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Entropy
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Entropy and Nonlinear Dynamics in Medicine, Health, and Life Sciences
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Entropy and Nonlinear Dynamics in Medicine, Health, and Life Sciences

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

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 12298

Special Issue Editors

Prof. Dr. Tuan D. Pham

E-Mail Website1 Website2
Guest Editor
Department of Biomedical Engineering, Linkoping University, 58183 Linkoping, Sweden
Interests: entropy and nonlinear dynamics with applications to medicine; physiology; health; and biology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jean-Marc Girault

E-Mail Website
Guest Editor
1. Department of Electronics and Control Engineering, Ecole Supérieure d’Electronique de l’Ouest, 49107 Angers, France
2. LAUM UMR CNRS 6613 Le Maine University, 72000 Le Mans, France
Interests: biomedical; entropy; symmetry; recurrences; nonlinear dynamical system; signal
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Methods of entropy and nonlinear dynamics have been developed for gaining insight into the predictability, complexity, and uncertainty of systems involving signals and images. Complex systems are those whose behavior is difficult to predict and model, because of the nonlinear dependencies and relationships between their components. 

Investigations into the underlying behavior and latent patterns of raw complex data, such as time series, multi-channel, and multi-modal images are useful for answering important questions in medicine, physiology, health, and biology.

This Special Issue calls for novel applications of entropy and nonlinear dynamics methods that include, but are not limited to, information–theoretical algorithms; chaos; artificial-intelligence-based nonlinear dynamics; and networks applied to complex diseases, radiology, digital pathology, biomarker discovery, cell biology, and mental health.

Prof. Tuan D. Pham
Prof. Jean-Marc Girault
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

  • entropy
  • nonlinear dynamics
  • chaos
  • recurrence plots
  • fuzzy recurrence plots
  • recurrence networks
  • complex networks
  • predictability
  • time-series analysis
  • image analysis
  • multi-dimensional and multi-modal data analysis
  • complexity analysis
  • artificial intelligence
  • applications to medicine, physiology, health, and biology

Published Papers (4 papers)

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Research

18 pages, 1436 KiB  
Article
Development of a Neurodegenerative Disease Gait Classification Algorithm Using Multiscale Sample Entropy and Machine Learning Classifiers
by Quoc Duy Nam Nguyen, An-Bang Liu and Che-Wei Lin
Entropy 2020, 22(12), 1340; https://doi.org/10.3390/e22121340 - 25 Nov 2020
Cited by 16 | Viewed by 3324
Abstract
The prevalence of neurodegenerative diseases (NDD) has grown rapidly in recent years and NDD screening receives much attention. NDD could cause gait abnormalities so that to screen NDD using gait signal is feasible. The research aim of this study is to develop an [...] Read more.
The prevalence of neurodegenerative diseases (NDD) has grown rapidly in recent years and NDD screening receives much attention. NDD could cause gait abnormalities so that to screen NDD using gait signal is feasible. The research aim of this study is to develop an NDD classification algorithm via gait force (GF) using multiscale sample entropy (MSE) and machine learning models. The Physionet NDD gait database is utilized to validate the proposed algorithm. In the preprocessing stage of the proposed algorithm, new signals were generated by taking one and two times of differential on GF and are divided into various time windows (10/20/30/60-sec). In feature extraction, the GF signal is used to calculate statistical and MSE values. Owing to the imbalanced nature of the Physionet NDD gait database, the synthetic minority oversampling technique (SMOTE) was used to rebalance data of each class. Support vector machine (SVM) and k-nearest neighbors (KNN) were used as the classifiers. The best classification accuracies for the healthy controls (HC) vs. Parkinson’s disease (PD), HC vs. Huntington’s disease (HD), HC vs. amyotrophic lateral sclerosis (ALS), PD vs. HD, PD vs. ALS, HD vs. ALS, HC vs. PD vs. HD vs. ALS, were 99.90%, 99.80%, 100%, 99.75%, 99.90%, 99.55%, and 99.68% under 10-sec time window with KNN. This study successfully developed an NDD gait classification based on MSE and machine learning classifiers. Full article
(This article belongs to the Special Issue Entropy and Nonlinear Dynamics in Medicine, Health, and Life Sciences)
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14 pages, 3999 KiB  
Article
Identification of Denatured Biological Tissues Based on Compressed Sensing and Improved Multiscale Dispersion Entropy during HIFU Treatment
by Bei Liu, Runmin Wang, Ziqi Peng and Lingjie Qin
Entropy 2020, 22(9), 944; https://doi.org/10.3390/e22090944 - 27 Aug 2020
Cited by 8 | Viewed by 2080
Abstract
Identification of denatured biological tissue is crucial to high-intensity focused ultrasound (HIFU) treatment, which can monitor HIFU treatment and improve treatment efficiency. In this paper, a novel method based on compressed sensing (CS) and improved multiscale dispersion entropy (IMDE) is proposed to evaluate [...] Read more.
Identification of denatured biological tissue is crucial to high-intensity focused ultrasound (HIFU) treatment, which can monitor HIFU treatment and improve treatment efficiency. In this paper, a novel method based on compressed sensing (CS) and improved multiscale dispersion entropy (IMDE) is proposed to evaluate the complexity of ultrasonic scattered echo signals during HIFU treatment. In the analysis of CS, the method of orthogonal matching pursuit (OMP) is employed to reconstruct the denoised signal. CS-OMP can denoise the ultrasonic scattered echo signal effectively. Comparing with traditional multiscale dispersion entropy (MDE), IMDE improves the coarse-grained process in the multiscale analysis, which improves the stability of MDE. In the analysis of simulated signals, the entropy value of the IMDE method has less fluctuation compared with MDE, indicating that the IMDE method has better stability. In addition, MDE and IMDE are applied to the 300 cases of ultrasonic scattered echo signals after denoising (including 150 cases of normal tissues and 150 cases of denatured tissues). The experimental results show that the MDE and IMDE values of denatured tissues are higher than normal tissues. Both the MDE and IMDE method can be used to identify whether biological tissue is denatured. However, the multiscale entropy curve of IMDE is smoother and more stable than MDE. The interclass distance of IMDE is greater than MDE, and the intraclass distance of IMDE is less than MDE at different scale factors. This indicates that IMDE can better distinguish normal tissues and denatured tissues to obtain more accurate clinical diagnosis during HIFU treatment. Full article
(This article belongs to the Special Issue Entropy and Nonlinear Dynamics in Medicine, Health, and Life Sciences)
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13 pages, 3808 KiB  
Article
Phase Entropy Analysis of Electrohysterographic Data at the Third Trimester of Human Pregnancy and Active Parturition
by José Javier Reyes-Lagos, Adriana Cristina Pliego-Carrillo, Claudia Ivette Ledesma-Ramírez, Miguel Ángel Peña-Castillo, María Teresa García-González, Gustavo Pacheco-López and Juan Carlos Echeverría
Entropy 2020, 22(8), 798; https://doi.org/10.3390/e22080798 - 22 Jul 2020
Cited by 8 | Viewed by 2917
Abstract
Phase Entropy (PhEn) was recently introduced for evaluating the nonlinear features of physiological time series. PhEn has been demonstrated to be a robust approach in comparison to other entropy-based methods to achieve this goal. In this context, the present study aimed [...] Read more.
Phase Entropy (PhEn) was recently introduced for evaluating the nonlinear features of physiological time series. PhEn has been demonstrated to be a robust approach in comparison to other entropy-based methods to achieve this goal. In this context, the present study aimed to analyze the nonlinear features of raw electrohysterogram (EHG) time series collected from women at the third trimester of pregnancy (TT) and later during term active parturition (P) by PhEn. We collected 10-min longitudinal transabdominal recordings of 24 low-risk pregnant women at TT (from 35 to 38 weeks of pregnancy) and P (>39 weeks of pregnancy). We computed the second-order difference plots (SODPs) for the TT and P stages, and we evaluated the PhEn by modifying the k value, a coarse-graining parameter. Our results pointed out that PhEn in TT is characterized by a higher likelihood of manifesting nonlinear dynamics compared to the P condition. However, both conditions maintain percentages of nonlinear series higher than 66%. We conclude that the nonlinear features appear to be retained for both stages of pregnancy despite the uterine and cervical reorganization process that occurs in the transition from the third trimester to parturition. Full article
(This article belongs to the Special Issue Entropy and Nonlinear Dynamics in Medicine, Health, and Life Sciences)
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16 pages, 4146 KiB  
Article
Digital Volume Pulse Measured at the Fingertip as an Indicator of Diabetic Peripheral Neuropathy in the Aged and Diabetic
by Hai-Cheng Wei, Na Ta, Wen-Rui Hu, Ming-Xia Xiao, Xiao-Jing Tang, Bagus Haryadi, Juin J. Liou and Hsien-Tsai Wu
Entropy 2019, 21(12), 1229; https://doi.org/10.3390/e21121229 - 16 Dec 2019
Cited by 11 | Viewed by 3269
Abstract
This study investigated the application of a modified percussion entropy index (PEIPPI) in assessing the complexity of baroreflex sensitivity (BRS) for diabetic peripheral neuropathy prognosis. The index was acquired by comparing the obedience of the fluctuation tendency in the change between [...] Read more.
This study investigated the application of a modified percussion entropy index (PEIPPI) in assessing the complexity of baroreflex sensitivity (BRS) for diabetic peripheral neuropathy prognosis. The index was acquired by comparing the obedience of the fluctuation tendency in the change between the amplitudes of continuous digital volume pulse (DVP) and variations in the peak-to-peak interval (PPI) from a decomposed intrinsic mode function (i.e., IMF6) through ensemble empirical mode decomposition (EEMD). In total, 100 middle-aged subjects were split into 3 groups: healthy subjects (group 1, 48–89 years, n = 34), subjects with type 2 diabetes without peripheral neuropathy within 5 years (group 2, 42–86 years, n = 42, HbA1c ≥ 6.5%), and type 2 diabetic patients with peripheral neuropathy within 5 years (group 3, 37–75 years, n = 24). The results were also found to be very successful at discriminating between PEIPPI values among the three groups (p < 0.017), and indicated significant associations with the anthropometric (i.e., body weight and waist circumference) and serum biochemical (i.e., triglycerides, glycated hemoglobin, and fasting blood glucose) parameters in all subjects (p < 0.05). The present study, which utilized the DVP signals of aged, overweight subjects and diabetic patients, successfully determined the PPI intervals from IMF6 through EEMD. The PEIPPI can provide a prognosis of peripheral neuropathy from diabetic patients within 5 years after photoplethysmography (PPG) measurement. Full article
(This article belongs to the Special Issue Entropy and Nonlinear Dynamics in Medicine, Health, and Life Sciences)
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