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Sensing Signals for Biomedical Monitoring
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Sensing Signals for Biomedical Monitoring

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 1976

Special Issue Editor

Prof. Dr. Carlos M. Travieso-González

E-Mail Website
Guest Editor
Signals and Communications Department, Institute for Technological Development and Innovation in Communications, University of Las Palmas de Gran Canaria, 35001 Las Palmas, Spain
Interests: biometrics; biomedical signals; data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical monitoring is an increasingly important field in modern medical care. Biomedical monitoring sensors detect and record a variety of physiological signals, such as heart rate, blood pressure, and brain activity, and others. These data can provide valuable information for the diagnosis and treatment of diseases. However, the processing and analysis of biomedical monitoring signals can be very complex and requires advanced signal processing and artificial intelligence techniques.

In the context of biomedical monitoring, we invite submissions addressing the theme "Sensing Signals for Biomedical Monitoring". Original research, practice papers, and systematic reviews of biomedical monitoring related to signal processing and analysis, machine learning algorithms, data acquisition systems, and the implementation of biomedical sensors will be accepted.

Some suggested topics for the papers can include:

  • The development of portable and low-cost biomedical monitoring systems.
  • The processing and analysis of biomedical monitoring signals using machine learning techniques.
  • The design and evaluation of biomedical sensors for the monitoring of different physiological signals.

Prof. Dr. Carlos M. Travieso-González
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. 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.

Published Papers (3 papers)

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Research

20 pages, 4929 KiB  
Article
Evaluating Vascular Depth-Dependent Changes in Multi-Wavelength PPG Signals Due to Contact Force
by Joan Lambert Cause, Ángel Solé Morillo, Bruno da Silva, Juan C. García-Naranjo and Johan Stiens
Sensors 2024, 24(9), 2692; https://doi.org/10.3390/s24092692 - 24 Apr 2024
Viewed by 217
Abstract
Photoplethysmography (PPG) is a non-invasive method used for cardiovascular monitoring, with multi-wavelength PPG (MW-PPG) enhancing its efficacy by using multiple wavelengths for improved assessment. This study explores how contact force (CF) variations impact MW-PPG signals. Data from 11 healthy subjects are analyzed to [...] Read more.
Photoplethysmography (PPG) is a non-invasive method used for cardiovascular monitoring, with multi-wavelength PPG (MW-PPG) enhancing its efficacy by using multiple wavelengths for improved assessment. This study explores how contact force (CF) variations impact MW-PPG signals. Data from 11 healthy subjects are analyzed to investigate the still understudied specific effects of CF on PPG signals. The obtained dataset includes simultaneous recording of five PPG wavelengths (470, 525, 590, 631, and 940 nm), CF, skin temperature, and the tonometric measurement derived from CF. The evolution of raw signals and the PPG DC and AC components are analyzed in relation to the increasing and decreasing faces of the CF. Findings reveal individual variability in signal responses related to skin and vasculature properties and demonstrate hysteresis and wavelength-dependent responses to CF changes. Notably, all wavelengths except 631 nm showed that the DC component of PPG signals correlates with CF trends, suggesting the potential use of this component as an indirect CF indicator. However, further validation is needed for practical application. The study underscores the importance of biomechanical properties at the measurement site and inter-individual variability and proposes the arterial pressure wave as a key factor in PPG signal formation. Full article
(This article belongs to the Special Issue Sensing Signals for Biomedical Monitoring)
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12 pages, 2477 KiB  
Article
A Custom-Tailored Multichannel Pressure Monitoring System Designed for Experimental Surgical Model of Abdominal Compartment Syndrome
by Zoltan Attila Godo, Katalin Peto, Klaudia Balog, Adam Deak, Erzsebet Vanyolos, Laszlo Adam Fazekas, Zsolt Szentkereszty and Norbert Nemeth
Sensors 2024, 24(2), 524; https://doi.org/10.3390/s24020524 - 15 Jan 2024
Viewed by 594
Abstract
In experimental medicine, a wide variety of sensory measurements are used. One of these is real-time precision pressure measurement. For comparative studies of the complex pathophysiology and surgical management of abdominal compartment syndrome, a multichannel pressure measurement system is essential. An important aspect [...] Read more.
In experimental medicine, a wide variety of sensory measurements are used. One of these is real-time precision pressure measurement. For comparative studies of the complex pathophysiology and surgical management of abdominal compartment syndrome, a multichannel pressure measurement system is essential. An important aspect is that this multichannel pressure measurement system should be able to monitor the pressure conditions in different tissue layers, and compartments, under different settings. We created a 12-channel positive–negative sensor system for simultaneous detection of pressure conditions in the abdominal cavity, the intestines, and the circulatory system. The same pressure sensor was used with different measurement ranges. In this paper, we describe the device and major experiences, advantages, and disadvantages. The sensory systems are capable of real-time, variable frequency sampling and data collection. It is also important to note that the pressure measurement system should be able to measure pressure with high sensitivity, independently of the filling medium (gas, liquid). The multichannel pressure measurement system we developed was well suited for abdominal compartment syndrome experiments and provided data for optimizing the method of negative pressure wound management. The system is also suitable for direct blood pressure measurement, making it appropriate for use in additional experimental surgical models. Full article
(This article belongs to the Special Issue Sensing Signals for Biomedical Monitoring)
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21 pages, 3864 KiB  
Article
ECG-Free Heartbeat Detection in Seismocardiography and Gyrocardiography Signals Provides Acceptable Heart Rate Variability Indices in Healthy and Pathological Subjects
by Salvatore Parlato, Jessica Centracchio, Daniele Esposito, Paolo Bifulco and Emilio Andreozzi
Sensors 2023, 23(19), 8114; https://doi.org/10.3390/s23198114 - 27 Sep 2023
Cited by 2 | Viewed by 847
Abstract
Cardio-mechanical monitoring techniques, such as Seismocardiography (SCG) and Gyrocardiography (GCG), have received an ever-growing interest in recent years as potential alternatives to Electrocardiography (ECG) for heart rate monitoring. Wearable SCG and GCG devices based on lightweight accelerometers and gyroscopes are particularly appealing for [...] Read more.
Cardio-mechanical monitoring techniques, such as Seismocardiography (SCG) and Gyrocardiography (GCG), have received an ever-growing interest in recent years as potential alternatives to Electrocardiography (ECG) for heart rate monitoring. Wearable SCG and GCG devices based on lightweight accelerometers and gyroscopes are particularly appealing for continuous, long-term monitoring of heart rate and its variability (HRV). Heartbeat detection in cardio-mechanical signals is usually performed with the support of a concurrent ECG lead, which, however, limits their applicability in standalone cardio-mechanical monitoring applications. The complex and variable morphology of SCG and GCG signals makes the ECG-free heartbeat detection task quite challenging; therefore, only a few methods have been proposed. Very recently, a template matching method based on normalized cross-correlation (NCC) has been demonstrated to provide very accurate detection of heartbeats and estimation of inter-beat intervals in SCG and GCG signals of pathological subjects. In this study, the accuracy of HRV indices obtained with this template matching method is evaluated by comparison with ECG. Tests were performed on two public datasets of SCG and GCG signals from healthy and pathological subjects. Linear regression, correlation, and Bland-Altman analyses were carried out to evaluate the agreement of 24 HRV indices obtained from SCG and GCG signals with those obtained from ECG signals, simultaneously acquired from the same subjects. The results of this study show that the NCC-based template matching method allowed estimating HRV indices from SCG and GCG signals of healthy subjects with acceptable accuracy. On healthy subjects, the relative errors on time-domain indices ranged from 0.25% to 15%, on frequency-domain indices ranged from 10% to 20%, and on non-linear indices were within 8%. The estimates obtained on signals from pathological subjects were affected by larger errors. Overall, GCG provided slightly better performances as compared to SCG, both on healthy and pathological subjects. These findings provide, for the first time, clear evidence that monitoring HRV via SCG and GCG sensors without concurrent ECG is feasible with the NCC-based template matching method for heartbeat detection. Full article
(This article belongs to the Special Issue Sensing Signals for Biomedical Monitoring)
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