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Biosensor Development and Innovation in Healthcare and Medical Applications

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

Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 27723

Special Issue Editors

Biological Sciences/Biomedical Engineering, Louisiana Tech University, 305 Wisteria Street, Ruston, LA 71272, USA
Interests: biosensors; device design; 3D printing; medical implants; personalized medicine; regnerative medicine; tissue engineering
School of Biological Sciences/Molecular Sciences and Nanotechnology, Louisiana Tech University, Ruston, LA, USA
Interests: microfluidics; thermoelectric sensors; 3D printing

Special Issue Information

Dear Colleagues,

This Special Issue will focus on recent biosensor development and innovation in healthcare and medical applications. Many devices being developed today are focused on tracking clinical information. We have seen a number of FDA approvals for watches that capture blood pressure and electrocardiogram readings. The demand for diagnostic, monitoring, and measuring sensors that have clinical applications will continue to grow. As these devices transition from lifestyle gadgets to sophisticated medical reporting tools, there will be many challenges. The near future will see the development of diagnostic systems that will be able to help predict and stop the spread of infectious disease. Molecular-level sensor detection networks that provide real-time monitoring of the agricultural, animal, environmental, and health fields will radically improve global healthcare. Microphysiological analysis platforms (MAP) that predict the most effective treatment of diseases for each patient using stem cell-derived human induced pluripotent stem cells (iPSCs)-based organoids MAP will provide an ideal model to address fundamental questions of development and disease pathogenesis. They will provide a major stimulus leading to new proactive, predictive, and preventive medical devices. Diagnostics will become tightly integrated into treatment strategies. This Special Issue welcomes the submission of manuscripts in these areas and also welcomes submissions that explore device accuracy, security, interoperability, patient compliance, and physician adoption. This Special Issue will help readers to learn about the underlying sensor technologies and emerging applications in their field. Areas of interest targeted by this Special Issue include connected health, next-generation wearables, and personalized care.

Dr. David Mills
Dr. Gergana G. Nestorova
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

  • Microphysiological analysis platforms
  • Medical reporting
  • Sensor detection networks
  • Real-time patient monitoring
  • Biosensor networks

Published Papers (8 papers)

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Editorial

Jump to: Research, Review

4 pages, 180 KiB  
Editorial
Biosensor Development and Innovation in Healthcare and Medical Applications
by David K. Mills and Gergana G. Nestorova
Sensors 2023, 23(5), 2717; https://0-doi-org.brum.beds.ac.uk/10.3390/s23052717 - 02 Mar 2023
Cited by 1 | Viewed by 1269
Abstract
The pandemic necessitated a change to the historical diagnostics model [...] Full article

Research

Jump to: Editorial, Review

16 pages, 2944 KiB  
Communication
Hydration Assessment Using the Bio-Impedance Analysis Method
by Reem AlDisi, Qamar Bader and Amine Bermak
Sensors 2022, 22(17), 6350; https://0-doi-org.brum.beds.ac.uk/10.3390/s22176350 - 24 Aug 2022
Cited by 7 | Viewed by 3507
Abstract
Body hydration is considered one of the most important physiological parameters to measure and one of the most challenging. Current methods to assess hydration are invasive and require costly clinical settings. The bio-impedance analysis offers a noninvasive and inexpensive tool to assess hydration, [...] Read more.
Body hydration is considered one of the most important physiological parameters to measure and one of the most challenging. Current methods to assess hydration are invasive and require costly clinical settings. The bio-impedance analysis offers a noninvasive and inexpensive tool to assess hydration, and it can be designed to be used in wearable health devices. The use of wearable electronics in healthcare applications has received increased attention over the last decade. New, emerging medical devices feature continuous patient monitoring and data collection to provide suitable treatment and preventive actions. In this paper, a model of human skin is developed and simulated to be used as a guide to designing a dehydration monitoring system based on a bio-impedance analysis technique. The study investigates the effect of applying different frequencies on the dielectric parameters of the skin and the resulting measured impedance. Two different interdigitated electrode designs are presented, and a comparison of the measurements is presented. The rectangular IDE is printed and tested on subjects to validate the bio-impedance method and study the interpretation of its results. The proposed design offers a classification criterion that can be used to assess dehydration without the need for a complex mathematical model. Further clinical testing and data are needed to refine and finalize the criteria. Full article
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16 pages, 1359 KiB  
Article
DE-PNN: Differential Evolution-Based Feature Optimization with Probabilistic Neural Network for Imbalanced Arrhythmia Classification
by Amnah Nasim and Yoon Sang Kim
Sensors 2022, 22(12), 4450; https://0-doi-org.brum.beds.ac.uk/10.3390/s22124450 - 12 Jun 2022
Cited by 3 | Viewed by 1650
Abstract
In this research, a heartbeat classification method is presented based on evolutionary feature optimization using differential evolution (DE) and classification using a probabilistic neural network (PNN) to discriminate between normal and arrhythmic heartbeats. The proposed method follows four steps: (1) preprocessing, (2) heartbeat [...] Read more.
In this research, a heartbeat classification method is presented based on evolutionary feature optimization using differential evolution (DE) and classification using a probabilistic neural network (PNN) to discriminate between normal and arrhythmic heartbeats. The proposed method follows four steps: (1) preprocessing, (2) heartbeat segmentation, (3) DE feature optimization, and (4) PNN classification. In this method, we have employed direct signal amplitude points constituting the heartbeat acquired from the ECG holter device with no secondary feature extraction step usually used in case of hand-crafted, frequency transformation or other features. The heartbeat types include normal, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature, ventricular escape, ventricular flutter and paced beat. Using ECG records from the MIT-BIH, heartbeats are identified to start at 250 ms before and end at 450 ms after the respective R-peak positions. In the next step, the DE method is applied to reduce and optimize the direct heartbeat features. Although complex and highly computational ECG heartbeat classification algorithms have been proposed in the literature, they failed to achieve high performance in detecting some minority heartbeat categories, especially for imbalanced datasets. To overcome this challenge, we propose an optimization step for the deep CNN model using a novel classification metric called the Matthews correlation coefficient (MCC). This function focuses on arrhythmia (minority) heartbeat classes by increasing their importance. Maximum MCC is used as a fitness function to identify the optimum combination of features for the uncorrelated and non-uniformly distributed eight beat class samples. The proposed DE-PNN scheme can provide better classification accuracy considering 8 classes with only 36 features optimized from a 253 element feature set implying an 85.77% reduction in direct amplitude features. Our proposed method achieved overall 99.33% accuracy, 94.56% F1, 93.84% sensitivity, and 99.21% specificity. Full article
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11 pages, 5445 KiB  
Communication
Wearable E-Textile and CNT Sensor Wireless Measurement System for Real-Time Penile Erection Monitoring
by Yongki Heo, Jinhyung Kim, Cheolung Cha, Kyusik Shin, Jihyoung Roh and Jungki Jo
Sensors 2022, 22(1), 231; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010231 - 29 Dec 2021
Cited by 4 | Viewed by 3251
Abstract
Erection measurements are the most important indicator of male urological disease diagnosis, treatment, and results. Rigiscan has been used widely in studies and diagnoses for nocturnal penile tumescence for evaluating erectile dysfunction by measuring the number and timing of erectile dysfunctions during sleep. [...] Read more.
Erection measurements are the most important indicator of male urological disease diagnosis, treatment, and results. Rigiscan has been used widely in studies and diagnoses for nocturnal penile tumescence for evaluating erectile dysfunction by measuring the number and timing of erectile dysfunctions during sleep. However, this device has limitations such as the weight and bulk of the device and has been questioned for its role as a standard for ED Erectile Dysfunction (ED) diagnosis. In this study, we propose a real-time wearable monitoring system that can quantitatively measure the length and circumference of the penis using electronic textiles (E-textile) and carbon nanotube (CNT) sensors. The E-textile sensor is used to measure the length, circumference, and gradient with portability, convenience, and comfort. Sensors were created by coating CNTs on latex for flexibility. The CNT-based latex condom-type sensor in our proposed system shows the length, circumference, and curvature measurements with changes in resistance, and the E-textile performance shows a 1.44% error rate and a cavity radius of 110 to 300. The results of this conceptual study are for supplementary sensor development with a combination of new technologies with alternatives or existing methods for measuring erection function. Full article
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13 pages, 3447 KiB  
Article
Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification
by Jayro Martínez-Cerveró, Majid Khalili Ardali, Andres Jaramillo-Gonzalez, Shizhe Wu, Alessandro Tonin, Niels Birbaumer and Ujwal Chaudhary
Sensors 2020, 20(9), 2443; https://0-doi-org.brum.beds.ac.uk/10.3390/s20092443 - 25 Apr 2020
Cited by 16 | Viewed by 4244
Abstract
Electrooculography (EOG) signals have been widely used in Human-Computer Interfaces (HCI). The HCI systems proposed in the literature make use of self-designed or closed environments, which restrict the number of potential users and applications. Here, we present a system for classifying four directions [...] Read more.
Electrooculography (EOG) signals have been widely used in Human-Computer Interfaces (HCI). The HCI systems proposed in the literature make use of self-designed or closed environments, which restrict the number of potential users and applications. Here, we present a system for classifying four directions of eye movements employing EOG signals. The system is based on open source ecosystems, the Raspberry Pi single-board computer, the OpenBCI biosignal acquisition device, and an open-source python library. The designed system provides a cheap, compact, and easy to carry system that can be replicated or modified. We used Maximum, Minimum, and Median trial values as features to create a Support Vector Machine (SVM) classifier. A mean of 90% accuracy was obtained from 7 out of 10 subjects for online classification of Up, Down, Left, and Right movements. This classification system can be used as an input for an HCI, i.e., for assisted communication in paralyzed people. Full article
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9 pages, 1529 KiB  
Article
Parametric Study of Bolt Clamping Effect on Resonance Characteristics of Langevin Transducers with Lumped Circuit Models
by Jinhyuk Kim and Jungwoo Lee
Sensors 2020, 20(7), 1952; https://0-doi-org.brum.beds.ac.uk/10.3390/s20071952 - 31 Mar 2020
Cited by 16 | Viewed by 4015
Abstract
We recently proposed a numerical model using equivalent circuit models to analyze the resonance characteristics of Langevin transducers and design them in a systematic manner. However, no pre-load torque biased by a metal bolt was considered in the model. Here, a parametric study [...] Read more.
We recently proposed a numerical model using equivalent circuit models to analyze the resonance characteristics of Langevin transducers and design them in a systematic manner. However, no pre-load torque biased by a metal bolt was considered in the model. Here, a parametric study is, therefore, carried out to reveal how model parameters are adapted to incorporate the pre-compression effect into our existing model. Analytical results are compared with corresponding experimental data, particularly regarding the input electrical impedance and effective electromechanical coupling coefficient for the transducer at resonance modes. The frequency response of input impedance is presented as a function of torque, both theoretically and experimentally. For 10.0 N·m bias, for instance, both resonance and anti-resonance frequencies are calculated as 38.64 kHz and 39.78 kHz, while these are measured as 38.62 kHz and 39.77 kHz by the impedance analyzer. The impedance difference between these cases is 14 Ω at resonance and 9 kΩ at anti-resonance, while the coupling coefficients in both cases become 0.238 and 0.239, respectively. Hence, these test results are closely matched with their theoretical values. Consequently, this study provides a quantitative guideline that specifies the pre-loading condition of bolt clamps with proper parameter settings to predict the intended resonance characteristics of Langevin transducers. Full article
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Review

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16 pages, 2712 KiB  
Review
Advances in Biosensors Technology for Detection and Characterization of Extracellular Vesicles
by Saif Mohammad Ishraq Bari, Faria Binte Hossain and Gergana G. Nestorova
Sensors 2021, 21(22), 7645; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227645 - 17 Nov 2021
Cited by 12 | Viewed by 3237
Abstract
Exosomes are extracellular vehicles (EVs) that encapsulate genomic and proteomic material from the cell of origin that can be used as biomarkers for non-invasive disease diagnostics in point of care settings. The efficient and accurate detection, quantification, and molecular profiling of exosomes are [...] Read more.
Exosomes are extracellular vehicles (EVs) that encapsulate genomic and proteomic material from the cell of origin that can be used as biomarkers for non-invasive disease diagnostics in point of care settings. The efficient and accurate detection, quantification, and molecular profiling of exosomes are crucial for the accurate identification of disease biomarkers. Conventional isolation methods, while well-established, provide the co-purification of proteins and other types of EVs. Exosome purification, characterization, and OMICS analysis are performed separately, which increases the complexity, duration, and cost of the process. Due to these constraints, the point-of-care and personalized analysis of exosomes are limited in clinical settings. Lab-on-a-chip biosensing has enabled the integration of isolation and characterization processes in a single platform. The presented review discusses recent advancements in biosensing technology for the separation and detection of exosomes. Fluorescent, colorimetric, electrochemical, magnetic, and surface plasmon resonance technologies have been developed for the quantification of exosomes in biological fluids. Size-exclusion filtration, immunoaffinity, electroactive, and acoustic-fluid-based technologies were successfully applied for the on-chip isolation of exosomes. The advancement of biosensing technology for the detection of exosomes provides better sensitivity and a reduced signal-to-noise ratio. The key challenge for the integration of clinical settings remains the lack of capabilities for on-chip genomic and proteomic analysis. Full article
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23 pages, 687 KiB  
Review
Situation Awareness-Oriented Patient Monitoring with Visual Patient Technology: A Qualitative Review of the Primary Research
by David Werner Tscholl, Julian Rössler, Sadiq Said, Alexander Kaserer, Donat Rudolf Spahn and Christoph Beat Nöthiger
Sensors 2020, 20(7), 2112; https://0-doi-org.brum.beds.ac.uk/10.3390/s20072112 - 09 Apr 2020
Cited by 25 | Viewed by 5577
Abstract
Visual Patient technology is a situation awareness-oriented visualization technology that translates numerical and waveform patient monitoring data into a new user-centered visual language. Vital sign values are converted into colors, shapes, and rhythmic movements—a language humans can easily perceive and interpret—on a patient [...] Read more.
Visual Patient technology is a situation awareness-oriented visualization technology that translates numerical and waveform patient monitoring data into a new user-centered visual language. Vital sign values are converted into colors, shapes, and rhythmic movements—a language humans can easily perceive and interpret—on a patient avatar model in real time. In this review, we summarize the current state of the research on the Visual Patient, including the technology, its history, and its scientific context. We also provide a summary of our primary research and a brief overview of research work on similar user-centered visualizations in medicine. In several computer-based studies under various experimental conditions, Visual Patient transferred more information per unit time, increased perceived diagnostic certainty, and lowered perceived workload. Eye tracking showed the technology worked because of the way it synthesizes and transforms vital sign information into new and logical forms corresponding to the real phenomena. The technology could be particularly useful for improving situation awareness in settings with high cognitive demand or when users must make quick decisions. This comprehensive review of Visual Patient research is the foundation for an evaluation of the technology in clinical applications, starting with a high-fidelity simulation study in early 2020. Full article
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