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

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

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 45356

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Special Issue Editors

Dr. Jan Kubicek

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Guest Editor

Department of Cybernetics and Biomedical Engineering, VSB–Technical University of Ostrava, 17. listopadu 15, 70800 Ostrava, Czech Republic
Interests: biomedical signal processing; biomedical image processing; biological systems modeling

Prof. Dr. Marek Penhaker

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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

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Guest Editor

Faculty of Informatics and Management, Center for Basic and Applied Research, University of Hradec Kralove, Rokitanskeho 62, 50003 Hradec Kralove, Czech Republic
Interests: control systems; smart sensors; ubiquitous computing; manufacturing; wireless technology; portable devices; biomedicine; image segmentation and recognition; biometrics; technical cybernetics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ali Selamat

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Guest Editor

Universiti Teknologi Malaysia (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, Collections and Topics 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

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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 engineering
biomedical sensors
transducers
artificial intelligence
biomedical signal and image processing

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Published Papers (11 papers)

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Editorial

Jump to: Research, Review

4 pages, 183 KiB

Open AccessEditorial

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

by Jan Kubicek, Marek Penhaker, Ondrej Krejcar and Ali Selamat

Sensors 2021, 21(3), 847; https://doi.org/10.3390/s21030847 - 27 Jan 2021

Cited by 4 | Viewed by 2356

Abstract

There are various modern systems for the measurement and consequent acquisition of valuable patient’s records in the form of medical signals and images, which are supposed to be processed to provide significant information about the state of biological tissues [...] Full article

(This article belongs to the Special Issue Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing)

Research

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18 pages, 5662 KiB

Open AccessArticle

A Bronchoscope Localization Method Using an Augmented Reality Co-Display of Real Bronchoscopy Images with a Virtual 3D Bronchial Tree Model

by Jong-Chih Chien, Jiann-Der Lee, Ellen Su and Shih-Hong Li

Sensors 2020, 20(23), 6997; https://doi.org/10.3390/s20236997 - 7 Dec 2020

Cited by 7 | Viewed by 3349

Abstract

In recent years, Image-Guide Navigation Systems (IGNS) have become an important tool for various surgical operations. In the preparations for planning a surgical path, verifying the location of a lesion, etc., it is an essential tool; in operations such as bronchoscopy, which is the procedure for the inspection and retrieval of diagnostic samples for lung-related surgeries, it is even more so. The IGNS for bronchoscopy uses 2D-based images from a flexible bronchoscope to navigate through the bronchial airways in order to reach the targeted location. In this procedure, the accurate localization of the scope becomes very important, because incorrect information could potentially cause a surgeon to mistakenly direct the scope down the wrong passage. It would be a great aid for the surgeon to be able to visualize the bronchoscope images alongside the current location of the bronchoscope. For this purpose, in this paper, we propose a novel registration method to match real bronchoscopy images with virtual bronchoscope images from a 3D bronchial tree model built using computed tomography (CT) image stacks in order to obtain the current 3D position of the bronchoscope in the airways. This method is a combination of a novel position-tracking method using the current frames from the bronchoscope and the verification of the position of the real bronchoscope image against an image extracted from the 3D model using an adaptive-network-based fuzzy inference system (ANFIS)-based image matching method. Experimental results show that the proposed method performs better than the other methods used in the comparison. Full article

(This article belongs to the Special Issue Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing)

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28 pages, 1606 KiB

Open AccessArticle

Identification of Heart Sounds with an Interpretable Evolving Fuzzy Neural Network

by Paulo Vitor de Campos Souza and Edwin Lughofer

Sensors 2020, 20(22), 6477; https://doi.org/10.3390/s20226477 - 12 Nov 2020

Cited by 12 | Viewed by 2796

Abstract

Heart problems are responsible for the majority of deaths worldwide. The use of intelligent techniques to assist in the identification of existing patterns in these diseases can facilitate treatments and decision making in the field of medicine. This work aims to extract knowledge from a dataset based on heart noise behaviors in order to determine whether heart murmur predilection exists or not in the analyzed patients. A heart murmur can be pathological due to defects in the heart, so the use of an evolving hybrid technique can assist in detecting this comorbidity team, and at the same time, extract knowledge through fuzzy linguistic rules, facilitating the understanding of the nature of the evaluated data. Heart disease detection tests were performed to compare the proposed hybrid model’s performance with state of the art for the subject. The results obtained (90.75% accuracy) prove that in addition to great assertiveness in detecting heart murmurs, the evolving hybrid model could be concomitant with the extraction of knowledge from data submitted to an intelligent approach. Full article

(This article belongs to the Special Issue Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing)

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25 pages, 3870 KiB

Open AccessArticle

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

by Patricia Becerra-Sánchez, Angelica Reyes-Munoz and Antonio Guerrero-Ibañez

Sensors 2020, 20(20), 5881; https://doi.org/10.3390/s20205881 - 17 Oct 2020

Cited by 21 | Viewed by 4166

Abstract

In recent years, research has focused on generating mechanisms to assess the levels of subjects’ cognitive workload when performing various activities that demand high concentration levels, such as driving a vehicle. These mechanisms have implemented several tools for analyzing the cognitive workload, and electroencephalographic (EEG) signals have been most frequently used due to their high precision. However, one of the main challenges in implementing the EEG signals is finding appropriate information for identifying cognitive states. Here, we present a new feature selection model for pattern recognition using information from EEG signals based on machine learning techniques called GALoRIS. GALoRIS combines Genetic Algorithms and Logistic Regression to create a new fitness function that identifies and selects the critical EEG features that contribute to recognizing high and low cognitive workloads and structures a new dataset capable of optimizing the model’s predictive process. We found that GALoRIS identifies data related to high and low cognitive workloads of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50% and maximizing the model’s predictive capacity, achieving a precision rate greater than 90%. Full article

(This article belongs to the Special Issue Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing)

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24 pages, 66565 KiB

Open AccessArticle

Towards to Optimal Wavelet Denoising Scheme—A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing

by Ladislav Stanke, Jan Kubicek, Dominik Vilimek, Marek Penhaker, Martin Cerny, Martin Augustynek, Nikola Slaninova and Muhammad Usman Akram

Sensors 2020, 20(18), 5301; https://doi.org/10.3390/s20185301 - 16 Sep 2020

Cited by 10 | Viewed by 3506

Abstract

Wavelet transformation is one of the most frequent procedures for data denoising, smoothing, decomposition, features extraction, and further related tasks. In order to perform such tasks, we need to select appropriate wavelet settings, including particular wavelet, decomposition level and other parameters, which form the wavelet transformation outputs. Selection of such parameters is a challenging area due to absence of versatile recommendation tools for suitable wavelet settings. In this paper, we propose a versatile recommendation system for prediction of suitable wavelet selection for data smoothing. The proposed system is aimed to generate spatial response matrix for selected wavelets and the decomposition levels. Such response enables the mapping of selected evaluation parameters, determining the efficacy of wavelet settings. The proposed system also enables tracking the dynamical noise influence in the context of Wavelet efficacy by using volumetric response. We provide testing on computed tomography (CT) and magnetic resonance (MR) image data and EMG signals mostly of musculoskeletal system to objectivise system usability for clinical data processing. The experimental testing is done by using evaluation parameters such is MSE (Mean Squared Error), ED (Euclidean distance) and Corr (Correlation index). We also provide the statistical analysis of the results based on Mann-Whitney test, which points out on statistically significant differences for individual Wavelets for the data corrupted with Salt and Pepper and Gaussian noise. Full article

(This article belongs to the Special Issue Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing)

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15 pages, 3953 KiB

Open AccessArticle

A New Approach for Testing Fetal Heart Rate Monitors

by Daniele Bibbo, Tomas Klinkovsky, Marek Penhaker, Petr Kudrna, Lukas Peter, Martin Augustynek, Vladimír Kašík, Jan Kubicek, Ali Selamat, Martin Cerny and Daniel Bielcik

Sensors 2020, 20(15), 4139; https://doi.org/10.3390/s20154139 - 25 Jul 2020

Cited by 4 | Viewed by 5326

Abstract

In this paper, a new approach for the periodical testing and the functionality evaluation of a fetal heart rate monitor device based on ultrasound principle is proposed. The design and realization of the device are presented, together with the description of its features and functioning tests. In the designed device, a relay element, driven by an electric signal that allows switching at two specific frequencies, is used to simulate the fetus and the mother’s heartbeat. The simulator was designed to be compliant with the standard requirements for accurate assessment and measurement of medical devices. The accuracy of the simulated signals was evaluated, and it resulted to be stable and reliable. The generated frequencies show an error of about 0.5% with respect to the nominal one while the accuracy of the test equipment was within ±3% of the test signal set frequency. This value complies with the technical standard for the accuracy of fetal heart rate monitor devices. Moreover, the performed tests and measurements show the correct functionality of the developed simulator. The proposed equipment and testing respect the technical requirements for medical devices. The features of the proposed device make it simple and quick in testing a fetal heart rate monitor, thus providing an efficient way to evaluate and test the correlation capabilities of commercial apparatuses. Full article

(This article belongs to the Special Issue Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing)

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13 pages, 2274 KiB

Open AccessArticle

An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram

by Khuong Vo, Tai Le, Amir M. Rahmani, Nikil Dutt and Hung Cao

Sensors 2020, 20(13), 3757; https://doi.org/10.3390/s20133757 - 4 Jul 2020

Cited by 31 | Viewed by 3799

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 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

(This article belongs to the Special Issue Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing)

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18 pages, 12483 KiB

Open AccessArticle

Real-Time Interference Artifacts Suppression in Array of ToF Sensors

by Jozef Volak, Jakub Bajzik, Silvia Janisova, Dusan Koniar and Libor Hargas

Sensors 2020, 20(13), 3701; https://doi.org/10.3390/s20133701 - 2 Jul 2020

Cited by 5 | Viewed by 2975

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 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

(This article belongs to the Special Issue Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing)

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19 pages, 2070 KiB

Open AccessArticle

Analysis and Testing of a Suitable Compatible Electrode’s Material for Continuous Measurement of Glucose Concentration

by Nikola Slaninova, Klara Fiedorova, Ali Selamat, Karolina Danisova, Jan Kubicek, Ewaryst Tkacz and Martin Augustynek

Sensors 2020, 20(13), 3666; https://doi.org/10.3390/s20133666 - 30 Jun 2020

Cited by 5 | Viewed by 3674

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 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

(This article belongs to the Special Issue Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing)

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14 pages, 2237 KiB

Open AccessArticle

Towards Robust and Accurate Detection of Abnormalities in Musculoskeletal Radiographs with a Multi-Network Model

by Shuang Liang and Yu Gu

Sensors 2020, 20(11), 3153; https://doi.org/10.3390/s20113153 - 2 Jun 2020

Cited by 18 | Viewed by 3201

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 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

(This article belongs to the Special Issue Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing)

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Review

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19 pages, 644 KiB

Open AccessReview

Applications of Nanotechnology in Sensor-Based Detection of Foodborne Pathogens

by Harsh Kumar, Kamil Kuča, Shashi Kant Bhatia, Kritika Saini, Ankur Kaushal, Rachna Verma, Tek Chand Bhalla and Dinesh Kumar

Sensors 2020, 20(7), 1966; https://doi.org/10.3390/s20071966 - 1 Apr 2020

Cited by 94 | Viewed by 8826

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 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

(This article belongs to the Special Issue Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing)

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