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Sensors and Signal Processing Techniques for Non-Invasive Health Monitoring
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Sensors and Signal Processing Techniques for Non-Invasive Health Monitoring

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

Deadline for manuscript submissions: 20 July 2025 | Viewed by 2436

Special Issue Editors

Dr. Elisabetta Peri

E-Mail Website
Guest Editor
Electrical Engineering Dept, Eindhoven University of Technology, Eindhoven, The Netherlands
Interests: biomedical signal processing; electrophysiological monitoring; non-invasive patient monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandra Galli

E-Mail Website
Guest Editor
Electrical Engineering Dept, Eindhoven University of Technology, Eindhoven, The Netherlands
Interests: biomedical signal processing; electrophysiological monitoring; non-invasive patient monitoring
Dr. Sarah Tonello

E-Mail Website
Guest Editor
Information Engineering Dept, University of Brescia, Brescia, Italy
Interests: printed sensors; wearable sensors; electrochemical biosensors; electrophysiological monitoring
Dr. Ana Neacșu

E-Mail Website
Guest Editor
Campus Research Institute, National University of Science and Technology POLITEHNICA, Bucharest, Romania
Interests: signal processing; deep learning; machine learning; automatic biomedical analysis
Dr. Maria G. Signorini

E-Mail Website
Guest Editor
Department of Electronic, Information and Bioengineering, Politecnico of Milan, Milan, Italy
Interests: biomedical signal processing; bioengineering

Special Issue Information

Dear Colleagues,

The rapid advancement in non-invasive biomedical diagnostics and monitoring holds the potential to transform healthcare by offering portable and unobtrusive devices that provide real-time, continuous monitoring of human physiological conditions. These innovations pave the way to early detection of diseases, facilitating timely, less invasive interventions, improving patient outcomes, and reducing hospitalization and associated costs.

Robust monitoring requires joint development of sensing technology and signal processing algorithms for extracting, analyzing, and interpreting complex biological signals, including‑but not limited to‑electrocardiograms, electroencephalograms, photoplethysmograms, and other physiological data.

This Special Issue aims to highlight cutting-edge research and encourage innovation in the field of non-invasive biomedical diagnostics and monitoring, by covering a wide spectrum of technological and methodological advances.

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

  • The development of novel sensor technologies;
  • Advanced algorithms for signal denoising, feature extraction, and classification;
  • Integration of machine learning models for diagnostic and monitoring applications;
  • Strategies for minimizing power consumption;
  • Algorithms for real-time processing in wearable and portable devices;
  • Methods for ensuring signal integrity across diverse clinical and home environments;

Both original contributions and reviews focused on the latest achievements of scientific research and emerging techniques are welcome.

Dr. Elisabetta Peri
Dr. Alessandra Galli
Dr. Sarah Tonello
Dr. Ana Neacșu
Dr. Maria G. Signorini
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

  • biomedical diagnostics
  • biomedical signal processing
  • non-invasive monitoring
  • biosensors
  • non-obtrusive devices
  • machine learning for biomedical applications

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

Published Papers (3 papers)

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Research

19 pages, 3002 KiB  
Article
A Novel Method for ECG-Free Heart Sound Segmentation in Patients with Severe Aortic Valve Disease
by Elza Abdessater, Paniz Balali, Jimmy Pawlowski, Jérémy Rabineau, Cyril Tordeur, Vitalie Faoro, Philippe van de Borne and Amin Hossein
Sensors 2025, 25(11), 3360; https://doi.org/10.3390/s25113360 - 27 May 2025
Viewed by 288
Abstract
Severe aortic valve diseases (AVD) cause changes in heart sounds, making phonocardiogram (PCG) analyses challenging. This study presents a novel method for segmenting heart sounds without relying on an electrocardiogram (ECG), specifically targeting patients with severe AVD. Our algorithm enhances traditional Hidden Semi-Markov [...] Read more.
Severe aortic valve diseases (AVD) cause changes in heart sounds, making phonocardiogram (PCG) analyses challenging. This study presents a novel method for segmenting heart sounds without relying on an electrocardiogram (ECG), specifically targeting patients with severe AVD. Our algorithm enhances traditional Hidden Semi-Markov Models by incorporating signal envelope calculations and statistical tests to improve the detection of the first and second heart sounds (S1 and S2). We evaluated the method on the PhysioNet/CinC 2016 Challenge dataset and a newly acquired AVD-specific dataset. The method was tested on a total of 27,400 cardiac cycles. The proposed approach outperformed the existing methods, achieving a higher sensitivity and positive predictive value for S2, especially in the presence of severe heart murmurs. Notably, in patients with severe aortic stenosis, our proposed ECG-free method improved S2 sensitivity from 41% to 70%. Full article
(This article belongs to the Special Issue Sensors and Signal Processing Techniques for Non-Invasive Health Monitoring)
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12 pages, 361 KiB  
Article
Analysis of Electrodermal Signal Features as Indicators of Cognitive and Emotional Reactions—Comparison of the Effectiveness of Selected Statistical Measures
by Marcin Jukiewicz and Joanna Marcinkowska
Sensors 2025, 25(11), 3300; https://doi.org/10.3390/s25113300 - 24 May 2025
Viewed by 351
Abstract
This study investigates which statistical measures of electrodermal activity (EDA) signal features most effectively differentiate between responses to stimuli and resting states in participants performing tasks with varying cognitive and emotional reactions. The study involved 30 healthy participants. Collected EDA data were statistically [...] Read more.
This study investigates which statistical measures of electrodermal activity (EDA) signal features most effectively differentiate between responses to stimuli and resting states in participants performing tasks with varying cognitive and emotional reactions. The study involved 30 healthy participants. Collected EDA data were statistically analyzed, comparing the effectiveness of twelve statistical signal measures in detecting stimulus-induced changes. The aim of this study is to answer the following research question: Which statistical features of the electrodermal activity signal most effectively indicate changes induced by cognitive and emotional reactions, and are there such significant similarities (high correlations) among these features that some of them can be considered redundant? The results indicated that amplitude-related measures—mean, median, maximum, and minimum—were most effective. It was also found that some signal features were highly correlated, suggesting the possibility of simplifying the analysis by choosing just one measure from each correlated pair. The results indicate that stronger emotional stimuli lead to more pronounced changes in EDA than stimuli with a low emotional load. These findings may contribute to the standardization of EDA analysis in future research on cognitive and emotional reaction engagement. Full article
(This article belongs to the Special Issue Sensors and Signal Processing Techniques for Non-Invasive Health Monitoring)
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17 pages, 900 KiB  
Article
Sleep Posture Detection via Embedded Machine Learning on a Reduced Set of Pressure Sensors
by Giacomo Peruzzi, Alessandra Galli, Giada Giorgi and Alessandro Pozzebon
Sensors 2025, 25(2), 458; https://doi.org/10.3390/s25020458 - 14 Jan 2025
Cited by 2 | Viewed by 1325
Abstract
Sleep posture is a key factor in assessing sleep quality, especially for individuals with Obstructive Sleep Apnea (OSA), where the sleeping position directly affects breathing patterns: the side position alleviates symptoms, while the supine position exacerbates them. Accurate detection of sleep posture is [...] Read more.
Sleep posture is a key factor in assessing sleep quality, especially for individuals with Obstructive Sleep Apnea (OSA), where the sleeping position directly affects breathing patterns: the side position alleviates symptoms, while the supine position exacerbates them. Accurate detection of sleep posture is essential in assessing and improving sleep quality. Automatic sleep posture detection systems, both wearable and non-wearable, have been developed to assess sleep quality. However, wearable solutions can be intrusive and affect sleep, while non-wearable systems, such as camera-based approaches and pressure sensor arrays, often face challenges related to privacy, cost, and computational complexity. The system in this paper proposes a microcontroller-based approach exploiting the execution of an embedded machine learning (ML) model for posture classification. By locally processing data from a minimal set of pressure sensors, the system avoids the need to transmit raw data to remote units, making it lightweight and suitable for real-time applications. Our results demonstrate that this approach maintains high classification accuracy (i.e., 0.90 and 0.96 for the configurations with 6 and 15 sensors, respectively) while reducing both hardware and computational requirements. Full article
(This article belongs to the Special Issue Sensors and Signal Processing Techniques for Non-Invasive Health Monitoring)
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