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Special Issue "Selected Papers from 36th Annual Conference of Spanish Society of Biomedical Engineering"

A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: closed (28 February 2019)

Special Issue Editors

Guest Editor
Prof. Dr. Raúl Alcaraz

Research Group in Electronic, Biomedical and Telecommunication Engineering, Universidad de Castilla-La Mancha, Campus Universitario s/n, 16071, Cuenca
Website | E-Mail
Interests: entropy; complexity; information theory; information geometry; nonlinear dynamics; computational mathematics and statistics in medicine; biomedical time series analysis; cardiac signal processing
Guest Editor
Dr. Elizabete Aramendi

Department of Communications Engineering, University of Basque Country, (UPV/EHU), Bilbao 48013, Spain
Website | E-Mail
Interests: biomedical signal processing; automated algorithms during resuscitation; management of large resuscitation datasets
Guest Editor
Dr. Raimon Jané Campos

Institute for Bioengineering of Catalonia (IBEC), Baldiri Reixac, 10-12, 08028 Barcelona, Spain
Website | E-Mail
Interests: biomedical signal and system applied to cardiorespiratory diseases
Guest Editor
Dr. Gloria Bueno

VISILAB, University of Castilla-La Mancha, E.T.S.I. Industriales, Avda Camilo Jose Cela s/n, Ciudad Real 13071, Spain
Website | E-Mail
Interests: artificial intelligence; biomedical and computer vision applications; decision support systems; image analysis

Special Issue Information

Dear Colleagues,

The Spanish Society of Biomedical Engineering (SEIB) runs an annual conference to bring together researchers, students, and professionals working in Biomedical Engineering. The objective is to close the gap between engineering and medicine and, thus, to advance in the diagnosis, monitoring, and therapy of a variety of diseases. This year, the SEIB Annual Conference (CASEIB) will be held on the 21st–23rd of November at the University of Castilla-La Mancha in Ciudad Real and will be supported by Entropy. This is an international journal dealing with the development and/or application of entropy or information–theoretic concepts in a wide variety of applications (for more details, see https://www.mdpi.com/journal/entropy/about). Thus, this Special Issue will collect the most relevant papers dealing with entropy and information theory-based applications presented in this conference. 

Hence, we encourage the authors who have presented an article at CASEIB 2018 and who feel that their contribution is within the scope of interest of the journal Entropy to apply through the following link: http://caseib.es/2018/revista-entropy. The selected authors will be asked to submit an original and essential extension of the CASEIB paper to be considered for publication no later than February 28th. More details can be found at https://www.mdpi.com/journal/entropy/instructions#preparation. The accepted papers, after a normal process of peer-review by experts in the field of biomedical engineering, will be published in Entropy. The processing charge (1500 CHF) will be paid on this occasion by both Entropy and the CASEIB organization.

Looking forward to receiving your support on this initiative.

Dr. Raúl Alcaraz
Dr. Elizabete Aramendi
Dr. Gloria Bueno
Dr. Raimon Jané Campos
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 1600 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.

Published Papers (7 papers)

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Research

Open AccessArticle
Machine Learning Techniques to Identify Antimicrobial Resistance in the Intensive Care Unit
Entropy 2019, 21(6), 603; https://doi.org/10.3390/e21060603
Received: 8 April 2019 / Revised: 4 June 2019 / Accepted: 13 June 2019 / Published: 18 June 2019
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Abstract
The presence of bacteria with resistance to specific antibiotics is one of the greatest threats to the global health system. According to the World Health Organization, antimicrobial resistance has already reached alarming levels in many parts of the world, involving a social and [...] Read more.
The presence of bacteria with resistance to specific antibiotics is one of the greatest threats to the global health system. According to the World Health Organization, antimicrobial resistance has already reached alarming levels in many parts of the world, involving a social and economic burden for the patient, for the system, and for society in general. Because of the critical health status of patients in the intensive care unit (ICU), time is critical to identify bacteria and their resistance to antibiotics. Since common antibiotics resistance tests require between 24 and 48 h after the culture is collected, we propose to apply machine learning (ML) techniques to determine whether a bacterium will be resistant to different families of antimicrobials. For this purpose, clinical and demographic features from the patient, as well as data from cultures and antibiograms are considered. From a population point of view, we also show graphically the relationship between different bacteria and families of antimicrobials by performing correspondence analysis. Results of the ML techniques evidence non-linear relationships helping to identify antimicrobial resistance at the ICU, with performance dependent on the family of antimicrobials. A change in the trend of antimicrobial resistance is also evidenced. Full article
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Open AccessArticle
Entropy Rate Superpixel Classification for Automatic Red Lesion Detection in Fundus Images
Entropy 2019, 21(4), 417; https://doi.org/10.3390/e21040417
Received: 28 February 2019 / Revised: 17 April 2019 / Accepted: 17 April 2019 / Published: 19 April 2019
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Abstract
Diabetic retinopathy (DR) is the main cause of blindness in the working-age population in developed countries. Digital color fundus images can be analyzed to detect lesions for large-scale screening. Thereby, automated systems can be helpful in the diagnosis of this disease. The aim [...] Read more.
Diabetic retinopathy (DR) is the main cause of blindness in the working-age population in developed countries. Digital color fundus images can be analyzed to detect lesions for large-scale screening. Thereby, automated systems can be helpful in the diagnosis of this disease. The aim of this study was to develop a method to automatically detect red lesions (RLs) in retinal images, including hemorrhages and microaneurysms. These signs are the earliest indicators of DR. Firstly, we performed a novel preprocessing stage to normalize the inter-image and intra-image appearance and enhance the retinal structures. Secondly, the Entropy Rate Superpixel method was used to segment the potential RL candidates. Then, we reduced superpixel candidates by combining inaccurately fragmented regions within structures. Finally, we classified the superpixels using a multilayer perceptron neural network. The used database contained 564 fundus images. The DB was randomly divided into a training set and a test set. Results on the test set were measured using two different criteria. With a pixel-based criterion, we obtained a sensitivity of 81.43% and a positive predictive value of 86.59%. Using an image-based criterion, we reached 84.04% sensitivity, 85.00% specificity and 84.45% accuracy. The algorithm was also evaluated on the DiaretDB1 database. The proposed method could help specialists in the detection of RLs in diabetic patients. Full article
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Open AccessArticle
Aging Modulates the Resting Brain after a Memory Task: A Validation Study from Multivariate Models
Entropy 2019, 21(4), 411; https://doi.org/10.3390/e21040411
Received: 28 February 2019 / Revised: 12 April 2019 / Accepted: 16 April 2019 / Published: 17 April 2019
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Abstract
Recent work has demonstrated that aging modulates the resting brain. However, the study of these modulations after cognitive practice, resulting from a memory task, has been scarce. This work aims at examining age-related changes in the functional reorganization of the resting brain after [...] Read more.
Recent work has demonstrated that aging modulates the resting brain. However, the study of these modulations after cognitive practice, resulting from a memory task, has been scarce. This work aims at examining age-related changes in the functional reorganization of the resting brain after cognitive training, namely, neuroplasticity, by means of the most innovative tools for data analysis. To this end, electroencephalographic activity was recorded in 34 young and 38 older participants. Different methods for data analyses, including frequency, time-frequency and machine learning-based prediction models were conducted. Results showed reductions in Alpha power in old compared to young adults in electrodes placed over posterior and anterior areas of the brain. Moreover, young participants showed Alpha power increases after task performance, while their older counterparts exhibited a more invariant pattern of results. These results were significant in the 140–160 s time window in electrodes placed over anterior regions of the brain. Machine learning analyses were able to accurately classify participants by age, but failed to predict whether resting state scans took place before or after the memory task. These findings greatly contribute to the development of multivariate tools for electroencephalogram (EEG) data analysis and improve our understanding of age-related changes in the functional reorganization of the resting brain. Full article
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Open AccessArticle
First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning
Entropy 2019, 21(4), 356; https://doi.org/10.3390/e21040356
Received: 28 February 2019 / Revised: 25 March 2019 / Accepted: 29 March 2019 / Published: 2 April 2019
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Abstract
Analysis of histopathological image supposes the most reliable procedure to identify prostate cancer. Most studies try to develop computer aid-systems to face the Gleason grading problem. On the contrary, we delve into the discrimination between healthy and cancerous tissues in its earliest stage, [...] Read more.
Analysis of histopathological image supposes the most reliable procedure to identify prostate cancer. Most studies try to develop computer aid-systems to face the Gleason grading problem. On the contrary, we delve into the discrimination between healthy and cancerous tissues in its earliest stage, only focusing on the information contained in the automatically segmented gland candidates. We propose a hand-driven learning approach, in which we perform an exhaustive hand-crafted feature extraction stage combining in a novel way descriptors of morphology, texture, fractals and contextual information of the candidates under study. Then, we carry out an in-depth statistical analysis to select the most relevant features that constitute the inputs to the optimised machine-learning classifiers. Additionally, we apply for the first time on prostate segmented glands, deep-learning algorithms modifying the popular VGG19 neural network. We fine-tuned the last convolutional block of the architecture to provide the model specific knowledge about the gland images. The hand-driven learning approach, using a nonlinear Support Vector Machine, reports a slight outperforming over the rest of experiments with a final multi-class accuracy of 0.876 ± 0.026 in the discrimination between false glands (artefacts), benign glands and Gleason grade 3 glands. Full article
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Open AccessArticle
Optimization of the KNN Supervised Classification Algorithm as a Support Tool for the Implantation of Deep Brain Stimulators in Patients with Parkinson’s Disease
Entropy 2019, 21(4), 346; https://doi.org/10.3390/e21040346
Received: 28 February 2019 / Revised: 21 March 2019 / Accepted: 26 March 2019 / Published: 29 March 2019
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Abstract
Deep Brain Stimulation (DBS) of the Subthalamic Nuclei (STN) is the most used surgical treatment to improve motor skills in patients with Parkinson’s Disease (PD) who do not adequately respond to pharmacological treatment, or have related side effects. During surgery for the implantation [...] Read more.
Deep Brain Stimulation (DBS) of the Subthalamic Nuclei (STN) is the most used surgical treatment to improve motor skills in patients with Parkinson’s Disease (PD) who do not adequately respond to pharmacological treatment, or have related side effects. During surgery for the implantation of a DBS system, signals are obtained through microelectrodes recordings (MER) at different depths of the brain. These signals are analyzed by neurophysiologists to detect the entry and exit of the STN region, as well as the optimal depth for electrode implantation. In the present work, a classification model is developed and supervised by the K-nearest neighbour algorithm (KNN), which is automatically trained from the 18 temporal features of MER registers of 14 patients with PD in order to provide a clinical support tool during DBS surgery. We investigate the effect of different standardizations of the generated database, the optimal definition of KNN configuration parameters, and the selection of features that maximize KNN performance. The results indicated that KNN trained with data that was standardized per cerebral hemisphere and per patient presented the best performance, achieving an accuracy of 94.35% (p < 0.001). By using feature selection algorithms, it was possible to achieve 93.5% in accuracy in selecting a subset of six features, improving computation time while processing in real time. Full article
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Open AccessArticle
Combination of Global Features for the Automatic Quality Assessment of Retinal Images
Entropy 2019, 21(3), 311; https://doi.org/10.3390/e21030311
Received: 28 February 2019 / Revised: 14 March 2019 / Accepted: 18 March 2019 / Published: 21 March 2019
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Abstract
Diabetic retinopathy (DR) is one of the most common causes of visual loss in developed countries. Computer-aided diagnosis systems aimed at detecting DR can reduce the workload of ophthalmologists in screening programs. Nevertheless, a large number of retinal images cannot be analyzed by [...] Read more.
Diabetic retinopathy (DR) is one of the most common causes of visual loss in developed countries. Computer-aided diagnosis systems aimed at detecting DR can reduce the workload of ophthalmologists in screening programs. Nevertheless, a large number of retinal images cannot be analyzed by physicians and automatic methods due to poor quality. Automatic retinal image quality assessment (RIQA) is needed before image analysis. The purpose of this study was to combine novel generic quality features to develop a RIQA method. Several features were calculated from retinal images to achieve this goal. Features derived from the spatial and spectral entropy-based quality (SSEQ) and the natural images quality evaluator (NIQE) methods were extracted. They were combined with novel sharpness and luminosity measures based on the continuous wavelet transform (CWT) and the hue saturation value (HSV) color model, respectively. A subset of non-redundant features was selected using the fast correlation-based filter (FCBF) method. Subsequently, a multilayer perceptron (MLP) neural network was used to obtain the quality of images from the selected features. Classification results achieved 91.46% accuracy, 92.04% sensitivity, and 87.92% specificity. Results suggest that the proposed RIQA method could be applied in a more general computer-aided diagnosis system aimed at detecting a variety of retinal pathologies such as DR and age-related macular degeneration. Full article
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Open AccessArticle
Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest
Entropy 2019, 21(3), 305; https://doi.org/10.3390/e21030305
Received: 8 March 2019 / Accepted: 19 March 2019 / Published: 21 March 2019
Cited by 1 | PDF Full-text (1078 KB) | HTML Full-text | XML Full-text
Abstract
The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary for the early recognition of the arrest and the detection of return of spontaneous circulation (end of the arrest). The only signal available in every single defibrillator and valid for the [...] Read more.
The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary for the early recognition of the arrest and the detection of return of spontaneous circulation (end of the arrest). The only signal available in every single defibrillator and valid for the detection of pulse is the electrocardiogram (ECG). In this study we propose two deep neural network (DNN) architectures to detect pulse using short ECG segments (5 s), i.e., to classify the rhythm into pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A total of 3914 5-s ECG segments, 2372 PR and 1542 PEA, were extracted from 279 OHCA episodes. Data were partitioned patient-wise into training (80%) and test (20%) sets. The first DNN architecture was a fully convolutional neural network, and the second architecture added a recurrent layer to learn temporal dependencies. Both DNN architectures were tuned using Bayesian optimization, and the results for the test set were compared to state-of-the art PR/PEA discrimination algorithms based on machine learning and hand crafted features. The PR/PEA classifiers were evaluated in terms of sensitivity (Se) for PR, specificity (Sp) for PEA, and the balanced accuracy (BAC), the average of Se and Sp. The Se/Sp/BAC of the DNN architectures were 94.1%/92.9%/93.5% for the first one, and 95.5%/91.6%/93.5% for the second one. Both architectures improved the performance of state of the art methods by more than 1.5 points in BAC. Full article
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Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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