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Diagnostics
Special Issues
Deep Learning Applications in Healthcare Wearable Devices
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Deep Learning Applications in Healthcare Wearable Devices

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Point-of-Care Diagnostics and Devices".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 1004

Special Issue Editor

Prof. Dr. Mamun Bin Ibne Reaz

E-Mail Website
Guest Editor
Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Interests: SoC design (AI accelerator); biomedical sensor; AI for biomedical applications

Special Issue Information

Dear Colleagues,

In recent years, wearable devices have been widely used in the field of healthcare. They can detect critical healthcare data such as body temperature, heart rate, respiration, EEG, ECG, EMG, and electrodermal activity in a non-invasive, automatic, and continuous way in helping with daily health monitoring and disease diagnosis. The collected data can be processed and analyzed with the help of artificial intelligence, improving the efficiency of patient monitoring and reducing the burden on the patient care system.

Therefore, this Special Issue on “Deep Learning Applications in Healthcare Wearable Devices” aims to highlight the application of artificial intelligence approaches such as deep learning and machine learning in healthcare wearable devices to help to detect, evaluate, and analyze patient health data. Researchers, scientists, and medical professionals are welcome to submit your works to Diagnostics. Both original articles and reviews will be considered.

Topics of interest include but are not limited to the following aspects:

  • Design and development of smart wearable devices;
  • Challenges of wearable devices in healthcare data acquisition, data transmission, and more;
  • Development of mobile applications for wearable devices and AI;
  • Data analysis methods for wearable devices;
  • Prospects for the application of artificial intelligence methods in wearable devices.

Prof. Dr. Mamun Bin Ibne Reaz
Guest Editor

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. Diagnostics 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

  • wearable devices
  • deep learning
  • machine learning
  • portable devices
  • health monitoring

Published Papers (1 paper)

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Research

14 pages, 1010 KiB  
Article
Transfer Learning for Automatic Sleep Staging Using a Pre-Gelled Electrode Grid
by Fabian A. Radke, Carlos F. da Silva Souto, Wiebke Pätzold and Karen Insa Wolf
Diagnostics 2024, 14(9), 909; https://doi.org/10.3390/diagnostics14090909 - 26 Apr 2024
Viewed by 268
Abstract
Novel sensor solutions for sleep monitoring at home could alleviate bottlenecks in sleep medical care as well as enable selective or continuous observation over long periods of time and contribute to new insights in sleep medicine and beyond. Since especially in the latter [...] Read more.
Novel sensor solutions for sleep monitoring at home could alleviate bottlenecks in sleep medical care as well as enable selective or continuous observation over long periods of time and contribute to new insights in sleep medicine and beyond. Since especially in the latter case the sensor data differ strongly in signal, number and extent of sensors from the classical polysomnography (PSG) sensor technology, an automatic evaluation is essential for the application. However, the training of an automatic algorithm is complicated by the fact that the development phase of the new sensor technology, extensive comparative measurements with standardized reference systems, is often not possible and therefore only small datasets are available. In order to circumvent high system-specific training data requirements, we employ pre-training on large datasets with finetuning on small datasets of new sensor technology to enable automatic sleep phase detection for small test series. By pre-training on publicly available PSG datasets and finetuning on 12 nights recorded with new sensor technology based on a pre-gelled electrode grid to capture electroencephalography (EEG), electrooculography (EOG) and electromyography (EMG), an F1 score across all sleep phases of 0.81 is achieved (wake 0.84, N1 0.62, N2 0.81, N3 0.87, REM 0.88), using only EEG and EOG. The analysis additionally considers the spatial distribution of the channels and an approach to approximate classical electrode positions based on specific linear combinations of the new sensor grid channels. Full article
(This article belongs to the Special Issue Deep Learning Applications in Healthcare Wearable Devices)
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