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Electronics
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Intelligent Systems for Healthcare and Biomedical Engineering
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Intelligent Systems for Healthcare and Biomedical Engineering

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 5955

Special Issue Editors

Dr. Daniel Alshamaa

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Guest Editor
LM2S, University of Technology of Troyes, Troyes, France
Interests: intelligent sensor systems; statistical signal processing; machine learning; multisensor data fusion; 3D reconstruction; biomedical engineering; healthcare institutions crisis management
Dr. Farah Mourad-Chehade

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Guest Editor
LIST3N-Laboratoire d'Informatique et Société Numérique, Université de Technologie de Troyes (UTT), 10004 Troyes, France
Interests: artificial intelligence; data mining; pattern recognition; wireless sensor networks; sensors localization; biomedical signal processing; prediction and diagnoses of pathologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The sanitary crisis we are currently passing through illustrates the importance of research in the domains of healthcare and biomedical engineering. The advances in the fields of artificial intelligence, signal and image processing, and data handling capabilities have enabled the development of powerful intelligent systems demonstrating great utility in these domains.

Intelligent systems take a wide variety of forms, such as wearable/non-wearable sensors and devices, smart algorithms to extract health-related information from signals and images, decision-based algorithms to help healthcare institutions in crisis management through resources optimization, among others.   

The aim of this Special Issue is to collect original papers covering unpublished research from academic and industrial actors. The fields of interest to this Special Issue are theoretical and practical advances of intelligent systems in the healthcare sector, ranging from wearable sensors and devices to decision-based systems and healthcare institutions crisis management.

Topics of interest of this Special Issue include, but are not limited to:

  • Wearable/non-wearable sensors and devices;
  • Biomedical signal and image processing;
  • Design and development of intelligent systems for healthcare;
  • Multisensor data fusion for decision-based systems;
  • Artificial intelligence and machine learning for connected health;
  • Intelligent systems for healthcare crisis management;
  • Healthcare resources optimization.

Dr. Daniel Alshamaa
Dr. Farah Mourad-Chehade
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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 (2 papers)

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Research

13 pages, 8150 KiB  
Article
VGG-C Transform Model with Batch Normalization to Predict Alzheimer’s Disease through MRI Dataset
by Batzaya Tuvshinjargal and Heejoung Hwang
Electronics 2022, 11(16), 2601; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11162601 - 19 Aug 2022
Cited by 3 | Viewed by 1930
Abstract
Alzheimer’s disease is the most common cause of dementia and is a generic term for memory and other cognitive abilities that are severe enough to interfere with daily life. In this paper, we propose an improved prediction method for Alzheimer’s disease using a [...] Read more.
Alzheimer’s disease is the most common cause of dementia and is a generic term for memory and other cognitive abilities that are severe enough to interfere with daily life. In this paper, we propose an improved prediction method for Alzheimer’s disease using a quantization method that transforms the MRI data set using a VGG-C Transform model and a convolutional neural network (CNN) consisting of batch normalization. MRI image data of Alzheimer’s disease are not fully disclosed to general research because it is data from real patients. So, we had to find a solution that could maximize the core functionality in a limited image. In other words, since it is necessary to adjust the interval, which is an important feature of MRI color information, rather than expressing the brain shape, the brain texture dataset was modified in the quantized pixel intensity method. We also use the VGG family, where the VGG-C Transform model with bundle normalization added to the VGG-C model performed the best with a test accuracy of about 0.9800. However, since MRI images are 208 × 176 pixels, conversion to 224 × 224 pixels may result in distortion and loss of pixel information. To address this, the proposed VGG model-based architecture can be trained while maintaining the original MRI size. As a result, we were able to obtain a prediction accuracy of 98% and the AUC score increased by up to 1.19%, compared to the normal MRI image data set. It is expected that our study will be helpful in predicting Alzheimer’s disease using the MRI dataset. Full article
(This article belongs to the Special Issue Intelligent Systems for Healthcare and Biomedical Engineering)
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27 pages, 2727 KiB  
Article
An Extended Semantic Interoperability Model for Distributed Electronic Health Record Based on Fuzzy Ontology Semantics
by Ebtsam Adel, Shaker El-Sappagh, Sherif Barakat, Jong-Wan Hu and Mohammed Elmogy
Electronics 2021, 10(14), 1733; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10141733 - 19 Jul 2021
Cited by 11 | Viewed by 2803
Abstract
Semantic interoperability of distributed electronic health record (EHR) systems is a crucial problem for querying EHR and machine learning projects. The main contribution of this paper is to propose and implement a fuzzy ontology-based semantic interoperability framework for distributed EHR systems. First, a [...] Read more.
Semantic interoperability of distributed electronic health record (EHR) systems is a crucial problem for querying EHR and machine learning projects. The main contribution of this paper is to propose and implement a fuzzy ontology-based semantic interoperability framework for distributed EHR systems. First, a separate standard ontology is created for each input source. Second, a unified ontology is created that merges the previously created ontologies. However, this crisp ontology is not able to answer vague or uncertain queries. We thirdly extend the integrated crisp ontology into a fuzzy ontology by using a standard methodology and fuzzy logic to handle this limitation. The used dataset includes identified data of 100 patients. The resulting fuzzy ontology includes 27 class, 58 properties, 43 fuzzy data types, 451 instances, 8376 axioms, 5232 logical axioms, 1216 declarative axioms, 113 annotation axioms, and 3204 data property assertions. The resulting ontology is tested using real data from the MIMIC-III intensive care unit dataset and real archetypes from openEHR. This fuzzy ontology-based system helps physicians accurately query any required data about patients from distributed locations using near-natural language queries. Domain specialists validated the accuracy and correctness of the obtained results. Full article
(This article belongs to the Special Issue Intelligent Systems for Healthcare and Biomedical Engineering)
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