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Special Issue "Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing"

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

Deadline for manuscript submissions: 30 September 2020.

Special Issue Editors

Dr. Jan Kubicek
Website
Guest Editor
VSB-Technical University of Ostrava, Czech Republic
Interests: Signal and image processing, data computational analysis and applied artificial intelligence
Prof. Dr. Marek Penhaker
Website
Guest Editor
Researcher, Department of Cybernetics and Biomedical Engineering, VŠB-Technical University of Ostrava, 17. listopadu 15, 70833 Ostrava-Poruba, Czech Republic.
Interests: biomedical engineering, biomedical sensors, biomedical signal and image processing
Prof. Dr. Ondrej Krejcar
Website
Guest Editor
Faculty of Informatics and Management, Center for Basic and Applied Research, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove, 50003, Czech Republic
Interests: control systems; smart sensors; ubiquitous computing; manufacturing; wireless technology; portable devices; biomedicine; image segmentation and recognition; biometrics; technical cybernetics; ubiquitous computing
Special Issues and Collections in MDPI journals
Prof. Dr. Ali Selamat
Website
Guest Editor
Universiti Teknologi Malaysia (UTM), UTM Kuala Lumpur, Malaysia
Interests: cloud based software engineering; software agents; information retrievals; pattern recognition; genetic algorithms; neural networks; soft computing; knowledge management; key performance indicators
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In the modern digital age, computer systems, including hardware sensors and software intelligent components, play an essential role in the area of biomedical engineering. This area is surrounded by various systems, producing data about the state and therapy of the living systems. Such sensoric systems provide clinical information in the form of biomedical signals and images which are further processed. In order to provide proper clinical information, we need to employ modern intelligent methods for processing and extracting clinical information, reporting the state of analyzed tissues. The development of intelligent and precise sensors predetermines obtaining properly clinically valuable information. Such information, nevertheless, contains additive components, reflecting noise and artefacts. Therefore, modern software trends are aimed at employing intelligent methods which are capable of identifying clinical important information, while other components are removed. Such intelligent systems comprise the basis for clinical decision-making systems, allowing for a feedback to the clinical specialist. Such feedback carries clinically valuable information in an objective way, instead of just subjective clinical opinions. In order to tackle such challenges, biomedical sensors and intelligent systems should be continuously updated by means of new perspective powerful methods and architectures with the goal to achieve and maintain a high level of applicability in various biomedical domains. This Special Issue is dedicated to the dissemination of recent advances and novel methods in the area of the biomedical sensors, modern trends of measuring biomedical information, and intelligent algorithms, mostly including the elements of the artificial intelligence, representing autonomous decision-making systems for supportive diagnosis and providing therapy. We invite all researchers and practitioners from the field of the biomedical engineering and related areas to contribute original research papers, reporting new advances in this field, as well as review papers, summarizing research literature. Topic of this Special Issue include but are not limited to the following areas:

  • medical imaging technologies and methods for health care;
  • decision support systems, intelligent and recommendation systems;
  • biomedical signal analysis and processing;
  • biomedical image processing and machine vision;
  • intelligent healthcare systems;
  • medical sensors technology;
  • genetic algorithms and programming;
  • machine learning and knowledge discovery;
  • medical robotics, intelligent medical devices, and smart technologies;
  • bioinformatics and biosystems;
  • fuzzy and expert systems in biomedicine;
  • artificial neural networks in biomedicine;
  • biomedical data modeling and classification;
  • 3D printing in biomedicine;
  • advanced computing and cloud computing in biomedicine;
  • chemo informatics and computational chemistry;
  • Use of IT for drug discovery;
  • Artificial Intelligence for medical information systems;
  • hardware in the healthcare industry;
  • biomedical sensors and applications

Dr. Jan Kubicek
Dr. Marek Penhaker
Prof. Dr. Ondrej Krejcar
Prof. Dr. Ali Selamat
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 papers will be 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 2000 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 engineering
  • biomedical sensors
  • transducers
  • artificial intelligence
  • biomedical signal and image processing

Published Papers (5 papers)

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Research

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Open AccessArticle
An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram
Sensors 2020, 20(13), 3757; https://doi.org/10.3390/s20133757 - 04 Jul 2020
Abstract
The invasive method of fetal electrocardiogram (fECG) monitoring is widely used with electrodes directly attached to the fetal scalp. There are potential risks such as infection and, thus, it is usually carried out during labor in rare cases. Recent advances in electronics and [...] Read more.
The invasive method of fetal electrocardiogram (fECG) monitoring is widely used with electrodes directly attached to the fetal scalp. There are potential risks such as infection and, thus, it is usually carried out during labor in rare cases. Recent advances in electronics and technologies have enabled fECG monitoring from the early stages of pregnancy through fECG extraction from the combined fetal/maternal ECG (f/mECG) signal recorded non-invasively in the abdominal area of the mother. However, cumbersome algorithms that require the reference maternal ECG as well as heavy feature crafting makes out-of-clinics fECG monitoring in daily life not yet feasible. To address these challenges, we proposed a pure end-to-end deep learning model to detect fetal QRS complexes (i.e., the main spikes observed on a fetal ECG waveform). Additionally, the model has the residual network (ResNet) architecture that adopts the novel 1-D octave convolution (OctConv) for learning multiple temporal frequency features, which in turn reduce memory and computational cost. Importantly, the model is capable of highlighting the contribution of regions that are more prominent for the detection. To evaluate our approach, data from the PhysioNet 2013 Challenge with labeled QRS complex annotations were used in the original form, and the data were then modified with Gaussian and motion noise, mimicking real-world scenarios. The model can achieve a F1 score of 91.1% while being able to save more than 50% computing cost with less than 2% performance degradation, demonstrating the effectiveness of our method. Full article
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Open AccessArticle
Real-Time Interference Artifacts Suppression in Array of ToF Sensors
Sensors 2020, 20(13), 3701; https://doi.org/10.3390/s20133701 - 02 Jul 2020
Abstract
Time of Flight (ToF) sensors are the source of various errors, including the multi-camera interference artifact caused by the parallel scanning mode of the sensors. This paper presents the novel Importance Map Based Median filtration algorithm for interference artifacts suppression, as the potential [...] Read more.
Time of Flight (ToF) sensors are the source of various errors, including the multi-camera interference artifact caused by the parallel scanning mode of the sensors. This paper presents the novel Importance Map Based Median filtration algorithm for interference artifacts suppression, as the potential 3D filtration method. The approach is based on the processing of multiple depth frames, using the extraction of the interference region and application of the interpolation. Considering the limitations and good functionalities of proposed algorithm, the combination with some standard methods was suggested. Performance of the algorithm was evaluated on the dataset consisting of the real-world objects with different texture and morphology against popular filtering methods based on neural networks and statistics. Full article
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Open AccessArticle
Analysis and Testing of a Suitable Compatible Electrode’s Material for Continuous Measurement of Glucose Concentration
Sensors 2020, 20(13), 3666; https://doi.org/10.3390/s20133666 - 30 Jun 2020
Abstract
The subject of the submitted work is the proposal of electrodes for the continual measurement of the glucose concentration for the purpose of specifying further hemodynamic parameters. The proposal includes the design of the electronic measuring system, the construction of the electrodes themselves [...] Read more.
The subject of the submitted work is the proposal of electrodes for the continual measurement of the glucose concentration for the purpose of specifying further hemodynamic parameters. The proposal includes the design of the electronic measuring system, the construction of the electrodes themselves and the functionality of the entire system, verified experimentally using various electrode materials. The proposed circuit works on the basis of micro-ammeter measuring the size of the flowing electric current and the electrochemical measurement method is used for specifying the glucose concentration. The electrode system is comprised of two electrodes embedded in a silicon tube. The solution consists of the measurement with three types of materials, which are verified by using three solutions with a precisely given concentration of glucose in the form of a mixed solution and enzyme glucose oxidase. For the testing of the proposed circuit and the selection of a suitable material, the testing did not take place on measurements in whole blood. For the construction of the electrodes, the three most frequently used materials for the construction of electrodes used in clinical practice for sensing biopotentials, specifically the materials Ag/AgCl, Cu and Au, were used. The performed experiments showed that the material Ag/AgCl, which had the greatest sensitivity for the measurement even without the enzyme, was the most suitable material for the electrode. This conclusion is supported by the performed statistical analysis. On the basis of the testing, we can come to the conclusion that even if the Ag/AgCl electrode appears to be the most suitable, showing high stability, gold-plated electrodes showed stability throughout the measurement similarly to Ag/AgCl electrodes, but did not achieve the same qualities in sensitivity and readability of the measured results. Full article
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Open AccessArticle
Towards Robust and Accurate Detection of Abnormalities in Musculoskeletal Radiographs with a Multi-Network Model
Sensors 2020, 20(11), 3153; https://doi.org/10.3390/s20113153 - 02 Jun 2020
Abstract
This study proposes a novel multi-network architecture consisting of a multi-scale convolution neural network (MSCNN) with fully connected graph convolution network (GCN), named MSCNN-GCN, for the detection of musculoskeletal abnormalities via musculoskeletal radiographs. To obtain both detailed and contextual information for a better [...] Read more.
This study proposes a novel multi-network architecture consisting of a multi-scale convolution neural network (MSCNN) with fully connected graph convolution network (GCN), named MSCNN-GCN, for the detection of musculoskeletal abnormalities via musculoskeletal radiographs. To obtain both detailed and contextual information for a better description of the characteristics of the radiographs, the designed MSCNN contains three subnetwork sequences (three different scales). It maintains high resolution in each sub-network, while fusing features with different resolutions. A GCN structure was employed to demonstrate global structure information of the images. Furthermore, both the outputs of MSCNN and GCN were fused through the concat of the two feature vectors from them, thus making the novel framework more discriminative. The effectiveness of this model was verified by comparing the performance of radiologists and three popular CNN models (DenseNet169, CapsNet, and MSCNN) with three evaluation metrics (Accuracy, F1 score, and Kappa score) using the MURA dataset (a large dataset of bone X-rays). Experimental results showed that the proposed framework not only reached the highest accuracy, but also demonstrated top scores on both F1 metric and kappa metric. This indicates that the proposed model achieves high accuracy and strong robustness in musculoskeletal radiographs, which presents strong potential for a feasible scheme with intelligent medical cases. Full article
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Review

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Open AccessReview
Applications of Nanotechnology in Sensor-Based Detection of Foodborne Pathogens
Sensors 2020, 20(7), 1966; https://doi.org/10.3390/s20071966 - 01 Apr 2020
Cited by 1
Abstract
The intake of microbial-contaminated food poses severe health issues due to the outbreaks of stern food-borne diseases. Therefore, there is a need for precise detection and identification of pathogenic microbes and toxins in food to prevent these concerns. Thus, understanding the concept of [...] Read more.
The intake of microbial-contaminated food poses severe health issues due to the outbreaks of stern food-borne diseases. Therefore, there is a need for precise detection and identification of pathogenic microbes and toxins in food to prevent these concerns. Thus, understanding the concept of biosensing has enabled researchers to develop nanobiosensors with different nanomaterials and composites to improve the sensitivity as well as the specificity of pathogen detection. The application of nanomaterials has enabled researchers to use advanced technologies in biosensors for the transfer of signals to enhance their efficiency and sensitivity. Nanomaterials like carbon nanotubes, magnetic and gold, dendrimers, graphene nanomaterials and quantum dots are predominantly used for developing biosensors with improved specificity and sensitivity of detection due to their exclusive chemical, magnetic, mechanical, optical and physical properties. All nanoparticles and new composites used in biosensors need to be classified and categorized for their enhanced performance, quick detection, and unobtrusive and effective use in foodborne analysis. Hence, this review intends to summarize the different sensing methods used in foodborne pathogen detection, their design, working principle and advances in sensing systems. Full article
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