applsci-logo

Journal Browser

Journal Browser

Biological Signal Development for Medical Support

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 341

Special Issue Editor


E-Mail Website
Guest Editor
Division of Science and Technology, Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Tokushima 770-8506, Japan
Interests: biomedical engineering

Special Issue Information

Dear Colleagues,

Currently, we are extending healthy life expectancy through various medical supports. For example, the widespread use of thermometers and blood pressure monitors has led to the development of signal processing techniques and indices for medical support, which are utilized in medical settings. Recently, the advancement of artificial intelligence (AI) has raised expectations for innovative medical support methods, which were difficult to develop with traditional human capabilities.

In this Special Issue, we aim to collate research on all aspects surrounding "Biological Signal Development for Medical Support". Original research articles and reviews are welcome. Research areas may include, but are not limited to, the following:

  • Biological signal processing techniques;
  • Artificial intelligence;
  • Features of medical and biological signals;
  • Health monitoring and management devices.

I look forward to receiving your contributions.

Dr. Takahiro Emoto
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. 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.

Keywords

  • biological signal processing
  • artificial intelligence 
  • medical signals

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 1903 KiB  
Article
Development of a Non-Invasive Clinical Machine Learning System for Arterial Pulse Wave Velocity Estimation
by Arturo Martinez-Rodrigo, João Pedrosa, Davide Carneiro, Iván Cavero-Redondo and Alicia Saz-Lara
Appl. Sci. 2025, 15(9), 4788; https://doi.org/10.3390/app15094788 - 25 Apr 2025
Viewed by 79
Abstract
Arterial stiffness (AS) is a well-established predictor of cardiovascular events, including myocardial infarction and stroke. One of the most recognized methods for assessing AS is through arterial pulse wave velocity (aPWV), which provides valuable clinical insights into vascular health. However, its measurement typically [...] Read more.
Arterial stiffness (AS) is a well-established predictor of cardiovascular events, including myocardial infarction and stroke. One of the most recognized methods for assessing AS is through arterial pulse wave velocity (aPWV), which provides valuable clinical insights into vascular health. However, its measurement typically requires specialized equipment, making it inaccessible in primary healthcare centers and low-resource settings. In this study, we developed and validated different machine learning models to estimate aPWV using common clinical markers routinely collected in standard medical examinations. Thus, we trained five regression models: Linear Regression, Polynomial Regression (PR), Gradient Boosting Regression, Support Vector Regression, and Neural Networks (NNs) on the EVasCu dataset, a cohort of apparently healthy individuals. A 10-fold cross-validation demonstrated that PR and NN achieved the highest predictive performance, effectively capturing nonlinear relationships in the data. External validation on two independent datasets, VascuNET (a healthy population) and ExIC-FEp (a cohort of cardiopathic patients), confirmed the robustness of PR and NN (R2>0.90) across different vascular conditions. These results indicate that by using easily accessible clinical variables and AI-driven insights, it is possible to develop a cost-effective tool for aPWV estimation, enabling early cardiovascular risk stratification in underserved and rural areas where specialized AS measurement devices are unavailable. Full article
(This article belongs to the Special Issue Biological Signal Development for Medical Support)
Show Figures

Figure 1

Back to TopTop