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Biomedical Sensors-Recent Advances and Future Challenges 2022
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Biomedical Sensors-Recent Advances and Future Challenges 2022

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 14816

Special Issue Editors

Prof. Dr. Thomas M. Deserno

E-Mail Website
Guest Editor
Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, 38106 Braunschweig, Germany
Interests: accident and emergency informatics; continuous health monitoring; smart car; smart home; biomedical image and signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yu-Dong Zhang

E-Mail Website
Guest Editor
Informatics Building School of Informatics, University of Leicester, Leicester LE1 7RH, UK
Interests: artificial intelligence; deep learning; medical image processingrecognition; transfer learning; medical image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that the Biomedical Sensors Section is now compiling a collection of papers submitted by the Editorial Board Members (EBMs) of our section and outstanding scholars in this research field. We welcome contributions as well as recommendations from EBMs.

This Special Issue is devoted to original peer-reviewed papers covering all aspects of biomedical sensors. It is focussed on biomedical sensors, including source and detection technologies for the study, treatment, and prevention of various diseases and injuries; biomedical sensor design and fabrication, performance, processing approaches, and applications; new developments and recent improvements in designs; and the electronics, data processing, and materials of biomedical sensors. The articles should address the most recent advances and future perspectives and challenges in biomedical sensors.

In the Special Issue, we would like to publish high-quality manuscripts, particularly review contributions, that demonstrate the advances in biomedical sensors.

Prof. Dr. Thomas Deserno
Dr. Yu-Dong Zhang
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 devices for disease diagnosis and treatment
  • assistive systems/rehabilitation robotics with multi-sensor systems
  • biomedical signals, image processing, and analysis
  • multiplexed biomarker detection
  • point-of-care testing and devices
  • microfluidic and lab-on-a-chip technologies
  • biosensors and biochips in diagnostics and treatment
  • mobile health
  • machine learning and artificial intelligence in biomedical sensors
  • implantable sensors
  • inertial sensors
  • clinical applications: diagnostic, guiding therapy, patient monitoring, disease prevention, and risk assessment
  • sensors in healthcare
  • activity monitoring
  • bioinstrumentation
  • medical sensor development
  • medical applications using iot sensor
  • sensors and telemedicine
  • wearable sensors for sports and health monitoring

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

Published Papers (5 papers)

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Research

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16 pages, 5176 KiB  
Article
Measurement Systems for Use in the Navigation of the Cannula–Guide Assembly within the Deep Regions of the Bronchial Tree
by Tomasz Nabagło, Zbisław Tabor and Piotr Augustyniak
Sensors 2023, 23(4), 2306; https://doi.org/10.3390/s23042306 - 19 Feb 2023
Viewed by 1820
Abstract
Background: The purpose of this paper is to present the spatial navigation system prototype for localizing the distal tip of the cannula–guide assembly. This assembly is shifted through the channel of a bronchoscope, which is fixed in relation to the patient. The navigation [...] Read more.
Background: The purpose of this paper is to present the spatial navigation system prototype for localizing the distal tip of the cannula–guide assembly. This assembly is shifted through the channel of a bronchoscope, which is fixed in relation to the patient. The navigation is carried out in the bronchial tree, based on maneuvers of the aforementioned assembly. Methods: The system consists of three devices mounted on the guide handle and at the entrance to the bronchoscope working channel. The devices record the following values: cannula displacement, rotation of the guide handle, and displacement of the handle ring associated with the bending of the distal tip of the guide. Results: In laboratory experiments, we demonstrate that the cannula displacement can be monitored with an accuracy of 2 mm, and the angles of rotation and bending of the guide tip with an accuracy of 10 and 20 degrees, respectively, which outperforms the accuracy of currently used methods of bronchoscopy support. Conclusions: This accuracy is crucial to ensure that we collect the material for histopathological examination from a precisely defined place. It makes it possible to reach cancer cells at their very early stage. Full article
(This article belongs to the Special Issue Biomedical Sensors-Recent Advances and Future Challenges 2022)
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15 pages, 1906 KiB  
Article
Overestimation of Oxygen Saturation Measured by Pulse Oximetry in Hypoxemia. Part 1: Effect of Optical Pathlengths-Ratio Increase
by Eyal Elron, Ruben Bromiker, Ori Gleisner, Ohad Yosef-Hai, Ori Goldberg, Itamar Nitzan and Meir Nitzan
Sensors 2023, 23(3), 1434; https://doi.org/10.3390/s23031434 - 28 Jan 2023
Cited by 2 | Viewed by 2738
Abstract
On average, arterial oxygen saturation measured by pulse oximetry (SpO2) is higher in hypoxemia than the true oxygen saturation measured invasively (SaO2), thereby increasing the risk of occult hypoxemia. In the current article, measurements of SpO2 on 17 [...] Read more.
On average, arterial oxygen saturation measured by pulse oximetry (SpO2) is higher in hypoxemia than the true oxygen saturation measured invasively (SaO2), thereby increasing the risk of occult hypoxemia. In the current article, measurements of SpO2 on 17 cyanotic newborns were performed by means of a Nellcor pulse oximeter (POx), based on light with two wavelengths in the red and infrared regions (660 and 900 nm), and by means of a novel POx, based on two wavelengths in the infrared region (761 and 820 nm). The SpO2 readings from the two POxs showed higher values than the invasive SaO2 readings, and the disparity increased with decreasing SaO2. SpO2 measured using the two infrared wavelengths showed better correlation with SaO2 than SpO2 measured using the red and infrared wavelengths. After appropriate calibration, the standard deviation of the individual SpO2−SaO2 differences for the two-infrared POx was smaller (3.6%) than that for the red and infrared POx (6.5%, p < 0.05). The overestimation of SpO2 readings in hypoxemia was explained by the increase in hypoxemia of the optical pathlengths-ratio between the two wavelengths. The two-infrared POx can reduce the overestimation of SpO2 measurement in hypoxemia and the consequent risk of occult hypoxemia, owing to its smaller increase in pathlengths-ratio in hypoxemia. Full article
(This article belongs to the Special Issue Biomedical Sensors-Recent Advances and Future Challenges 2022)
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11 pages, 4422 KiB  
Article
Single Camera-Based Dual-Channel Near-Infrared Fluorescence Imaging system
by Janghoon Choi, Jun-Geun Shin, Yoon-Oh Tak, Youngseok Seo and Jonghyun Eom
Sensors 2022, 22(24), 9758; https://doi.org/10.3390/s22249758 - 13 Dec 2022
Cited by 3 | Viewed by 2265
Abstract
In this study, we propose a single camera-based dual-channel near-infrared (NIR) fluorescence imaging system that produces color and dual-channel NIR fluorescence images in real time. To simultaneously acquire color and dual-channel NIR fluorescence images of two fluorescent agents, three cameras and additional optical [...] Read more.
In this study, we propose a single camera-based dual-channel near-infrared (NIR) fluorescence imaging system that produces color and dual-channel NIR fluorescence images in real time. To simultaneously acquire color and dual-channel NIR fluorescence images of two fluorescent agents, three cameras and additional optical parts are generally used. As a result, the volume of the image acquisition unit increases, interfering with movements during surgical procedures and increasing production costs. In the system herein proposed, instead of using three cameras, we set a single camera equipped with two image sensors that can simultaneously acquire color and single-channel NIR fluorescence images, thus reducing the volume of the image acquisition unit. The single-channel NIR fluorescence images were time-divided into two channels by synchronizing the camera and two excitation lasers, and the noise caused by the crosstalk effect between the two fluorescent agents was removed through image processing. To evaluate the performance of the system, experiments were conducted for the two fluorescent agents to measure the sensitivity, crosstalk effect, and signal-to-background ratio. The compactness of the resulting image acquisition unit alleviates the inconvenient movement obstruction of previous devices during clinical and animal surgery and reduces the complexity and costs of the manufacturing process, which may facilitate the dissemination of this type of system. Full article
(This article belongs to the Special Issue Biomedical Sensors-Recent Advances and Future Challenges 2022)
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13 pages, 1756 KiB  
Article
A Novel Application of Deep Learning (Convolutional Neural Network) for Traumatic Spinal Cord Injury Classification Using Automatically Learned Features of EMG Signal
by Farah Masood, Milan Sharma, Davleen Mand, Shanker Nesathurai, Heather A. Simmons, Kevin Brunner, Dane R. Schalk, John B. Sledge and Hussein A. Abdullah
Sensors 2022, 22(21), 8455; https://doi.org/10.3390/s22218455 - 3 Nov 2022
Cited by 2 | Viewed by 2072
Abstract
In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a [...] Read more.
In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesion from five Macaca fasicularis monkeys. The proposed classifier is based on a CNN using filtered segmented EMG signals from the pre- and post-lesion periods as inputs, while the kNN is designed using four hand-crafted EMG features. The results suggest that the CNN provides a promising classification technique for TSCI, compared to conventional machine learning classification. The kNN with hand-crafted EMG features classified the pre- and post-lesion EMG data with an F-measure of 89.7% and 92.7% for the left- and right-side muscles, respectively, while the CNN with the EMG segments classified the data with an F-measure of 89.8% and 96.9% for the left- and right-side muscles, respectively. Finally, the proposed deep learning classification model (CNN), with its learning ability of high-level features using EMG segments as inputs, shows high potential and promising results for use as a TSCI classification system. Future studies can confirm this finding by considering more subjects. Full article
(This article belongs to the Special Issue Biomedical Sensors-Recent Advances and Future Challenges 2022)
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Review

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31 pages, 623 KiB  
Review
Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review
by Roberto Sánchez-Reolid, Francisco López de la Rosa, Daniel Sánchez-Reolid, María T. López and Antonio Fernández-Caballero
Sensors 2022, 22(22), 8886; https://doi.org/10.3390/s22228886 - 17 Nov 2022
Cited by 10 | Viewed by 4047
Abstract
This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected according to various criteria established. The systematic review [...] Read more.
This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected according to various criteria established. The systematic review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, pre-processing, processing and feature extraction. Finally, all ML techniques applied to the features of these signals for arousal classification have been studied. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high-performance values. In contrast, it has been shown that unsupervised learning is not present in the detection of arousal through EDA. This systematic review concludes that the use of EDA for the detection of arousal is widely spread, with particularly good results in classification with the ML methods found. Full article
(This article belongs to the Special Issue Biomedical Sensors-Recent Advances and Future Challenges 2022)
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