g., autoregressive models, power spectral density, coherence, connectivity) and nonlinear (e.g., fractals, entropy-derived indices) methods to assess their complexity and regularity to unveil peculiar patterns and human adaptability to the environment. Nonlinear measures allow the extraction of information complementary to the classical linear features to enable a comprehensive characterization of both physiological and pathological time-series.

We welcome the submission of original research findings (e.g., case reports, clinical trials, clinical studies, comparative studies, and methodological papers) or review articles on the topic. Manuscripts might focus on the analysis of human physiological/pathological mechanisms through both linear and nonlinear methods identifying potential biomarkers to be used in the design of intervention strategies.

We encourage authors to share their processed data and code to guarantee the reproducibility of their findings.

We expect that works collected for this Special Issue will provide new insights into the distinctive patterns of human signals helpful in the implementation of rehabilitative and preventive interventions.

Dr. Roberto Di Marco
Dr. Emanuela Formaggio
Dr. Maria Rubega
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 submissions that pass pre-check are 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 2600 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

  • electrophysiology
  • electroencephalography (EEG)
  • electromyography (EMG)
  • accelerometry
  • nonlinear analysis
  • wearable devices

Published Papers (2 papers)

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Research

11 pages, 599 KiB  
Article
Exploring Physiological Differences in Brain Areas Using Statistical Complexity Analysis of BOLD Signals
by Catalina Morales-Rojas, Ronney B. Panerai and José Luis Jara
Entropy 2024, 26(1), 81; https://doi.org/10.3390/e26010081 - 18 Jan 2024
Viewed by 796
Abstract
The brain is a fundamental organ for the human body to function properly, for which it needs to receive a continuous flow of blood, which explains the existence of control mechanisms that act to maintain this flow as constant as possible in a [...] Read more.
The brain is a fundamental organ for the human body to function properly, for which it needs to receive a continuous flow of blood, which explains the existence of control mechanisms that act to maintain this flow as constant as possible in a process known as cerebral autoregulation. One way to obtain information on how the levels of oxygen supplied to the brain vary is through of BOLD (Magnetic Resonance) images, which have the advantage of greater spatial resolution than other forms of measurement, such as transcranial Doppler. However, they do not provide good temporal resolution nor allow for continuous prolonged examination. Thus, it is of great importance to find a method to detect regional differences from short BOLD signals. One of the existing alternatives is complexity measures that can detect changes in the variability and temporal organisation of a signal that could reflect different physiological states. The so-called statistical complexity, created to overcome the shortcomings of entropy alone to explain the concept of complexity, has shown potential with haemodynamic signals. The aim of this study is to determine by using statistical complexity whether it is possible to find differences between physiologically distinct brain areas in healthy individuals. The data set includes BOLD images of 10 people obtained at the University Hospital of Leicester NHS Trust with a 1.5 Tesla magnetic resonance imaging scanner. The data were captured for 180 s at a frequency of 1 Hz. Using various combinations of statistical complexities, no differences were found between hemispheres. However, differences were detected between grey matter and white matter, indicating that these measurements are sensitive to differences in brain tissues. Full article
(This article belongs to the Special Issue Nonlinear Methods for Biomedical Engineering)
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10 pages, 649 KiB  
Article
Detection of Blood CO2 Influences on Cerebral Hemodynamics Using Transfer Entropy
by Juan Fernández-Muñoz, Victoria J. Haunton, Ronney B. Panerai and José Luis Jara
Entropy 2024, 26(1), 23; https://doi.org/10.3390/e26010023 - 25 Dec 2023
Viewed by 768
Abstract
Cerebral hemodynamics describes an important physiological system affected by components such as blood pressure, CO2 levels, and endothelial factors. Recently, novel techniques have emerged to analyse cerebral hemodynamics based on the calculation of entropies, which quantifies or describes changes in the complexity [...] Read more.
Cerebral hemodynamics describes an important physiological system affected by components such as blood pressure, CO2 levels, and endothelial factors. Recently, novel techniques have emerged to analyse cerebral hemodynamics based on the calculation of entropies, which quantifies or describes changes in the complexity of this system when it is affected by a pathological or physiological influence. One recently described measure is transfer entropy, which allows for the determination of causality between the various components of a system in terms of their flow of information, and has shown positive results in the multivariate analysis of physiological signals. This study aims to determine whether conditional transfer entropy reflects the causality in terms of entropy generated by hypocapnia on cerebral hemodynamics. To achieve this, non-invasive signals from 28 healthy individuals who undertook a hyperventilation maneuver were analyzed using conditional transfer entropy to assess the variation in the relevance of CO2 levels on cerebral blood velocity. By employing a specific method to discretize the signals, it was possible to differentiate the influence of CO2 levels during the hyperventilation phase (22.0% and 20.3% increase for the left and right hemispheres, respectively) compared to normal breathing, which remained higher during the recovery phase (15.3% and 15.2% increase, respectively). Full article
(This article belongs to the Special Issue Nonlinear Methods for Biomedical Engineering)
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