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Biosensors
Special Issues
Biomedical Sensing and Imaging
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Biomedical Sensing and Imaging

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Biosensors and Healthcare".

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 24575

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Wuliang Yin

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, University of Manchester, Manchester M60 1QD, UK
Interests: EM sensing; instruments; NDT; tomography
Special Issues, Collections and Topics in MDPI journals
Dr. Yuedong Xie

E-Mail Website
Guest Editor
School of Instrumentation and Opto-Electronic Engineering, Beihang University, Beijing 100191, China
Interests: non-destructive detection and evaluation; eddy current detection; electromagnetic acoustic detection; ultrasonic detection

Special Issue Information

Dear Colleagues,

Biomedical sensing and imaging are technologies by which biomedical information is acquired and processed, which are of great significance for the early detection, rapid diagnosis and precise treatment of diseases. Biomedical sensing and imaging involve multiple disciplines, including electronic information technology, biomedical technology, artificial intelligence and more. During past years, considerable research efforts have been devoted to biomedical sensing and imaging. Common biomedical sensing technologies, such as electroencephalogram (EEG), electrocardiogram (ECG) and invasive ultrasound (IVUS), have been successfully applied in clinical medicine.

Biosensors convert biomedical signals to electrical signals for further acquisition and processing by downstream devices and algorithms, which are also crucial for rapid and accurate biomedical diagnosis.

More recently, wearable/smart biosensors and devices which facilitate the diagnostics in a non-clinical setting have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field.

It is therefore necessary to solicit recent advances in the above topics on biosensing and imaging, especially in wearable/smart biosensors and advanced imaging algorithms. The aim of this Special Issue is to provide a research forum in biomedical sensing and imaging and to extend the scientific frontier of this very important and significant biomedical endeavor.

Prof. Wuliang Yin
Prof. Yuedong Xie
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. Biosensors is an international peer-reviewed open access monthly 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 2700 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

  • biosensing and bioelectronics
  • biomedical imaging
  • bio-diagnostics
  • artificial intelligence for biomedical applications
  • imaging algorithms related to biomedical applications
  • wearable biosensors and devices

Published Papers (5 papers)

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Research

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16 pages, 8022 KiB  
Article
Simultaneous Imaging of Bio- and Non-Conductive Targets by Combining Frequency and Time Difference Imaging Methods in Electrical Impedance Tomography
by Xue Bai, Dun Liu, Jinzhao Wei, Xu Bai, Shijie Sun and Wenbin Tian
Biosensors 2021, 11(6), 176; https://doi.org/10.3390/bios11060176 - 31 May 2021
Cited by 5 | Viewed by 2540
Abstract
As a promising medical imaging modality, electrical impedance tomography (EIT) can image the electrical properties within a region of interest using electrical measurements applied at electrodes on the region boundary. This paper proposes to combine frequency and time difference imaging methods in EIT [...] Read more.
As a promising medical imaging modality, electrical impedance tomography (EIT) can image the electrical properties within a region of interest using electrical measurements applied at electrodes on the region boundary. This paper proposes to combine frequency and time difference imaging methods in EIT to simultaneously image bio- and non-conductive targets, where the image fusion is accomplished by applying a wavelet-based technique. To enable image fusion, both time and frequency difference imaging methods are investigated regarding the reconstruction of bio- or non-conductive inclusions in the target region at varied excitation frequencies, indicating that none of those two methods can tackle with the scenarios where both bio- and non-conductive inclusions exist. This dilemma can be resolved by fusing the time difference (td) and appropriate frequency difference (fd) EIT images since they are complementary to each other. Through simulation and in vitro experiment, it is demonstrated that the proposed fusion method can reasonably reconstruct both the bio- and non-conductive inclusions within the lung models established to simulate the ventilation process, which is expected to be beneficial for the diagnosis of lung-tissue related diseases by EIT. Full article
(This article belongs to the Special Issue Biomedical Sensing and Imaging)
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15 pages, 3933 KiB  
Article
Development of an Optical Method for the Evaluation of Whole Blood Coagulation
by Marinos Louka and Efstathios Kaliviotis
Biosensors 2021, 11(4), 113; https://doi.org/10.3390/bios11040113 - 09 Apr 2021
Cited by 7 | Viewed by 2721
Abstract
Blood coagulation is a defense mechanism, which is activated in case of blood loss, due to vessel damage, or other injury. Pathological cases arise from malfunctions of the blood coagulation mechanism, and rapid growth of clots results in partially or even fully blocked [...] Read more.
Blood coagulation is a defense mechanism, which is activated in case of blood loss, due to vessel damage, or other injury. Pathological cases arise from malfunctions of the blood coagulation mechanism, and rapid growth of clots results in partially or even fully blocked blood vessel. The aim of this work is to characterize blood coagulation, by analyzing the time-dependent structural properties of whole blood, using an inexpensive design and robust processing approaches. The methods used in this work include brightfield microscopy and image processing techniques, applied on finger-prick blood samples. The blood samples were produced and directly utilized in custom-made glass microchannels. Color images were captured via a microscopy-camera setup for a period of 35 min, utilizing three different magnifications. Statistical information was extracted directly from the color components and the binary conversions of the images. The main advantage in the current work lies on a Boolean classification approach utilized on the binary data, which enabled to identify the interchange between specific structural elements of blood, namely the red blood cells, the plasma and the clotted regions, as a result of the clotting process. Coagulation indices produced included a bulk coagulation index, a plasma-reduction based index and a clot formation index. The results produced with the inexpensive design and the low computational complexity in the current approach, show good agreement with the literature, and a great potential for a robust characterization of blood coagulation. Full article
(This article belongs to the Special Issue Biomedical Sensing and Imaging)
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15 pages, 10490 KiB  
Article
A Novel Efficient FEM Thin Shell Model for Bio-Impedance Analysis
by Jiawei Tang, Mingyang Lu, Yuedong Xie and Wuliang Yin
Biosensors 2020, 10(6), 69; https://doi.org/10.3390/bios10060069 - 17 Jun 2020
Cited by 4 | Viewed by 3339
Abstract
In this paper, a novel method for accelerating eddy currents calculation on a cell model using the finite element method (FEM) is presented. Due to the tiny thickness of cell membrane, a full-mesh cell model requires a large number of mesh elements and [...] Read more.
In this paper, a novel method for accelerating eddy currents calculation on a cell model using the finite element method (FEM) is presented. Due to the tiny thickness of cell membrane, a full-mesh cell model requires a large number of mesh elements and hence intensive computation resources and long time. In this paper, an acceleration method is proposed to reduce the number of mesh elements and therefore reduce the computing time. It is based on the principle of replacing the thin cell membrane with an equivalent thicker structure. The method can reduce the number of mesh elements to 23% and the computational time to 17%, with an error of less than 1%. The method was verified using 2D and 3D finite element methods and can potentially be extended to other thin shell structures. The simulation results were validated by measurement and analytical results. Full article
(This article belongs to the Special Issue Biomedical Sensing and Imaging)
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Review

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35 pages, 8551 KiB  
Review
Biomedical Applications of Electromagnetic Detection: A Brief Review
by Pu Huang, Lijun Xu and Yuedong Xie
Biosensors 2021, 11(7), 225; https://doi.org/10.3390/bios11070225 - 07 Jul 2021
Cited by 15 | Viewed by 5047
Abstract
This paper presents a review on the biomedical applications of electromagnetic detection in recent years. First of all, the thermal, non-thermal, and cumulative thermal effects of electromagnetic field on organism and their biological mechanisms are introduced. According to the electromagnetic biological theory, the [...] Read more.
This paper presents a review on the biomedical applications of electromagnetic detection in recent years. First of all, the thermal, non-thermal, and cumulative thermal effects of electromagnetic field on organism and their biological mechanisms are introduced. According to the electromagnetic biological theory, the main parameters affecting electromagnetic biological effects are frequency and intensity. This review subsequently makes a brief review about the related biomedical application of electromagnetic detection and biosensors using frequency as a clue, such as health monitoring, food preservation, and disease treatment. In addition, electromagnetic detection in combination with machine learning (ML) technology has been used in clinical diagnosis because of its powerful feature extraction capabilities. Therefore, the relevant research involving the application of ML technology to electromagnetic medical images are summarized. Finally, the future development to electromagnetic detection for biomedical applications are presented. Full article
(This article belongs to the Special Issue Biomedical Sensing and Imaging)
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30 pages, 4692 KiB  
Review
EMG-Centered Multisensory Based Technologies for Pattern Recognition in Rehabilitation: State of the Art and Challenges
by Chaoming Fang, Bowei He, Yixuan Wang, Jin Cao and Shuo Gao
Biosensors 2020, 10(8), 85; https://doi.org/10.3390/bios10080085 - 26 Jul 2020
Cited by 75 | Viewed by 9374
Abstract
In the field of rehabilitation, the electromyography (EMG) signal plays an important role in interpreting patients’ intentions and physical conditions. Nevertheless, utilizing merely the EMG signal suffers from difficulty in recognizing slight body movements, and the detection accuracy is strongly influenced by environmental [...] Read more.
In the field of rehabilitation, the electromyography (EMG) signal plays an important role in interpreting patients’ intentions and physical conditions. Nevertheless, utilizing merely the EMG signal suffers from difficulty in recognizing slight body movements, and the detection accuracy is strongly influenced by environmental factors. To address the above issues, multisensory integration-based EMG pattern recognition (PR) techniques have been developed in recent years, and fruitful results have been demonstrated in diverse rehabilitation scenarios, such as achieving high locomotion detection and prosthesis control accuracy. Owing to the importance and rapid development of the EMG centered multisensory fusion technologies in rehabilitation, this paper reviews both theories and applications in this emerging field. The principle of EMG signal generation and the current pattern recognition process are explained in detail, including signal preprocessing, feature extraction, classification algorithms, etc. Mechanisms of collaborations between two important multisensory fusion strategies (kinetic and kinematics) and EMG information are thoroughly explained; corresponding applications are studied, and the pros and cons are discussed. Finally, the main challenges in EMG centered multisensory pattern recognition are discussed, and a future research direction of this area is prospected. Full article
(This article belongs to the Special Issue Biomedical Sensing and Imaging)
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