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Special Issue "Information Theory Applied to Physiological Signals"

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

Deadline for manuscript submissions: closed (30 September 2017)

Special Issue Editors

Guest Editor
Prof. Dr. Danilo P. Mandic

Department of Electrical and Electronic Engineering, Imperial College London, United Kingdom
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Guest Editor
Prof. Dr. Andrzej Cichocki

Brain Science Institute, RIKEN, Japan
Website | E-Mail
Guest Editor
Prof. Dr. Chung-Kang Peng

Rey Institute for Nonlinear Dynamics in Medicine, Harvard Medical School, USA
Website | E-Mail

Special Issue Information

Dear Colleagues,

Information theory is a well-known methodology, traditionally used in communication engineering, and has relatively recently been extended and applied to a variety of emerging areas, including bioengineering. Conceptually, physiological systems can be considered as communication channels of a special kind, which admit the information content can be analysed; this concept has been particularly successful in the analysis of neural systems. Information theory has also been traditionally applied to systems which require well-defined metrics for quantifying their dynamic behaviours, or for quantifying their degrees of nonlinearity and complexity.

Signal analyses, based on information theory, have typically taken the form of entropy, probability, and divergence analyses. In this Special Issue, we consider the most widely analysed physiological signals, such as electrocardiograms (ECG), electroencephalograms (EEG), electromyograms (EMG), electrooculograms (EOG), and respiratory signals. The application of information theory principles to physiological signals has undoubtedly shed light on the intrinsic dynamics and mechanisms underlying many physiological systems, consequently elucidating interactions that would not have been possible using temporal or spectral analyses alone.

With the understanding of the mechanisms governing many physiological systems still remaining a challenge, information theory based analyses are likely to continue to substantially aid in the comprehensive understanding of the physiology and signal generating mechanism. Another challenge is to develop information theoretic measures for real-world physiological data which are notoriously noisy, with drifting baselines, and which do not obey any synthetic probability distribution.

The main goal of this Special Issue is, therefore, to disseminate new and original research based on information theory analyses of physiological signals, in order to assist in both the understanding of physiological phenomena, diagnosis and treatment, and for planning healthcare strategies to prevent the occurrences of certain pathologies. Furthermore, manuscripts summarizing the most recent state-of-the-art of this topic will also be welcome.

Prof. Dr. Danilo P. Mandic
Prof. Dr. Andrzej Cichocki
Prof. Dr. Chung-Kang Peng
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 1500 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

  • Joint analysis of physiological signals at multiple temporal, frequency, and spatial scales (ECG, EEG, EMG EOG, etc.)
  • Multiscale entropy, complexity loss theory for the monitoring and management of diseases
  • Entropy or information content for data fusion from recordings of different natures
  • Computationally efficient entropy measures for physiological data
  • Kullback-Leibler divergence, other divergences applied to physiological monitoring
  • Extensions to symbolic dynamics and coding in biological systems
  • Practical considerations: entropic scales and embedding dimensions, sample size, signal modality characterization for health
  • Levels of consciousness, fatigue, fitness for duty
  • Heart rate variability (HRV) analysis, co-morbidity between HRV and other physiological responses
  • Psychophysiological signals (physical/mental/emotional analysis), especially in newborns and the elderly
  • Complexity loss theory in dementia, epilepsy, posture, and sleep disorders
  • Other clinical applications of multiscale entropy

Related Special Issue

Published Papers (8 papers)

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Research

Open AccessArticle Re-Evaluating Electromyogram–Force Relation in Healthy Biceps Brachii Muscles Using Complexity Measures
Entropy 2017, 19(11), 624; doi:10.3390/e19110624 (registering DOI)
Received: 9 August 2017 / Revised: 26 September 2017 / Accepted: 17 October 2017 / Published: 19 November 2017
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Abstract
The objective of this study is to re-evaluate the relation between surface electromyogram (EMG) and muscle contraction torque in biceps brachii (BB) muscles of healthy subjects using two different complexity measures. Ten healthy subjects were recruited and asked to complete a series of
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The objective of this study is to re-evaluate the relation between surface electromyogram (EMG) and muscle contraction torque in biceps brachii (BB) muscles of healthy subjects using two different complexity measures. Ten healthy subjects were recruited and asked to complete a series of elbow flexion tasks following different isometric muscle contraction levels ranging from 10% to 80% of maximum voluntary contraction (MVC) with each increment of 10%. Meanwhile, both the elbow flexion torque and surface EMG data from the muscle were recorded. The root mean square (RMS), sample entropy (SampEn) and fuzzy entropy (FuzzyEn) of corresponding EMG data were analyzed for each contraction level, and the relation between EMG and muscle torque was accordingly quantified. The experimental results showed a nonlinear relation between the traditional RMS amplitude of EMG and the muscle torque. By contrast, the FuzzyEn of EMG exhibited an improved linear correlation with the muscle torque than the RMS amplitude of EMG, which indicates its great value in estimating BB muscle strength in a simple and straightforward manner. In addition, the SampEn of EMG was found to be insensitive to the varying muscle torques, almost presenting a flat trend with the increment of muscle force. Such a character of the SampEn implied its potential application as a promising surface EMG biomarker for examining neuromuscular changes while overcoming interference from muscle strength. Full article
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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Open AccessArticle Multiscale Sample Entropy of Cardiovascular Signals: Does the Choice between Fixed- or Varying-Tolerance among Scales Influence Its Evaluation and Interpretation?
Entropy 2017, 19(11), 590; doi:10.3390/e19110590
Received: 9 October 2017 / Revised: 30 October 2017 / Accepted: 31 October 2017 / Published: 4 November 2017
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Abstract
Multiscale entropy (MSE) quantifies the cardiovascular complexity evaluating Sample Entropy (SampEn) on coarse-grained series at increasing scales τ. Two approaches exist, one using a fixed tolerance r at all scales (MSEFT), the other a varying tolerance r(τ
[...] Read more.
Multiscale entropy (MSE) quantifies the cardiovascular complexity evaluating Sample Entropy (SampEn) on coarse-grained series at increasing scales τ. Two approaches exist, one using a fixed tolerance r at all scales (MSEFT), the other a varying tolerance r(τ) adjusted following the standard-deviation changes after coarse graining (MSEVT). The aim of this study is to clarify how the choice between MSEFT and MSEVT influences quantification and interpretation of cardiovascular MSE, and whether it affects some signals more than others. To achieve this aim, we considered 2-h long beat-by-beat recordings of inter-beat intervals and of systolic and diastolic blood pressures in male (N = 42) and female (N = 42) healthy volunteers. We compared MSE estimated with fixed and varying tolerances, and evaluated whether the choice between MSEFT and MSEVT estimators influence quantification and interpretation of sex-related differences. We found substantial discrepancies between MSEFT and MSEVT results, related to the degree of correlation among samples and more important for heart rate than for blood pressure; moreover the choice between MSEFT and MSEVT may influence the interpretation of gender differences for MSE of heart rate. We conclude that studies on cardiovascular complexity should carefully choose between fixed- or varying-tolerance estimators, particularly when evaluating MSE of heart rate. Full article
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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Open AccessArticle Challenging Recently Published Parameter Sets for Entropy Measures in Risk Prediction for End-Stage Renal Disease Patients
Entropy 2017, 19(11), 582; doi:10.3390/e19110582
Received: 29 September 2017 / Revised: 26 October 2017 / Accepted: 27 October 2017 / Published: 31 October 2017
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Abstract
Heart rate variability (HRV) analysis is a non-invasive tool for assessing cardiac health. Entropy measures quantify the chaotic properties of HRV, but they are sensitive to the choice of their required parameters. Previous studies therefore have performed parameter optimization, targeting solely their particular
[...] Read more.
Heart rate variability (HRV) analysis is a non-invasive tool for assessing cardiac health. Entropy measures quantify the chaotic properties of HRV, but they are sensitive to the choice of their required parameters. Previous studies therefore have performed parameter optimization, targeting solely their particular patient cohort. In contrast, this work aimed to challenge entropy measures with recently published parameter sets, without time-consuming optimization, for risk prediction in end-stage renal disease patients. Approximate entropy, sample entropy, fuzzy entropy, fuzzy measure entropy, and corrected approximate entropy were examined. In total, 265 hemodialysis patients from the ISAR (rISk strAtification in end-stage Renal disease) study were analyzed. Throughout a median follow-up time of 43 months, 70 patients died. Fuzzy entropy and corrected approximate entropy (CApEn) provided significant hazard ratios, which remained significant after adjustment for clinical risk factors from literature if an entropy maximizing threshold parameter was chosen. Revealing results were seen in the subgroup of patients with heart disease (HD) when setting the radius to a multiple of the data’s standard deviation ( r = 0.2 · σ ); all entropies, except CApEn, predicted mortality significantly and remained significant after adjustment. Therefore, these two parameter settings seem to reflect different cardiac properties. This work shows the potential of entropy measures for cardiovascular risk stratification in cohorts the parameters were not optimized for, and it provides additional insights into the parameter choice. Full article
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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Open AccessFeature PaperArticle Entropy of Entropy: Measurement of Dynamical Complexity for Biological Systems
Entropy 2017, 19(10), 550; doi:10.3390/e19100550
Received: 13 September 2017 / Revised: 10 October 2017 / Accepted: 16 October 2017 / Published: 18 October 2017
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Abstract
Healthy systems exhibit complex dynamics on the changing of information embedded in physiologic signals on multiple time scales that can be quantified by employing multiscale entropy (MSE) analysis. Here, we propose a measure of complexity, called entropy of entropy (EoE) analysis. The analysis
[...] Read more.
Healthy systems exhibit complex dynamics on the changing of information embedded in physiologic signals on multiple time scales that can be quantified by employing multiscale entropy (MSE) analysis. Here, we propose a measure of complexity, called entropy of entropy (EoE) analysis. The analysis combines the features of MSE and an alternate measure of information, called superinformation, useful for DNA sequences. In this work, we apply the hybrid analysis to the cardiac interbeat interval time series. We find that the EoE value is significantly higher for the healthy than the pathologic groups. Particularly, short time series of 70 heart beats is sufficient for EoE analysis with an accuracy of 81% and longer series of 500 beats results in an accuracy of 90%. In addition, the EoE versus Shannon entropy plot of heart rate time series exhibits an inverted U relationship with the maximal EoE value appearing in the middle of extreme order and disorder. Full article
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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Open AccessArticle A Permutation Disalignment Index-Based Complex Network Approach to Evaluate Longitudinal Changes in Brain-Electrical Connectivity
Entropy 2017, 19(10), 548; doi:10.3390/e19100548
Received: 26 September 2017 / Revised: 12 October 2017 / Accepted: 13 October 2017 / Published: 17 October 2017
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Abstract
In the study of neurological disorders, Electroencephalographic (EEG) signal processing can provide valuable information because abnormalities in the interaction between neuron circuits may reflect on macroscopic abnormalities in the electrical potentials that can be detected on the scalp. A Mild Cognitive Impairment (MCI)
[...] Read more.
In the study of neurological disorders, Electroencephalographic (EEG) signal processing can provide valuable information because abnormalities in the interaction between neuron circuits may reflect on macroscopic abnormalities in the electrical potentials that can be detected on the scalp. A Mild Cognitive Impairment (MCI) condition, when caused by a disorder degenerating into dementia, affects the brain connectivity. Motivated by the promising results achieved through the recently developed descriptor of coupling strength between EEG signals, the Permutation Disalignment Index (PDI), the present paper introduces a novel PDI-based complex network model to evaluate the longitudinal variations in brain-electrical connectivity. A group of 33 amnestic MCI subjects was enrolled and followed-up with over four months. The results were compared to MoCA (Montreal Cognitive Assessment) tests, which scores the cognitive abilities of the patient. A significant negative correlation could be observed between MoCA variation and the characteristic path length ( λ ) variation ( r = - 0 . 56 , p = 0 . 0006 ), whereas a significant positive correlation could be observed between MoCA variation and the variation of clustering coefficient (CC, r = 0 . 58 , p = 0 . 0004 ), global efficiency (GE, r = 0 . 57 , p = 0 . 0005 ) and small worldness (SW, r = 0 . 57 , p = 0 . 0005 ). Cognitive decline thus seems to reflect an underlying cortical “disconnection” phenomenon: worsened subjects indeed showed an increased λ and decreased CC, GE and SW. The PDI-based connectivity model, proposed in the present work, could be a novel tool for the objective quantification of longitudinal brain-electrical connectivity changes in MCI subjects. Full article
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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Open AccessArticle Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis
Entropy 2017, 19(6), 222; doi:10.3390/e19060222
Received: 14 March 2017 / Revised: 5 May 2017 / Accepted: 9 May 2017 / Published: 27 May 2017
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Abstract
Epileptic seizure detection is commonly implemented by expert clinicians with visual observation of electroencephalography (EEG) signals, which tends to be time consuming and sensitive to bias. The epileptic detection in most previous research suffers from low power and unsuitability for processing large datasets.
[...] Read more.
Epileptic seizure detection is commonly implemented by expert clinicians with visual observation of electroencephalography (EEG) signals, which tends to be time consuming and sensitive to bias. The epileptic detection in most previous research suffers from low power and unsuitability for processing large datasets. Therefore, a computerized epileptic seizure detection method is highly required to eradicate the aforementioned problems, expedite epilepsy research and aid medical professionals. In this work, we propose an automatic epilepsy diagnosis framework based on the combination of multi-domain feature extraction and nonlinear analysis of EEG signals. Firstly, EEG signals are pre-processed by using the wavelet threshold method to remove the artifacts. We then extract representative features in the time domain, frequency domain, time-frequency domain and nonlinear analysis features based on the information theory. These features are further extracted in five frequency sub-bands based on the clinical interest, and the dimension of the original feature space is then reduced by using both a principal component analysis and an analysis of variance. Furthermore, the optimal combination of the extracted features is identified and evaluated via different classifiers for the epileptic seizure detection of EEG signals. Finally, the performance of the proposed method is investigated by using a public EEG database at the University Hospital Bonn, Germany. Experimental results demonstrate that the proposed epileptic seizure detection method can achieve a high average accuracy of 99.25%, indicating a powerful method in the detection and classification of epileptic seizures. The proposed seizure detection scheme is thus hoped to eliminate the burden of expert clinicians when they are processing a large number of data by visual observation and to speed-up the epilepsy diagnosis. Full article
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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Open AccessArticle Investigation of the Intra- and Inter-Limb Muscle Coordination of Hands-and-Knees Crawling in Human Adults by Means of Muscle Synergy Analysis
Entropy 2017, 19(5), 229; doi:10.3390/e19050229
Received: 27 March 2017 / Revised: 25 April 2017 / Accepted: 15 May 2017 / Published: 17 May 2017
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Abstract
To investigate the intra- and inter-limb muscle coordination mechanism of human hands-and-knees crawling by means of muscle synergy analysis, surface electromyographic (sEMG) signals of 20 human adults were collected bilaterally from 32 limb related muscles during crawling with hands and knees at different
[...] Read more.
To investigate the intra- and inter-limb muscle coordination mechanism of human hands-and-knees crawling by means of muscle synergy analysis, surface electromyographic (sEMG) signals of 20 human adults were collected bilaterally from 32 limb related muscles during crawling with hands and knees at different speeds. The nonnegative matrix factorization (NMF) algorithm was exerted on each limb to extract muscle synergies. The results showed that intra-limb coordination was relatively stable during human hands-and-knees crawling. Two synergies, one relating to the stance phase and the other relating to the swing phase, could be extracted from each limb during a crawling cycle. Synergy structures during different speeds kept good consistency, but the recruitment levels, durations, and phases of muscle synergies were adjusted to adapt the change of crawling speed. Furthermore, the ipsilateral phase lag (IPL) value which was used to depict the inter-limb coordination changed with crawling speed for most subjects, and subjects using the no-limb-pairing mode at low speed tended to adopt the trot-like mode or pace-like mode at high speed. The research results could be well explained by the two-level central pattern generator (CPG) model consisting of a half-center rhythm generator (RG) and a pattern formation (PF) circuit. This study sheds light on the underlying control mechanism of human crawling. Full article
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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Open AccessArticle Muscle Fatigue Analysis of the Deltoid during Three Head-Related Static Isometric Contraction Tasks
Entropy 2017, 19(5), 221; doi:10.3390/e19050221
Received: 27 March 2017 / Revised: 5 May 2017 / Accepted: 9 May 2017 / Published: 11 May 2017
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Abstract
This study aimed to investigate the fatiguing characteristics of muscle-tendon units (MTUs) within skeletal muscles during static isometric contraction tasks. The deltoid was selected as the target muscle and three head-related static isometric contraction tasks were designed to activate three heads of the
[...] Read more.
This study aimed to investigate the fatiguing characteristics of muscle-tendon units (MTUs) within skeletal muscles during static isometric contraction tasks. The deltoid was selected as the target muscle and three head-related static isometric contraction tasks were designed to activate three heads of the deltoid in different modes. Nine male subjects participated in this study. Surface electromyography (SEMG) signals were collected synchronously from the three heads of the deltoid. The performances of five SEMG parameters, including root mean square (RMS), mean power frequency (MPF), the first coefficient of autoregressive model (ARC1), sample entropy (SE) and Higuchi’s fractal dimension (HFD), in quantification of fatigue, were evaluated in terms of sensitivity to variability ratio (SVR) and consistency firstly. Then, the HFD parameter was selected as the fatigue index for further muscle fatigue analysis. The experimental results demonstrated that the three deltoid heads presented different activation modes during three head-related fatiguing contractions. The fatiguing characteristics of the three heads were found to be task-dependent, and the heads kept in a relatively high activation level were more prone to fatigue. In addition, the differences in fatiguing rate between heads increased with the increase in load. The findings of this study can be helpful in better understanding the underlying neuromuscular control strategies of the central nervous system (CNS). Based on the results of this study, the CNS was thought to control the contraction of the deltoid by taking the three heads as functional units, but a certain synergy among heads might also exist to accomplish a contraction task. Full article
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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