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Applied Sciences
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Advances of Biomedical Signal Processing and Control
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Advances of Biomedical Signal Processing and Control

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 9348

Special Issue Editors

Prof. Dr. Seok-Pil Lee

E-Mail Website
Guest Editor
Department of Electronic Engineering, SangMyung University, Seoul 03016, Republic of Korea
Interests: gesture recognition; flexible epidermal tactile sensor array; wearable device; wearable sensor gesture recognition; tactile sensors; Internet; biomechanics; collaborative filtering; computational complexity; data encapsulation; discrete cosine transforms; electroencephalography; electromyography; emotion recognition; extra features
Prof. Dr. Se Dong Min

E-Mail Website
Guest Editor
Department of Medical IT Engineering, Soonchunhyang University, Asan 31538, Republic of Korea
Interests: biomedical signal processing; mobile healthcare; wearable healthcare; smart health; digital therapeutics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past forty years, technology has evolved impressively and led to unimaginable advances in computing power and memory capacity, with significant reductions in size and cost. Despite this technological progress, however, biomedical signal processing and control (theory, methods, and their applications) has only made small steps, while in other fields, such as speech recognition and synthesis, signal processing improvement has been remarkable with an extraordinary spread of applications.

Biomedical signal processing and control have enabled a dynamic area of expertise in both academic and research aspects of biomedical engineering. Biomedical signals include electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), photoplethysmogram (PPG), coughing sound, blood pressure (BP), etc.

In particular, this Special Issue is concerned with signal processing, classification, and interpretation from the information of biomedical signals. Furthermore, it includes biometrics, disease diagnosis, distress analysis, emotion recognition, and various applications based on deep learning or computational intelligence, also including the following fields:

  • Wearable sensing
  • Implantable electronics
  • Pervasive healthcare
  • M-health
  • Bedside monitoring
  • Point-of-care

Prof. Dr. Seok-Pil Lee
Prof. Dr. Se Dong Min
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. Applied Sciences 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

19 pages, 779 KiB  
Article
Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide
by Jose Bernal and Claudia Mazo
Appl. Sci. 2022, 12(20), 10228; https://doi.org/10.3390/app122010228 - 11 Oct 2022
Cited by 9 | Viewed by 5366
Abstract
Although it is widely assumed that Artificial Intelligence (AI) will revolutionise healthcare in the near future, considerable progress must yet be made in order to gain the trust of healthcare professionals and patients. Improving AI transparency is a promising avenue for addressing such [...] Read more.
Although it is widely assumed that Artificial Intelligence (AI) will revolutionise healthcare in the near future, considerable progress must yet be made in order to gain the trust of healthcare professionals and patients. Improving AI transparency is a promising avenue for addressing such trust issues. However, transparency still lacks maturation and definitions. We seek to answer what challenges do experts and professionals in computing and healthcare identify concerning transparency of AI in healthcare? Here, we examine AI transparency in healthcare from five angles: interpretability, privacy, security, equity, and intellectual property. We respond to this question based on recent literature discussing the transparency of AI in healthcare and on an international online survey we sent to professionals working in computing and healthcare and potentially within AI. We collected responses from 40 professionals around the world. Overall, the survey results and current state of the art suggest key problems are a generalised lack of information available to the general public, a lack of understanding of transparency aspects covered in this work, and a lack of involvement of all stakeholders in the development of AI systems. We propose a set of recommendations, the implementation of which can enhance the transparency of AI in healthcare. Full article
(This article belongs to the Special Issue Advances of Biomedical Signal Processing and Control)
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12 pages, 10061 KiB  
Article
COVID-19 Diagnosis from Crowdsourced Cough Sound Data
by Myoung-Jin Son and Seok-Pil Lee
Appl. Sci. 2022, 12(4), 1795; https://doi.org/10.3390/app12041795 - 09 Feb 2022
Cited by 12 | Viewed by 2779
Abstract
The highly contagious and rapidly mutating COVID-19 virus is affecting individuals worldwide. A rapid and large-scale method for COVID-19 testing is needed to prevent infection. Cough testing using AI has been shown to be potentially valuable. In this paper, we propose a COVID-19 [...] Read more.
The highly contagious and rapidly mutating COVID-19 virus is affecting individuals worldwide. A rapid and large-scale method for COVID-19 testing is needed to prevent infection. Cough testing using AI has been shown to be potentially valuable. In this paper, we propose a COVID-19 diagnostic method based on an AI cough test. We used only crowdsourced cough sound data to distinguish between the cough sound of COVID-19-positive people and that of healthy people. First, we used the COUGHVID cough database to segment only the cough sound from the original cough data. An effective audio feature set was then extracted from the segmented cough sounds. A deep learning model was trained on the extracted feature set. The COVID-19 diagnostic system constructed using this method had a sensitivity of 93% and a specificity of 94%, and achieved better results than models trained by other existing methods. Full article
(This article belongs to the Special Issue Advances of Biomedical Signal Processing and Control)
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