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Edge Computing for Biomedical Signal Processing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (1 December 2019) | Viewed by 8020

Special Issue Editors


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Guest Editor
School of Electrical and Data Engineering, University of Technology, Sydney (UTS), Broadway, NSW 2007, Australia
Interests: biomedical engineering; neuromorphic engineering; mixed-signal integrated circuit design; medical devices; machine learning; circuits and systems for implantable and wearable biomedical devices
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Engineering, Design and Built Environment, Western Sydney University, Milperra, NSW 2214, Australia
Interests: biomedical signal processing; wearable and electrode-less physiological monitoring; brain–computer interface; biomedical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, Edge Computing—that is, processing data close to where it is collected rather than on the cloud—has become an important way to deal with the ever-increasing volume of user-generated data. This new paradigm in signal processing comes with many challenges in terms of reducing complexity and processing time while maintaining accuracy and specificity.

In this Issue, we will explore the issues of biomedical signal processing at the edge and seek contributions addressing, though not limited to, the following topics:

  • Biomedical signal processing schemes implemented on wearable or implantable medical devices;
  • Machine learning techniques for biomedical devices;
  • Data security for edge computing;
  • Hardware-aware algorithms; and
  • Unsupervised learning for personalized medicine.

Prof. Tara Julia Hamilton
Dr. Gaetano D. Gargiulo
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. Sensors 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 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

  • edge computing
  • biomedical signal processing
  • intelligent devices
  • wearable and implantable medical devices
  • personalized medicine

Published Papers (1 paper)

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Research

19 pages, 4340 KiB  
Article
Transfer Learning Assisted Classification and Detection of Alzheimer’s Disease Stages Using 3D MRI Scans
by Muazzam Maqsood, Faria Nazir, Umair Khan, Farhan Aadil, Habibullah Jamal, Irfan Mehmood and Oh-young Song
Sensors 2019, 19(11), 2645; https://doi.org/10.3390/s19112645 - 11 Jun 2019
Cited by 132 | Viewed by 7473
Abstract
Alzheimer’s disease effects human brain cells and results in dementia. The gradual deterioration of the brain cells results in disability of performing daily routine tasks. The treatment for this disease is still not mature enough. However, its early diagnosis may allow restraining the [...] Read more.
Alzheimer’s disease effects human brain cells and results in dementia. The gradual deterioration of the brain cells results in disability of performing daily routine tasks. The treatment for this disease is still not mature enough. However, its early diagnosis may allow restraining the spread of disease. For early detection of Alzheimer’s through brain Magnetic Resonance Imaging (MRI), an automated detection and classification system needs to be developed that can detect and classify the subject having dementia. These systems also need not only to classify dementia patients but to also identify the four progressing stages of dementia. The proposed system works on an efficient technique of utilizing transfer learning to classify the images by fine-tuning a pre-trained convolutional network, AlexNet. The architecture is trained and tested over the pre-processed segmented (Grey Matter, White Matter, and Cerebral Spinal Fluid) and un-segmented images for both binary and multi-class classification. The performance of the proposed system is evaluated over Open Access Series of Imaging Studies (OASIS) dataset. The algorithm showed promising results by giving the best overall accuracy of 92.85% for multi-class classification of un-segmented images. Full article
(This article belongs to the Special Issue Edge Computing for Biomedical Signal Processing)
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