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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: 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 (3 papers)

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Research

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
Cited by 1 | PDF Full-text (889 KB) | HTML Full-text | XML Full-text
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
PDF Full-text (3987 KB) | HTML Full-text | XML Full-text
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
PDF Full-text (2200 KB) | HTML Full-text | XML Full-text
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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