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Information Theory Applied to Physiological Signals

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

Deadline for manuscript submissions: closed (30 September 2017) | Viewed by 110902

Special Issue Editors

Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Brain Science Institute, RIKEN, Japan
Rey Institute for Nonlinear Dynamics in Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA

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 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

  • 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

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Published Papers (19 papers)

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12 pages, 2625 KiB  
Article
Information Theoretic-Based Interpretation of a Deep Neural Network Approach in Diagnosing Psychogenic Non-Epileptic Seizures
by Sara Gasparini, Maurizio Campolo, Cosimo Ieracitano, Nadia Mammone, Edoardo Ferlazzo, Chiara Sueri, Giovanbattista Gaspare Tripodi, Umberto Aguglia and Francesco Carlo Morabito
Entropy 2018, 20(2), 43; https://doi.org/10.3390/e20020043 - 23 Jan 2018
Cited by 30 | Viewed by 4470
Abstract
The use of a deep neural network scheme is proposed to help clinicians solve a difficult diagnosis problem in neurology. The proposed multilayer architecture includes a feature engineering step (from time-frequency transformation), a double compressing stage trained by unsupervised learning, and a classification [...] Read more.
The use of a deep neural network scheme is proposed to help clinicians solve a difficult diagnosis problem in neurology. The proposed multilayer architecture includes a feature engineering step (from time-frequency transformation), a double compressing stage trained by unsupervised learning, and a classification stage trained by supervised learning. After fine-tuning, the deep network is able to discriminate well the class of patients from controls with around 90% sensitivity and specificity. This deep model gives better classification performance than some other standard discriminative learning algorithms. As in clinical problems there is a need for explaining decisions, an effort has been carried out to qualitatively justify the classification results. The main novelty of this paper is indeed to give an entropic interpretation of how the deep scheme works and reach the final decision. Full article
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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19 pages, 1072 KiB  
Article
Characterizing Normal and Pathological Gait through Permutation Entropy
by Massimiliano Zanin, David Gómez-Andrés, Irene Pulido-Valdeolivas, Juan Andrés Martín-Gonzalo, Javier López-López, Samuel Ignacio Pascual-Pascual and Estrella Rausell
Entropy 2018, 20(1), 77; https://doi.org/10.3390/e20010077 - 19 Jan 2018
Cited by 17 | Viewed by 5181
Abstract
Cerebral palsy is a physical impairment stemming from a brain lesion at perinatal time, most of the time resulting in gait abnormalities: the first cause of severe disability in childhood. Gait study, and instrumental gait analysis in particular, has been receiving increasing attention [...] Read more.
Cerebral palsy is a physical impairment stemming from a brain lesion at perinatal time, most of the time resulting in gait abnormalities: the first cause of severe disability in childhood. Gait study, and instrumental gait analysis in particular, has been receiving increasing attention in the last few years, for being the complex result of the interactions between different brain motor areas and thus a proxy in the understanding of the underlying neural dynamics. Yet, and in spite of its importance, little is still known about how the brain adapts to cerebral palsy and to its impaired gait and, consequently, about the best strategies for mitigating the disability. In this contribution, we present the hitherto first analysis of joint kinematics data using permutation entropy, comparing cerebral palsy children with a set of matched control subjects. We find a significant increase in the permutation entropy for the former group, thus indicating a more complex and erratic neural control of joints and a non-trivial relationship between the permutation entropy and the gait speed. We further show how this information theory measure can be used to train a data mining model able to forecast the child’s condition. We finally discuss the relevance of these results in clinical applications and specifically in the design of personalized medicine interventions. Full article
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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3283 KiB  
Article
Normalised Mutual Information of High-Density Surface Electromyography during Muscle Fatigue
by Adrian Bingham, Sridhar P. Arjunan, Beth Jelfs and Dinesh K. Kumar
Entropy 2017, 19(12), 697; https://doi.org/10.3390/e19120697 - 20 Dec 2017
Cited by 13 | Viewed by 5373
Abstract
This study has developed a technique for identifying the presence of muscle fatigue based on the spatial changes of the normalised mutual information (NMI) between multiple high density surface electromyography (HD-sEMG) channels. Muscle fatigue in the tibialis anterior (TA) during isometric contractions at [...] Read more.
This study has developed a technique for identifying the presence of muscle fatigue based on the spatial changes of the normalised mutual information (NMI) between multiple high density surface electromyography (HD-sEMG) channels. Muscle fatigue in the tibialis anterior (TA) during isometric contractions at 40% and 80% maximum voluntary contraction levels was investigated in ten healthy participants (Age range: 21 to 35 years; Mean age = 26 years; Male = 4, Female = 6). HD-sEMG was used to record 64 channels of sEMG using a 16 by 4 electrode array placed over the TA. The NMI of each electrode with every other electrode was calculated to form an NMI distribution for each electrode. The total NMI for each electrode (the summation of the electrode’s NMI distribution) highlighted regions of high dependence in the electrode array and was observed to increase as the muscle fatigued. To summarise this increase, a function, M(k), was defined and was found to be significantly affected by fatigue and not by contraction force. The technique discussed in this study has overcome issues regarding electrode placement and was used to investigate how the dependences between sEMG signals within the same muscle change spatially during fatigue. Full article
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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2674 KiB  
Article
Entropy and Compression Capture Different Complexity Features: The Case of Fetal Heart Rate
by João Monteiro-Santos, Hernâni Gonçalves, João Bernardes, Luís Antunes, Mohammad Nozari and Cristina Costa-Santos
Entropy 2017, 19(12), 688; https://doi.org/10.3390/e19120688 - 14 Dec 2017
Cited by 8 | Viewed by 3932
Abstract
Entropy and compression have been used to distinguish fetuses at risk of hypoxia from their healthy counterparts through the analysis of Fetal Heart Rate (FHR). Low correlation that was observed between these two approaches suggests that they capture different complexity features. This study [...] Read more.
Entropy and compression have been used to distinguish fetuses at risk of hypoxia from their healthy counterparts through the analysis of Fetal Heart Rate (FHR). Low correlation that was observed between these two approaches suggests that they capture different complexity features. This study aims at characterizing the complexity of FHR features captured by entropy and compression, using as reference international guidelines. Single and multi-scale approaches were considered in the computation of entropy and compression. The following physiologic-based features were considered: FHR baseline; percentage of abnormal long (%abLTV) and short (%abSTV) term variability; average short term variability; and, number of acceleration and decelerations. All of the features were computed on a set of 68 intrapartum FHR tracings, divided as normal, mildly, and moderately-severely acidemic born fetuses. The correlation between entropy/compression features and the physiologic-based features was assessed. There were correlations between compressions and accelerations and decelerations, but neither accelerations nor decelerations were significantly correlated with entropies. The %abSTV was significantly correlated with entropies (ranging between −0.54 and −0.62), and to a higher extent with compression (ranging between −0.80 and −0.94). Distinction between groups was clearer in the lower scales using entropy and in the higher scales using compression. Entropy and compression are complementary complexity measures. Full article
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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848 KiB  
Article
Altered Brain Complexity in Women with Primary Dysmenorrhea: A Resting-State Magneto-Encephalography Study Using Multiscale Entropy Analysis
by Intan Low, Po-Chih Kuo, Yu-Hsiang Liu, Cheng-Lin Tsai, Hsiang-Tai Chao, Jen-Chuen Hsieh, Li-Fen Chen and Yong-Sheng Chen
Entropy 2017, 19(12), 680; https://doi.org/10.3390/e19120680 - 11 Dec 2017
Cited by 10 | Viewed by 5510
Abstract
How chronic pain affects brain functions remains unclear. As a potential indicator, brain complexity estimated by entropy-based methods may be helpful for revealing the underlying neurophysiological mechanism of chronic pain. In this study, complexity features with multiple time scales and spectral features were [...] Read more.
How chronic pain affects brain functions remains unclear. As a potential indicator, brain complexity estimated by entropy-based methods may be helpful for revealing the underlying neurophysiological mechanism of chronic pain. In this study, complexity features with multiple time scales and spectral features were extracted from resting-state magnetoencephalographic signals of 156 female participants with/without primary dysmenorrhea (PDM) during pain-free state. Revealed by multiscale sample entropy (MSE), PDM patients (PDMs) exhibited loss of brain complexity in regions associated with sensory, affective, and evaluative components of pain, including sensorimotor, limbic, and salience networks. Significant correlations between MSE values and psychological states (depression and anxiety) were found in PDMs, which may indicate specific nonlinear disturbances in limbic and default mode network circuits after long-term menstrual pain. These findings suggest that MSE is an important measure of brain complexity and is potentially applicable to future diagnosis of chronic pain. Full article
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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4601 KiB  
Article
Automated Detection of Paroxysmal Atrial Fibrillation Using an Information-Based Similarity Approach
by Xingran Cui, Emily Chang, Wen-Hung Yang, Bernard C. Jiang, Albert C. Yang and Chung-Kang Peng
Entropy 2017, 19(12), 677; https://doi.org/10.3390/e19120677 - 10 Dec 2017
Cited by 25 | Viewed by 5319
Abstract
Atrial fibrillation (AF) is an abnormal rhythm of the heart, which can increase heart-related complications. Paroxysmal AF episodes occur intermittently with varying duration. Human-based diagnosis of paroxysmal AF with a longer-term electrocardiogram recording is time-consuming. Here we present a fully automated ensemble model [...] Read more.
Atrial fibrillation (AF) is an abnormal rhythm of the heart, which can increase heart-related complications. Paroxysmal AF episodes occur intermittently with varying duration. Human-based diagnosis of paroxysmal AF with a longer-term electrocardiogram recording is time-consuming. Here we present a fully automated ensemble model for AF episode detection based on RR-interval time series, applying a novel approach of information-based similarity analysis and ensemble scheme. By mapping RR-interval time series to binary symbolic sequences and comparing the rank-frequency patterns of m-bit words, the dissimilarity between AF and normal sinus rhythms (NSR) were quantified. To achieve high detection specificity and sensitivity, and low variance, a weighted variation of bagging with multiple AF and NSR templates was applied. By performing dissimilarity comparisons between unknown RR-interval time series and multiple templates, paroxysmal AF episodes were detected. Based on our results, optimal AF detection parameters are symbolic word length m = 9 and observation window n = 150, achieving 97.04% sensitivity, 97.96% specificity, and 97.78% overall accuracy. Sensitivity, specificity, and overall accuracy vary little despite changes in m and n parameters. This study provides quantitative information to enhance the categorization of AF and normal cardiac rhythms. Full article
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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3539 KiB  
Article
Association between Multiscale Entropy Characteristics of Heart Rate Variability and Ischemic Stroke Risk in Patients with Permanent Atrial Fibrillation
by Ryo Matsuoka, Kohzoh Yoshino, Eiichi Watanabe and Ken Kiyono
Entropy 2017, 19(12), 672; https://doi.org/10.3390/e19120672 - 07 Dec 2017
Cited by 2 | Viewed by 4292
Abstract
Multiscale entropy (MSE) profiles of heart rate variability (HRV) in patients with atrial fibrillation (AFib) provides clinically useful information for ischemic stroke risk assessment, suggesting that the complex properties characterized by MSE profiles are associated with ischemic stroke risk. However, the meaning of [...] Read more.
Multiscale entropy (MSE) profiles of heart rate variability (HRV) in patients with atrial fibrillation (AFib) provides clinically useful information for ischemic stroke risk assessment, suggesting that the complex properties characterized by MSE profiles are associated with ischemic stroke risk. However, the meaning of HRV complexity in patients with AFib has not been clearly interpreted, and the physical and mathematical understanding of the relation between HRV dynamics and the ischemic stroke risk is not well established. To gain a deeper insight into HRV dynamics in patients with AFib, and to improve ischemic stroke risk assessment using HRV analysis, we study the HRV characteristics related to MSE profiles, such as the long-range correlation and probability density function. In this study, we analyze the HRV time series of 173 patients with permanent AFib. Our results show that, although HRV time series in patients with AFib exhibit long-range correlation (1/f fluctuations)—as observed in healthy subjects—in a range longer than 90 s, these autocorrelation properties have no significant predictive power for ischemic stroke occurrence. Further, the probability density function structure of the coarse-grained times series at scales greater than 2 s is dominantly associated with ischemic stroke risk. This observation could provide valuable information for improving ischemic stroke risk assessment using HRV analysis. Full article
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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2152 KiB  
Article
Assessment of Heart Rate Variability during an Endurance Mountain Trail Race by Multi-Scale Entropy Analysis
by Montserrat Vallverdú, Aroa Ruiz-Muñoz, Emma Roca, Pere Caminal, Ferran A. Rodríguez, Alfredo Irurtia and Alexandre Perera
Entropy 2017, 19(12), 658; https://doi.org/10.3390/e19120658 - 01 Dec 2017
Cited by 6 | Viewed by 5168
Abstract
The aim of the study was to analyze heart rate variability (HRV) response to high-intensity exercise during a 35-km mountain trail race and to ascertain whether fitness level could influence autonomic nervous system (ANS) modulation. Time-domain, frequency-domain, and multi-scale entropy (MSE) [...] Read more.
The aim of the study was to analyze heart rate variability (HRV) response to high-intensity exercise during a 35-km mountain trail race and to ascertain whether fitness level could influence autonomic nervous system (ANS) modulation. Time-domain, frequency-domain, and multi-scale entropy (MSE) indexes were calculated for eleven mountain-trail runners who completed the race. Many changes were observed, mostly related to exercise load and fatigue. These changes were characterized by increased mean values and standard deviations of the normal-to-normal intervals associated with sympathetic activity, and by decreased differences between successive intervals related to parasympathetic activity. Normalized low frequency (LF) power suggested that ANS modulation varied greatly during the race and between individuals. Normalized high frequency (HF) power, associated with parasympathetic activity, varied considerably over the race, and tended to decrease at the final stages, whereas changes in the LF/HF ratio corresponded to intervals with varying exercise load. MSE indexes, related to system complexity, indicated the existence of many interactions between the heart and its neurological control mechanism. The time-domain, frequency-domain, and MSE indexes were also able to discriminate faster from slower runners, mainly in the more difficult and in the final stages of the race. These findings suggest the use of HRV analysis to study cardiac function mechanisms in endurance sports. Full article
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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2018 KiB  
Article
Cosine Similarity Entropy: Self-Correlation-Based Complexity Analysis of Dynamical Systems
by Theerasak Chanwimalueang and Danilo P. Mandic
Entropy 2017, 19(12), 652; https://doi.org/10.3390/e19120652 - 30 Nov 2017
Cited by 32 | Viewed by 7791
Abstract
The nonparametric Sample Entropy (SE) estimator has become a standard for the quantification of structural complexity of nonstationary time series, even in critical cases of unfavorable noise levels. The SE has proven very successful for signals that exhibit a certain degree of the [...] Read more.
The nonparametric Sample Entropy (SE) estimator has become a standard for the quantification of structural complexity of nonstationary time series, even in critical cases of unfavorable noise levels. The SE has proven very successful for signals that exhibit a certain degree of the underlying structure, but do not obey standard probability distributions, a typical case in real-world scenarios such as with physiological signals. However, the SE estimates structural complexity based on uncertainty rather than on (self) correlation, so that, for reliable estimation, the SE requires long data segments, is sensitive to spikes and erratic peaks in data, and owing to its amplitude dependence it exhibits lack of precision for signals with long-term correlations. To this end, we propose a class of new entropy estimators based on the similarity of embedding vectors, evaluated through the angular distance, the Shannon entropy and the coarse-grained scale. Analysis of the effects of embedding dimension, sample size and tolerance shows that the so introduced Cosine Similarity Entropy (CSE) and the enhanced Multiscale Cosine Similarity Entropy (MCSE) are amplitude-independent and therefore superior to the SE when applied to short time series. Unlike the SE, the CSE is shown to yield valid entropy values over a broad range of embedding dimensions. By evaluating the CSE and the MCSE over a variety of benchmark synthetic signals as well as for real-world data (heart rate variability of three different cardiovascular pathologies), the proposed algorithms are demonstrated to be able to quantify degrees of structural complexity in the context of self-correlation over small to large temporal scales, thus offering physically meaningful interpretations and rigor in the understanding the intrinsic properties of the structural complexity of a system, such as the number of its degrees of freedom. Full article
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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2659 KiB  
Article
Parametric PET Image Reconstruction via Regional Spatial Bases and Pharmacokinetic Time Activity Model
by Naoki Kawamura, Tatsuya Yokota, Hidekata Hontani, Muneyuki Sakata and Yuichi Kimura
Entropy 2017, 19(11), 629; https://doi.org/10.3390/e19110629 - 22 Nov 2017
Cited by 2 | Viewed by 3942
Abstract
It is known that the process of reconstruction of a Positron Emission Tomography (PET) image from sinogram data is very sensitive to measurement noises; it is still an important research topic to reconstruct PET images with high signal-to-noise ratios. In this paper, we [...] Read more.
It is known that the process of reconstruction of a Positron Emission Tomography (PET) image from sinogram data is very sensitive to measurement noises; it is still an important research topic to reconstruct PET images with high signal-to-noise ratios. In this paper, we propose a new reconstruction method for a temporal series of PET images from a temporal series of sinogram data. In the proposed method, PET images are reconstructed by minimizing the Kullback–Leibler divergence between the observed sinogram data and sinogram data derived from a parametric model of PET images. The contributions of the proposition include the following: (1) regions of targets in images are explicitly expressed using a set of spatial bases in order to ignore the noises in the background; (2) a parametric time activity model of PET images is explicitly introduced as a constraint; and (3) an algorithm for solving the optimization problem is clearly described. To demonstrate the advantages of the proposed method, quantitative evaluations are performed using both synthetic and clinical data of human brains. Full article
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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1209 KiB  
Article
Re-Evaluating Electromyogram–Force Relation in Healthy Biceps Brachii Muscles Using Complexity Measures
by Xiaofei Zhu, Xu Zhang, Xiao Tang, Xiaoping Gao and Xiang Chen
Entropy 2017, 19(11), 624; https://doi.org/10.3390/e19110624 - 19 Nov 2017
Cited by 10 | Viewed by 5262
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 [...] Read more.
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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2071 KiB  
Article
Multiscale Sample Entropy of Cardiovascular Signals: Does the Choice between Fixed- or Varying-Tolerance among Scales Influence Its Evaluation and Interpretation?
by Paolo Castiglioni, Paolo Coruzzi, Matteo Bini, Gianfranco Parati and Andrea Faini
Entropy 2017, 19(11), 590; https://doi.org/10.3390/e19110590 - 04 Nov 2017
Cited by 20 | Viewed by 4290
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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808 KiB  
Article
Challenging Recently Published Parameter Sets for Entropy Measures in Risk Prediction for End-Stage Renal Disease Patients
by Stefan Hagmair, Martin Bachler, Matthias C. Braunisch, Georg Lorenz, Christoph Schmaderer, Anna-Lena Hasenau, Lukas Von Stülpnagel, Axel Bauer, Kostantinos D. Rizas, Siegfried Wassertheurer and Christopher C. Mayer
Entropy 2017, 19(11), 582; https://doi.org/10.3390/e19110582 - 31 Oct 2017
Cited by 2 | Viewed by 3775
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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856 KiB  
Article
Entropy of Entropy: Measurement of Dynamical Complexity for Biological Systems
by Chang Francis Hsu, Sung-Yang Wei, Han-Ping Huang, Long Hsu, Sien Chi and Chung-Kang Peng
Entropy 2017, 19(10), 550; https://doi.org/10.3390/e19100550 - 18 Oct 2017
Cited by 28 | Viewed by 6661
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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2554 KiB  
Article
A Permutation Disalignment Index-Based Complex Network Approach to Evaluate Longitudinal Changes in Brain-Electrical Connectivity
by Nadia Mammone, Simona De Salvo, Cosimo Ieracitano, Silvia Marino, Angela Marra, Francesco Corallo and Francesco C. Morabito
Entropy 2017, 19(10), 548; https://doi.org/10.3390/e19100548 - 17 Oct 2017
Cited by 16 | Viewed by 5275
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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889 KiB  
Article
Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis
by Lina Wang, Weining Xue, Yang Li, Meilin Luo, Jie Huang, Weigang Cui and Chao Huang
Entropy 2017, 19(6), 222; https://doi.org/10.3390/e19060222 - 27 May 2017
Cited by 190 | Viewed by 13585
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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3987 KiB  
Article
Investigation of the Intra- and Inter-Limb Muscle Coordination of Hands-and-Knees Crawling in Human Adults by Means of Muscle Synergy Analysis
by Xiang Chen, Xiaocong Niu, De Wu, Yi Yu and Xu Zhang
Entropy 2017, 19(5), 229; https://doi.org/10.3390/e19050229 - 17 May 2017
Cited by 21 | Viewed by 7364
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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2200 KiB  
Article
Muscle Fatigue Analysis of the Deltoid during Three Head-Related Static Isometric Contraction Tasks
by Wenxiang Cui, Xiang Chen, Shuai Cao and Xu Zhang
Entropy 2017, 19(5), 221; https://doi.org/10.3390/e19050221 - 11 May 2017
Cited by 5 | Viewed by 6155
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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Review

Jump to: Research

29 pages, 1005 KiB  
Review
Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison
by Rubén Martín-Clemente, Javier Olias, Deepa Beeta Thiyam, Andrzej Cichocki and Sergio Cruces
Entropy 2018, 20(1), 7; https://doi.org/10.3390/e20010007 - 02 Jan 2018
Cited by 25 | Viewed by 6300
Abstract
Brain computer interfaces (BCIs) have been attracting a great interest in recent years. The common spatial patterns (CSP) technique is a well-established approach to the spatial filtering of the electroencephalogram (EEG) data in BCI applications. Even though CSP was originally proposed from a [...] Read more.
Brain computer interfaces (BCIs) have been attracting a great interest in recent years. The common spatial patterns (CSP) technique is a well-established approach to the spatial filtering of the electroencephalogram (EEG) data in BCI applications. Even though CSP was originally proposed from a heuristic viewpoint, it can be also built on very strong foundations using information theory. This paper reviews the relationship between CSP and several information-theoretic approaches, including the Kullback–Leibler divergence, the Beta divergence and the Alpha-Beta log-det (AB-LD)divergence. We also revise other approaches based on the idea of selecting those features that are maximally informative about the class labels. The performance of all the methods will be also compared via experiments. Full article
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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