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Topical Advisory Panel Members' Collection Series: Electronic Sensors for Biological Sensing and Healthcare Monitoring

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

Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 8315

Special Issue Editors

Dr. Insoo Kim

E-Mail Website
Guest Editor
1. Department of Medicine, University of Connecticut School of Medicine, Farmington, CT 06030, USA
2. Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: circuits and systems for biosensors; wearable and attachable sensors for physiological signal monitoring and processing; chronic disease monitoring; connected healthcare system
Special Issues, Collections and Topics in MDPI journals
Dr. George Shaker

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
Interests: antennas & propagation; RF engineering; UAV wireless communications; mm-waves; sensors; energy harvesting systems; biomedical engineering; vehicle and UAV wireless communications; navigation systems; telematics systems
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio Šarolić

E-Mail Website
Guest Editor
FESB, University of Split, 21000 Split, Croatia
Interests: biomedical applications of electromagnetic fields; bioelectronic medicine; bioelectromagnetics; biological and health effects of electromagnetic fields; electronic medical therapeutic and diagnostic devices; electronic medical wearable and implantable devices
Dr. Alessandro Puiatti

E-Mail Website
Guest Editor
Institute of Digital Technologies for Personalized Healthcare, University of Applied Sciences and Arts of Southern Switzerland, 6962 Lugano-Viganello, Switzerland
Interests: wearable sensors; wearable networking; digital health; IoT; EMG; IMU; BCI

Special Issue Information

Dear Colleagues,

Electronic sensors play a crucial role in biological sensing and health monitoring. The real-time monitoring of physiological and behavioral data (heartbeat, respiration, body temperature, gait, etc.) using electronic sensors has provided us with new culture and information for a healthy lifestyle. The Section “Electronic Sensors” is now compiling a collection of feature papers invited by the Topical Advisory Panel Members and advanced results in this research field.

This Special Issue aims to highlight the advances in electronic sensors for biological sensing and healthcare monitoring. Topics include, but are not limited to, the following:

  • Electronic/mobile/wearable/implantable sensors for physiological monitoring;
  • Soft/stretchable electronics;
  • Bio-circuit systems and therapeutic devices;
  • E-skins, prosthetics, surgical/non-surgical robotics;
  • Human measurement for medical applications;
  • Sensor interface, signal processing and data fusion;
  • Remote patient monitoring and home health monitoring.

Dr. Insoo Kim
Dr. George Shaker
Dr. Antonio Šarolić
Dr. Alessandro Puiatti
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

  • electronic sensors
  • wearable electronics
  • eHealth
  • physiological monitoring

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

Published Papers (4 papers)

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Research

27 pages, 6598 KiB  
Article
Fully-Gated Denoising Auto-Encoder for Artifact Reduction in ECG Signals
by Ahmed Shaheen, Liang Ye, Chrishni Karunaratne and Tapio Seppänen
Sensors 2025, 25(3), 801; https://doi.org/10.3390/s25030801 - 29 Jan 2025
Viewed by 905
Abstract
Cardiovascular diseases (CVDs) are the primary cause of death worldwide. For accurate diagnosis of CVDs, robust and efficient ECG denoising is particularly critical in ambulatory cases where various artifacts can degrade the quality of the ECG signal. None of the present denoising methods [...] Read more.
Cardiovascular diseases (CVDs) are the primary cause of death worldwide. For accurate diagnosis of CVDs, robust and efficient ECG denoising is particularly critical in ambulatory cases where various artifacts can degrade the quality of the ECG signal. None of the present denoising methods preserve the morphology of ECG signals adequately for all noise types, especially at high noise levels. This study proposes a novel Fully-Gated Denoising Autoencoder (FGDAE) to significantly reduce the effects of different artifacts on ECG signals. The proposed FGDAE utilizes gating mechanisms in all its layers, including skip connections, and employs Self-organized Operational Neural Network (self-ONN) neurons in its encoder. Furthermore, a multi-component loss function is proposed to learn efficient latent representations of ECG signals and provide reliable denoising with maximal morphological preservation. The proposed model is trained and benchmarked on the QT Database (QTDB), degraded by adding randomly mixed artifacts collected from the MIT-BIH Noise Stress Test Database (NSTDB). The FGDAE showed the best performance on all seven error metrics used in our work in different noise intensities and artifact combinations compared with state-of-the-art algorithms. Moreover, FGDAE provides reliable denoising in extreme conditions and for varied noise compositions. The significantly reduced model size, 61% to 73% reduction, compared with the state-of-the-art algorithm, and the inference speed of the FGDAE model provide evident benefits in various practical applications. While our model performs best compared with other models tested in this study, more improvements are needed for optimal morphological preservation, especially in the presence of electrode motion artifacts. Full article
(This article belongs to the Special Issue Topical Advisory Panel Members' Collection Series: Electronic Sensors for Biological Sensing and Healthcare Monitoring)
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21 pages, 7653 KiB  
Article
The Use of Virtual Tissue Constructs That Include Morphological Variability to Assess the Potential of Electrical Impedance Spectroscopy to Differentiate between Thyroid and Parathyroid Tissues during Surgery
by Malwina Matella, Keith Hunter, Saba Balasubramanian and Dawn Walker
Sensors 2024, 24(7), 2198; https://doi.org/10.3390/s24072198 - 29 Mar 2024
Cited by 2 | Viewed by 1464
Abstract
Electrical impedance spectroscopy (EIS) has been proposed as a promising noninvasive method to differentiate healthy thyroid from parathyroid tissues during thyroidectomy. However, previously reported similarities in the in vivo measured spectra of these tissues during a pilot study suggest that this separation may [...] Read more.
Electrical impedance spectroscopy (EIS) has been proposed as a promising noninvasive method to differentiate healthy thyroid from parathyroid tissues during thyroidectomy. However, previously reported similarities in the in vivo measured spectra of these tissues during a pilot study suggest that this separation may not be straightforward. We utilise computational modelling as a method to elucidate the distinguishing characteristics in the EIS signal and explore the features of the tissue that contribute to the observed electrical behaviour. Firstly, multiscale finite element models (or ‘virtual tissue constructs’) of thyroid and parathyroid tissues were developed and verified against in vivo tissue measurements. A global sensitivity analysis was performed to investigate the impact of physiological micro-, meso- and macroscale tissue morphological features of both tissue types on the computed macroscale EIS spectra and explore the separability of the two tissue types. Our results suggest that the presence of a surface fascia layer could obstruct tissue differentiation, but an analysis of the separability of simulated spectra without the surface fascia layer suggests that differentiation of the two tissue types should be possible if this layer is completely removed by the surgeon. Comprehensive in vivo measurements are required to fully determine the potential for EIS as a method in distinguishing between thyroid and parathyroid tissues. Full article
(This article belongs to the Special Issue Topical Advisory Panel Members' Collection Series: Electronic Sensors for Biological Sensing and Healthcare Monitoring)
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16 pages, 2433 KiB  
Article
Combined Cardiac and Respiratory Monitoring from a Single Signal: A Case Study Employing the Fantasia Database
by Benjamin M. Brandwood, Ganesh R. Naik, Upul Gunawardana and Gaetano D. Gargiulo
Sensors 2023, 23(17), 7401; https://doi.org/10.3390/s23177401 - 25 Aug 2023
Cited by 1 | Viewed by 2922
Abstract
This study proposes a novel method for obtaining the electrocardiogram (ECG) derived respiration (EDR) from a single lead ECG and respiration-derived cardiogram (RDC) from a respiratory stretch sensor. The research aims to reconstruct the respiration waveform, determine the respiration rate from ECG QRS [...] Read more.
This study proposes a novel method for obtaining the electrocardiogram (ECG) derived respiration (EDR) from a single lead ECG and respiration-derived cardiogram (RDC) from a respiratory stretch sensor. The research aims to reconstruct the respiration waveform, determine the respiration rate from ECG QRS heartbeat complexes data, locate heartbeats, and calculate a heart rate (HR) using the respiration signal. The accuracy of both methods will be evaluated by comparing located QRS complexes and inspiration maxima to reference positions. The findings of this study will ultimately contribute to the development of new, more accurate, and efficient methods for identifying heartbeats in respiratory signals, leading to better diagnosis and management of cardiovascular diseases, particularly during sleep where respiration monitoring is paramount to detect apnoea and other respiratory dysfunctions linked to a decreased life quality and known cause of cardiovascular diseases. Additionally, this work could potentially assist in determining the feasibility of using simple, no-contact wearable devices for obtaining simultaneous cardiology and respiratory data from a single device. Full article
(This article belongs to the Special Issue Topical Advisory Panel Members' Collection Series: Electronic Sensors for Biological Sensing and Healthcare Monitoring)
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13 pages, 3150 KiB  
Article
Exploring the Potential of Pulse Transit Time as a Biomarker for Sleep Efficiency through a Comparison Analysis with Heart Rate and Heart Rate Variability
by Jenna Bridges, Hossein Hamidi Shishavan, Adrian Salmon, Mark Metersky and Insoo Kim
Sensors 2023, 23(11), 5112; https://doi.org/10.3390/s23115112 - 27 May 2023
Cited by 4 | Viewed by 2213
Abstract
The relationship between sleep dynamics and blood pressure (BP) changes is well established. Moreover, sleep efficiency and wakefulness during sleep (WASO) events have a significant impact on BP dipping. Despite this knowledge, there is limited research on the measurement of sleep dynamics and [...] Read more.
The relationship between sleep dynamics and blood pressure (BP) changes is well established. Moreover, sleep efficiency and wakefulness during sleep (WASO) events have a significant impact on BP dipping. Despite this knowledge, there is limited research on the measurement of sleep dynamics and continuous blood pressure (CBP). This study aims to explore the relationship between sleep efficiency and cardiovascular function indicators such as pulse transit time (PTT), as a biomarker of CBP, and heart rate variability (HRV), measured using wearable sensors. The results of the study conducted on 20 participants at the UConn Health Sleep Disorders Center suggest a strong linear relationship between sleep efficiency and changes in PTT (r2 = 0.8515) and HRV during sleep (r2 = 5886). The findings of this study contribute to our understanding of the relationship between sleep dynamics, CBP, and cardiovascular health. Full article
(This article belongs to the Special Issue Topical Advisory Panel Members' Collection Series: Electronic Sensors for Biological Sensing and Healthcare Monitoring)
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