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Network Physiology and Entropy
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Network Physiology and Entropy

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

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 12750

Special Issue Editor

Dr. Javier Escudero

E-Mail Website
Guest Editor
School of Engineering, Institute for Digital Communications, University of Edinburgh, Edinburgh EH9 3FB, UK
Interests: connectivity; biomedical signal processing; nonlinear analysis; brain activity; multiway array analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Network Physiology has recently emerged as an excellent paradigm to study living organisms (not only humans, but also animals) as a whole, allowing us to inspect and model the interdependencies between diverse regulatory mechanisms that are crucial for healthy living. These physiological interactions change dynamically. Notorious examples are transitions between wake and sleep, and disruptions in multiple pathologies. Furthermore, such interactions span multiple temporal and spatial scales and they often are governed by nonlinear processes. In this setting, methods rooted in complexity science such as nonlinear analysis and network theory are crucial to assess and understand these interdependencies between bodily organs and systems.

Hence, this Special Issue “Network Physiology and Entropy” welcomes theoretical or applied submissions reporting original research on the development and application of nonlinear analysis techniques (and those based on entropy and information theory in particular) and graph theory to quantify, characterize or model interactions between different types of physiological recordings. These include, but are not limited to, brain activity signals, cardiovascular time series, respiratory data, muscular activations, and any combination thereof. Given the relevance and timeliness of this research area, we are also happy to receive reviews and commentaries aligned with the vision of this Special Issue.

Dr. Javier Escudero
Guest Editor

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

  • Physiological recordings
  • Nonlinear metrics
  • Network analysis
  • Information transfer
  • Network dynamics
  • Synchronization
  • Multiscale modelling
  • Brain networks
  • Cardiovascular systems
  • Clinical data

Published Papers (4 papers)

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Research

17 pages, 2906 KiB  
Article
Assessment of Cardiorespiratory Interactions during Apneic Events in Sleep via Fuzzy Kernel Measures of Information Dynamics
by Ivan Lazic, Riccardo Pernice, Tatjana Loncar-Turukalo, Gorana Mijatovic and Luca Faes
Entropy 2021, 23(6), 698; https://doi.org/10.3390/e23060698 - 31 May 2021
Cited by 6 | Viewed by 3135
Abstract
Apnea and other breathing-related disorders have been linked to the development of hypertension or impairments of the cardiovascular, cognitive or metabolic systems. The combined assessment of multiple physiological signals acquired during sleep is of fundamental importance for providing additional insights about breathing disorder [...] Read more.
Apnea and other breathing-related disorders have been linked to the development of hypertension or impairments of the cardiovascular, cognitive or metabolic systems. The combined assessment of multiple physiological signals acquired during sleep is of fundamental importance for providing additional insights about breathing disorder events and the associated impairments. In this work, we apply information-theoretic measures to describe the joint dynamics of cardiorespiratory physiological processes in a large group of patients reporting repeated episodes of hypopneas, apneas (central, obstructive, mixed) and respiratory effort related arousals (RERAs). We analyze the heart period as the target process and the airflow amplitude as the driver, computing the predictive information, the information storage, the information transfer, the internal information and the cross information, using a fuzzy kernel entropy estimator. The analyses were performed comparing the information measures among segments during, immediately before and after the respiratory event and with control segments. Results highlight a general tendency to decrease of predictive information and information storage of heart period, as well as of cross information and information transfer from respiration to heart period, during the breathing disordered events. The information-theoretic measures also vary according to the breathing disorder, and significant changes of information transfer can be detected during RERAs, suggesting that the latter could represent a risk factor for developing cardiovascular diseases. These findings reflect the impact of different sleep breathing disorders on respiratory sinus arrhythmia, suggesting overall higher complexity of the cardiac dynamics and weaker cardiorespiratory interactions which may have physiological and clinical relevance. Full article
(This article belongs to the Special Issue Network Physiology and Entropy)
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22 pages, 5035 KiB  
Article
Entropy and Multifractal-Multiscale Indices of Heart Rate Time Series to Evaluate Intricate Cognitive-Autonomic Interactions
by Pierre Bouny, Laurent M. Arsac, Emma Touré Cuq and Veronique Deschodt-Arsac
Entropy 2021, 23(6), 663; https://doi.org/10.3390/e23060663 - 25 May 2021
Cited by 9 | Viewed by 2935
Abstract
Recent research has clarified the existence of a networked system involving a cortical and subcortical circuitry regulating both cognition and cardiac autonomic control, which is dynamically organized as a function of cognitive demand. The main interactions span multiple temporal and spatial scales and [...] Read more.
Recent research has clarified the existence of a networked system involving a cortical and subcortical circuitry regulating both cognition and cardiac autonomic control, which is dynamically organized as a function of cognitive demand. The main interactions span multiple temporal and spatial scales and are extensively governed by nonlinear processes. Hence, entropy and (multi)fractality in heart period time series are suitable to capture emergent behavior of the cognitive-autonomic network coordination. This study investigated how entropy and multifractal-multiscale analyses could depict specific cognitive-autonomic architectures reflected in the heart rate dynamics when students performed selective inhibition tasks. The participants (N=37) completed cognitive interference (Stroop color and word task), action cancellation (stop-signal) and action restraint (go/no-go) tasks, compared to watching a neutral movie as baseline. Entropy and fractal markers (respectively, the refined composite multiscale entropy and multifractal-multiscale detrended fluctuation analysis) outperformed other time-domain and frequency-domain markers of the heart rate variability in distinguishing cognitive tasks. Crucially, the entropy increased selectively during cognitive interference and the multifractality increased during action cancellation. An interpretative hypothesis is that cognitive interference elicited a greater richness in interactive processes that form the central autonomic network while action cancellation, which is achieved via biasing a sensorimotor network, could lead to a scale-specific heightening of multifractal behavior. Full article
(This article belongs to the Special Issue Network Physiology and Entropy)
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21 pages, 448 KiB  
Article
Assessment of Outliers and Detection of Artifactual Network Segments Using Univariate and Multivariate Dispersion Entropy on Physiological Signals
by Evangelos Kafantaris, Ian Piper, Tsz-Yan Milly Lo and Javier Escudero
Entropy 2021, 23(2), 244; https://doi.org/10.3390/e23020244 - 20 Feb 2021
Cited by 1 | Viewed by 2101
Abstract
Network physiology has emerged as a promising paradigm for the extraction of clinically relevant information from physiological signals by moving from univariate to multivariate analysis, allowing for the inspection of interdependencies between organ systems. However, for its successful implementation, the disruptive effects of [...] Read more.
Network physiology has emerged as a promising paradigm for the extraction of clinically relevant information from physiological signals by moving from univariate to multivariate analysis, allowing for the inspection of interdependencies between organ systems. However, for its successful implementation, the disruptive effects of artifactual outliers, which are a common occurrence in physiological recordings, have to be studied, quantified, and addressed. Within the scope of this study, we utilize Dispersion Entropy (DisEn) to initially quantify the capacity of outlier samples to disrupt the values of univariate and multivariate features extracted with DisEn from physiological network segments consisting of synchronised, electroencephalogram, nasal respiratory, blood pressure, and electrocardiogram signals. The DisEn algorithm is selected due to its efficient computation and good performance in the detection of changes in signals for both univariate and multivariate time-series. The extracted features are then utilised for the training and testing of a logistic regression classifier in univariate and multivariate configurations in an effort to partially automate the detection of artifactual network segments. Our results indicate that outlier samples cause significant disruption in the values of extracted features with multivariate features displaying a certain level of robustness based on the number of signals formulating the network segments from which they are extracted. Furthermore, the deployed classifiers achieve noteworthy performance, where the percentage of correct network segment classification surpasses 95% in a number of experimental setups, with the effectiveness of each configuration being affected by the signal in which outliers are located. Finally, due to the increase in the number of features extracted within the framework of network physiology and the observed impact of artifactual samples in the accuracy of their values, the implementation of algorithmic steps capable of effective feature selection is highlighted as an important area for future research. Full article
(This article belongs to the Special Issue Network Physiology and Entropy)
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13 pages, 2075 KiB  
Article
Hemispheric Asymmetry of Functional Brain Networks under Different Emotions Using EEG Data
by Rui Cao, Huiyu Shi, Xin Wang, Shoujun Huo, Yan Hao, Bin Wang, Hao Guo and Jie Xiang
Entropy 2020, 22(9), 939; https://doi.org/10.3390/e22090939 - 26 Aug 2020
Cited by 18 | Viewed by 3692
Abstract
Despite many studies reporting hemispheric asymmetry in the representation and processing of emotions, the essence of the asymmetry remains controversial. Brain network analysis based on electroencephalography (EEG) is a useful biological method to study brain function. Here, EEG data were recorded while participants [...] Read more.
Despite many studies reporting hemispheric asymmetry in the representation and processing of emotions, the essence of the asymmetry remains controversial. Brain network analysis based on electroencephalography (EEG) is a useful biological method to study brain function. Here, EEG data were recorded while participants watched different emotional videos. According to the videos’ emotional categories, the data were divided into four categories: high arousal high valence (HAHV), low arousal high valence (LAHV), low arousal low valence (LALV) and high arousal low valence (HALV). The phase lag index as a connectivity index was calculated in theta (4–7 Hz), alpha (8–13 Hz), beta (14–30 Hz) and gamma (31–45 Hz) bands. Hemispheric networks were constructed for each trial, and graph theory was applied to quantify the hemispheric networks’ topological properties. Statistical analyses showed significant topological differences in the gamma band. The left hemispheric network showed significantly higher clustering coefficient (Cp), global efficiency (Eg) and local efficiency (Eloc) and lower characteristic path length (Lp) under HAHV emotion. The right hemispheric network showed significantly higher Cp and Eloc and lower Lp under HALV emotion. The results showed that the left hemisphere was dominant for HAHV emotion, while the right hemisphere was dominant for HALV emotion. The research revealed the relationship between emotion and hemispheric asymmetry from the perspective of brain networks. Full article
(This article belongs to the Special Issue Network Physiology and Entropy)
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