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Bioengineering
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Monitoring and Analysis of Human Biosignals, 3rd Edition
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Monitoring and Analysis of Human Biosignals, 3rd Edition

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 555

Special Issue Editor

Dr. Antonio Lanata

E-Mail Website
Guest Editor
Department of Information Engineering, Università degli Studi di Firenze, Firenze, Italy
Interests: wearable system for non-invasive physiological monitoring; statistical and nonlinear biomedical signal processing; affective computing; mood/mental/neurological disorders; human–animal–robot interaction; autonomic nervous system investigation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is the third edition of the previous release of “Monitoring and Analysis of Human Biosignals” (https://www.mdpi.com/journal/bioengineering/special_issues/monitoring_analysis_human_biosignals) and “Monitoring and Analysis of Human Biosignals, 2nd Edition” (https://www.mdpi.com/journal/bioengineering/special_issues/OT70E6A244).

Biosignals are the evidence that biosystems communicate and are our primary source of information on their behavior, playing a pivotal role in health care monitoring and clinical diagnosis. Among the best-known biosignals are the following: ECG, EEG, EMG, EOG, ERG and GSR. Biosignals also refer to non-electrical signals, such as acoustic signals and optical signals. Recent advances in artificial intelligence (AI) and machine learning (ML) make it possible to gather more information on biosignals, and this may lead to a deeper understanding of various pathophysiological states. 

This Special Issue, titled "Monitoring and Analysis of Human Biosignals, 3rd Edition", aims to provide a collection of contributions showing new advancements and applications of biosignal monitoring and analysis. Topics may include, but are not limited to, the following:

  • Biosignal acquisition;
  • Biosignal quality analysis;
  • Biosignal processing and analysis;
  • Deep learning for biosignal analysis;
  • Human body sensing;
  • Biomedical image processing and analysis;
  • Computational neuroscience;
  • Emotion analysis;
  • Speech analysis.

Dr. Antonio Lanata
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • biosignal
  • human body sensing
  • ECG
  • EEG
  • EMG
  • artificial intelligence

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Published Papers (1 paper)

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Research

17 pages, 4587 KiB  
Article
Improving Brain Metabolite Detection with a Combined Low-Rank Approximation and Denoising Diffusion Probabilistic Model Approach
by Yeong-Jae Jeon, Kyung Min Nam, Shin-Eui Park and Hyeon-Man Baek
Bioengineering 2024, 11(11), 1170; https://doi.org/10.3390/bioengineering11111170 - 20 Nov 2024
Viewed by 350
Abstract
In vivo proton magnetic resonance spectroscopy (MRS) is a noninvasive technique for monitoring brain metabolites. However, it is challenged by a low signal-to-noise ratio (SNR), often necessitating extended scan times to compensate. One of the conventional techniques for noise reduction is signal averaging, [...] Read more.
In vivo proton magnetic resonance spectroscopy (MRS) is a noninvasive technique for monitoring brain metabolites. However, it is challenged by a low signal-to-noise ratio (SNR), often necessitating extended scan times to compensate. One of the conventional techniques for noise reduction is signal averaging, which is inherently time-consuming and can lead to participant discomfort, thus posing limitations in clinical settings. This study aimed to develop a hybrid denoising strategy that integrates low-rank approximation and denoising diffusion probabilistic model (DDPM) to enhance MRS data quality and shorten scan times. Using publicly available 1H MRS datasets from 15 subjects, we applied the Casorati SVD and DDPM to obtain baseline and functional data during a pain stimulation task. This method significantly improved SNR, resulting in outcomes comparable to or better than averaging over 32 signals. It also provided the most consistent metabolite measurements and adequately tracked temporal changes in glutamate levels, correlating with pain intensity ratings after heating. These findings demonstrate that our approach enhances MRS data quality, offering a more efficient alternative to conventional methods and expanding the potential for the real-time monitoring of neurochemical changes. This contribution has the potential to advance MRS techniques by integrating advanced denoising methods to increase the acquisition speed and enhance the precision of brain metabolite analyses. Full article
(This article belongs to the Special Issue Monitoring and Analysis of Human Biosignals, 3rd Edition)
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