entropy-logo

Journal Browser

Journal Browser

Entropy on Biosignals and Intelligent Systems

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

Deadline for manuscript submissions: closed (31 July 2016) | Viewed by 48969

Special Issue Editors


E-Mail Website
Guest Editor
Signals and Communications Department, Institute for Technological Development and Innovation in Communications, University of Las Palmas de Gran Canaria, 35001 Las Palmas, Spain
Interests: biometrics; biomedical signals; data mining
Special Issues, Collections and Topics in MDPI journals

E-Mail
Guest Editor
Institute for Technological Development and Innovation in Communications, Signals and Communications Department, University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, s/n, Pabellón B - Despacho 102, E-35017, Las Palmas de Gran Canaria, Spain
Interests: bayesian inference; discriminative information; prediction systems; biomathemathics; data mining; clustering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Many specifics of biosignals and intelligent systems are not well addressed by the conventional models currently used in the field of artificial intelligence. The purpose of the Special Issue on “Entropy on Biosignals and Intelligent Systems” is to present and discuss novel ideas, work, and results related to alternative techniques for bioinspired approaches, which depart from mainstream procedures.

 

Nowadays, studies based on complex systems has opened new doors in research fields and, in particular, to improve the quality and the results of diverse applications. Biosignals and intelligent systems easily take care of this task, and are also useful in areas such as biodiversity conservation, biomedicine, security applications, etc.

 

This Special Issue focuses on original and new research results concerning bioinspired systems in science and engineering. Manuscripts discussing biosignals and intelligent systems, and their entropy on applications, are welcome; additionally, submissions addressing novel issues, as well as those addressing more specific topics that illustrate the broad impact of bioinspired entropy-based techniques on image coding, processing and analysis, signal processing and analysis, natural sounds, and video analysis, are welcome.

 

Dr. Carlos M. Travieso
Dr. Jesus B. Alonso
Guest Editors

Keywords

  • biosignal entropy
  • pattern recognition
  • entropy in natural environments
  • artificial intelligence techniques
  • biomedical engineering
  • bioinformatics
  • clustering
  • data mining
  • biomathemathics
  • biostatistic

Published Papers (6 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

1170 KiB  
Article
A Novel Sequence-Based Feature for the Identification of DNA-Binding Sites in Proteins Using Jensen–Shannon Divergence
by Truong Khanh Linh Dang, Cornelia Meckbach, Rebecca Tacke, Stephan Waack and Mehmet Gültas
Entropy 2016, 18(10), 379; https://doi.org/10.3390/e18100379 - 24 Oct 2016
Cited by 5 | Viewed by 6561
Abstract
The knowledge of protein-DNA interactions is essential to fully understand the molecular activities of life. Many research groups have developed various tools which are either structure- or sequence-based approaches to predict the DNA-binding residues in proteins. The structure-based methods usually achieve good results, [...] Read more.
The knowledge of protein-DNA interactions is essential to fully understand the molecular activities of life. Many research groups have developed various tools which are either structure- or sequence-based approaches to predict the DNA-binding residues in proteins. The structure-based methods usually achieve good results, but require the knowledge of the 3D structure of protein; while sequence-based methods can be applied to high-throughput of proteins, but require good features. In this study, we present a new information theoretic feature derived from Jensen–Shannon Divergence (JSD) between amino acid distribution of a site and the background distribution of non-binding sites. Our new feature indicates the difference of a certain site from a non-binding site, thus it is informative for detecting binding sites in proteins. We conduct the study with a five-fold cross validation of 263 proteins utilizing the Random Forest classifier. We evaluate the functionality of our new features by combining them with other popular existing features such as position-specific scoring matrix (PSSM), orthogonal binary vector (OBV), and secondary structure (SS). We notice that by adding our features, we can significantly boost the performance of Random Forest classifier, with a clear increment of sensitivity and Matthews correlation coefficient (MCC). Full article
(This article belongs to the Special Issue Entropy on Biosignals and Intelligent Systems)
Show Figures

Figure 1

2206 KiB  
Article
Contact-Free Detection of Obstructive Sleep Apnea Based on Wavelet Information Entropy Spectrum Using Bio-Radar
by Fugui Qi, Chuantao Li, Shuaijie Wang, Hua Zhang, Jianqi Wang and Guohua Lu
Entropy 2016, 18(8), 306; https://doi.org/10.3390/e18080306 - 18 Aug 2016
Cited by 25 | Viewed by 6488
Abstract
Judgment and early danger warning of obstructive sleep apnea (OSA) is meaningful to the diagnosis of sleep illness. This paper proposed a novel method based on wavelet information entropy spectrum to make an apnea judgment of the OSA respiratory signal detected by bio-radar [...] Read more.
Judgment and early danger warning of obstructive sleep apnea (OSA) is meaningful to the diagnosis of sleep illness. This paper proposed a novel method based on wavelet information entropy spectrum to make an apnea judgment of the OSA respiratory signal detected by bio-radar in wavelet domain. It makes full use of the features of strong irregularity and disorder of respiratory signal resulting from the brain stimulation by real, low airflow during apnea. The experimental results demonstrated that the proposed method is effective for detecting the occurrence of sleep apnea and is also able to detect some apnea cases that the energy spectrum method cannot. Ultimately, the comprehensive judgment accuracy resulting from 10 groups of OSA data is 93.1%, which is promising for the non-contact aided-diagnosis of the OSA. Full article
(This article belongs to the Special Issue Entropy on Biosignals and Intelligent Systems)
Show Figures

Figure 1

800 KiB  
Article
ECG Classification Using Wavelet Packet Entropy and Random Forests
by Taiyong Li and Min Zhou
Entropy 2016, 18(8), 285; https://doi.org/10.3390/e18080285 - 5 Aug 2016
Cited by 373 | Viewed by 16940
Abstract
The electrocardiogram (ECG) is one of the most important techniques for heart disease diagnosis. Many traditional methodologies of feature extraction and classification have been widely applied to ECG analysis. However, the effectiveness and efficiency of such methodologies remain to be improved, and much [...] Read more.
The electrocardiogram (ECG) is one of the most important techniques for heart disease diagnosis. Many traditional methodologies of feature extraction and classification have been widely applied to ECG analysis. However, the effectiveness and efficiency of such methodologies remain to be improved, and much existing research did not consider the separation of training and testing samples from the same set of patients (so called inter-patient scheme). To cope with these issues, in this paper, we propose a method to classify ECG signals using wavelet packet entropy (WPE) and random forests (RF) following the Association for the Advancement of Medical Instrumentation (AAMI) recommendations and the inter-patient scheme. Specifically, we firstly decompose the ECG signals by wavelet packet decomposition (WPD), and then calculate entropy from the decomposed coefficients as representative features, and finally use RF to build an ECG classification model. To the best of our knowledge, it is the first time that WPE and RF are used to classify ECG following the AAMI recommendations and the inter-patient scheme. Extensive experiments are conducted on the publicly available MIT–BIH Arrhythmia database and influence of mother wavelets and level of decomposition for WPD, type of entropy and the number of base learners in RF on the performance are also discussed. The experimental results are superior to those by several state-of-the-art competing methods, showing that WPE and RF is promising for ECG classification. Full article
(This article belongs to the Special Issue Entropy on Biosignals and Intelligent Systems)
Show Figures

Graphical abstract

867 KiB  
Article
Effect of a Percutaneous Coronary Intervention Procedure on Heart Rate Variability and Pulse Transit Time Variability: A Comparison Study Based on Fuzzy Measure Entropy
by Guang Zhang, Chengyu Liu, Lizhen Ji, Jing Yang and Changchun Liu
Entropy 2016, 18(7), 246; https://doi.org/10.3390/e18070246 - 9 Jul 2016
Cited by 2 | Viewed by 6172
Abstract
Percutaneous coronary intervention (PCI) is a common treatment method for patients with coronary artery disease (CAD), but its effect on synchronously measured heart rate variability (HRV) and pulse transit time variability (PTTV) have not been well established. This study aimed to verify whether [...] Read more.
Percutaneous coronary intervention (PCI) is a common treatment method for patients with coronary artery disease (CAD), but its effect on synchronously measured heart rate variability (HRV) and pulse transit time variability (PTTV) have not been well established. This study aimed to verify whether PCI for CAD patients affects both HRV and PTTV parameters. Sixteen CAD patients were enrolled. Two five-minute ECG and finger photoplethysmography (PPG) signals were recorded, one within 24 h before PCI and another within 24 h after PCI. The changes of RR and pulse transit time (PTT) intervals due to the PCI procedure were first compared. Then, HRV and PTTV were evaluated by a standard short-term time-domain variability index of standard deviation of time series (SDTS) and our previously developed entropy-based index of fuzzy measure entropy (FuzzyMEn). To test the effect of different time series length on HRV and PTTV results, we segmented the RR and PTT time series using four time windows of 200, 100, 50 and 25 beats respectively. The PCI-induced changes in HRV and PTTV, as well as in RR and PTT intervals, are different. PCI procedure significantly decreased RR intervals (before PCI 973 ± 85 vs. after PCI 907 ± 100 ms, p < 0.05) while significantly increasing PTT intervals (207 ± 18 vs. 214 ± 19 ms, p < 0.01). For HRV, SDTS-only output significant lower values after PCI when time windows are 100 and 25 beats while presenting no significant decreases for other two time windows. By contrast, FuzzyMEn gave significant lower values after PCI for all four time windows (all p < 0.05). For PTTV, SDTS hardly changed after PCI at any time window (all p > 0.90) whereas FuzzyMEn still reported significant lower values (p < 0.05 for 25 beats time window and p < 0.01 for other three time windows). For both HRV and PTTV, with the increase of time window values, SDTS decreased while FuzzyMEn increased. This pilot study demonstrated that the RR interval decreased whereas the PTT interval increased after the PCI procedure and that there were significant reductions in both HRV and PTTV immediately after PCI using the FuzzyMEn method, indicating the changes in underlying mechanisms in cardiovascular system. Full article
(This article belongs to the Special Issue Entropy on Biosignals and Intelligent Systems)
Show Figures

Graphical abstract

2029 KiB  
Article
Shannon Entropy in a European Seabass (Dicentrarchus labrax) System during the Initial Recovery Period after a Short-Term Exposure to Methylmercury
by Harkaitz Eguiraun, Karmele López-de-Ipiña and Iciar Martinez
Entropy 2016, 18(6), 209; https://doi.org/10.3390/e18060209 - 27 May 2016
Cited by 6 | Viewed by 4448
Abstract
Methylmercury (MeHg) is an environmental contaminant of increasing relevance as a seafood safety hazard that affects the health and welfare of fish. Non-invasive, on-line methodologies to monitor and evaluate the behavior of a fish system in aquaculture may make the identification of altered [...] Read more.
Methylmercury (MeHg) is an environmental contaminant of increasing relevance as a seafood safety hazard that affects the health and welfare of fish. Non-invasive, on-line methodologies to monitor and evaluate the behavior of a fish system in aquaculture may make the identification of altered systems feasible—for example, due to the presence of agents that compromise their welfare and wholesomeness—and find a place in the implementation of Hazard Analysis and Critical Control Points and Fish Welfare Assurance Systems. The Shannon entropy (SE) of a European seabass (Dicentrarchus labrax) system has been shown to differentiate MeHg-treated from non-treated fish, the former displaying a lower SE value than the latter. However, little is known about the initial evolution of the system after removal of the toxicant. To help to cover this gap, the present work aims at providing information about the evolution of the SE of a European seabass system during a recuperation period of 11 days following a two-week treatment with 4 µg·MeHg/L. The results indicate that the SE of the system did not show a recovery trend during the examined period, displaying erratic responses with daily fluctuations and lacking a tendency to reach the initial SE values. Full article
(This article belongs to the Special Issue Entropy on Biosignals and Intelligent Systems)
Show Figures

Graphical abstract

3710 KiB  
Article
Selection of Entropy Based Features for Automatic Analysis of Essential Tremor
by Karmele López-de-Ipiña, Jordi Solé-Casals, Marcos Faundez-Zanuy, Pilar M. Calvo, Enric Sesa, Unai Martinez de Lizarduy, Patricia De La Riva, Jose F. Marti-Masso, Blanca Beitia and Alberto Bergareche
Entropy 2016, 18(5), 184; https://doi.org/10.3390/e18050184 - 16 May 2016
Cited by 26 | Viewed by 7571
Abstract
Biomedical systems produce biosignals that arise from interaction mechanisms. In a general form, those mechanisms occur across multiple scales, both spatial and temporal, and contain linear and non-linear information. In this framework, entropy measures are good candidates in order provide useful evidence about [...] Read more.
Biomedical systems produce biosignals that arise from interaction mechanisms. In a general form, those mechanisms occur across multiple scales, both spatial and temporal, and contain linear and non-linear information. In this framework, entropy measures are good candidates in order provide useful evidence about disorder in the system, lack of information in time-series and/or irregularity of the signals. The most common movement disorder is essential tremor (ET), which occurs 20 times more than Parkinson’s disease. Interestingly, about 50%–70% of the cases of ET have a genetic origin. One of the most used standard tests for clinical diagnosis of ET is Archimedes’ spiral drawing. This work focuses on the selection of non-linear biomarkers from such drawings and handwriting, and it is part of a wider cross study on the diagnosis of essential tremor, where our piece of research presents the selection of entropy features for early ET diagnosis. Classic entropy features are compared with features based on permutation entropy. Automatic analysis system settled on several Machine Learning paradigms is performed, while automatic features selection is implemented by means of ANOVA (analysis of variance) test. The obtained results for early detection are promising and appear applicable to real environments. Full article
(This article belongs to the Special Issue Entropy on Biosignals and Intelligent Systems)
Show Figures

Figure 1

Back to TopTop