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Electronic Devices and Systems for Biomedical Applications

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 28998

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Special Issue Editors

Prof. Dr. Radu Ciorap

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Guest Editor

Faculty of Medical Bioengineering, “Grigore T. Popa” University of Medicine and Pharmacy, Iasi 700115, Romania
Interests: biomedical engineering; medical devices; e-health; telemedicine; physiological measurements; rehabilitation engineering; health technology management; bioengineering

Dr. Jiri Hozman

E-Mail Website
Guest Editor

Faculty of Biomedical Engineering, Czech Technical University in Prague, Nam. Sitna 3105, 272 01 Kladno, Czech Republic
Interests: biomedical engineering; biomedical instrumentations; medical devices; signal, image, and video processing; biomedical imaging

Prof. Dr. Jan Vrba

E-Mail Website
Guest Editor

Department of Electromagnetic Field, Czech Technical University, 160 00 Prague, Czech Republic
Interests: technical development microwave hyperthermia systems and treatment applicators
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electronic systems are ubiquitous in our daily lives. Furthermore, the biomedical applications of electronic circuits and systems play the most critical role in developing a quality health system. For this reason, more and more specialists are attracted to this field.

Thanks to electronics development, it is now possible to diagnose many diseases quickly and accurately and effectively monitor patients with severe or chronic conditions. The support that electronic systems provide for resolving major health crises should not be neglected, as in the current situation of the COVID-19 pandemic.

This Special Issue is dedicated to the dissemination of recent advances and novel methods in the area of biomedical applications of electronic circuits and systems.

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.

The topics of this Special Issue include but are not limited to the following areas:

Applied optoelectronics in medicine
Artificial lung ventilators
Assistive technologies
Automation in tissue decellularization
Bio(medical) Sensors and Transducers
Biomedical Monitoring Systems
Clinical trials and verification
Devices for personalized wound therapy
Electrical impedance tomography in medicine
Home therapy electronic medical devices
Instrumentation in tissue engineering applications
New tools for regenerative medicine
Measurement and testing (non-invasive approach)
Medical devices standardization
Medical electronic devices
Medical imaging systems and methods
Medical robotics
Microwave medical diagnostics
Microwave medical therapy
Microwave tomography in medicine
Mobile biomedical analyzers
Modeling and simulation for biomedical application
Monitoring in critical care medicine and anesthesia
Physiological monitoring using wearable technologies
Patient safety
Reliability of wearables for biological signal measurement and interpretation
Remote medical device monitoring
Signal processing for advanced diagnostics in medicine
Telemedicine and eHealth
Wearable technology

Prof. Dr. Radu Ciorap
Dr. Jiri Hozman
Prof. Dr. Jan Vrba
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. Electronics 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 2400 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

medical devices
instrumentation and signal conditioning
scalable and reconfigurable electronics
commercial functional blocks and modules
hardware custom solutions and personalized solutions
hardware-embedded intelligence
wearables

Published Papers (11 papers)

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Research

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20 pages, 4387 KiB

Open AccessArticle

A Novel Automated Empirical Mode Decomposition (EMD) Based Method and Spectral Feature Extraction for Epilepsy EEG Signals Classification

by Mădălina-Giorgiana Murariu, Florica-Ramona Dorobanțu and Daniela Tărniceriu

Electronics 2023, 12(9), 1958; https://doi.org/10.3390/electronics12091958 - 22 Apr 2023

Cited by 4 | Viewed by 1616

Abstract

The increasing incidence of epilepsy has led to the need for automatic systems that can provide accurate diagnoses in order to improve the life quality of people suffering from this neurological disorder. This paper proposes a method to automatically classify epilepsy types using EEG recordings from two databases. This approach uses the spectral power density of intrinsic mode functions (IMFs) that are obtained through the empirical mode decomposition (EMD) of EEG signals. The spectral power density of IMFs has been applied as features for the classification of focal and non-focal, as well as of focal and generalized EEG signals. The data are then classified using K-nearest Neighbor (KNN) and Naïve Bayes (NB) classifiers. The focal and non-focal data were classified with high accuracy, with KNN and NB classifiers achieving a maximum classification rate of 99.90% and 99.80%, respectively. Focal and generalized epilepsy data were classified with high rates of accuracy during wakefulness and sleep stages, with KNN achieving a maximum rate of 99.49% and NB achieving 99.20%. This method shows significant improvements in the classification of EEG signals in epilepsy compared to previous studies. It could potentially aid clinical decisions for epilepsy patients. Full article

(This article belongs to the Special Issue Electronic Devices and Systems for Biomedical Applications)

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12 pages, 1662 KiB

Open AccessArticle

Research on a Non-Invasive Hemoglobin Measurement System Based on Four-Wavelength Photoplethysmography

by Zhencheng Chen, Huishan Qin, Wenjun Ge, Shiyong Li and Yongbo Liang

Electronics 2023, 12(6), 1346; https://doi.org/10.3390/electronics12061346 - 12 Mar 2023

Cited by 3 | Viewed by 3254

Abstract

Hemoglobin is an essential parameter in human blood. This paper proposes a non-invasive hemoglobin concentration measurement method based on the characteristic parameters of four-wavelength photoplethysmography (PPG) signals combined with machine learning. The DCM08 sensor and NRF52840 chip form a data acquisition system to collect 58 human fingertip photoelectric volumetric pulse wave signals. The 160 four-wavelength PPG signal feature parameters were constructed and extracted. The feature parameters were screened by combining three feature selection methods: reliefF, Chi-square score, and information gain. The top 10, 20, and 30 features screened were used as input to evaluate the prediction performance of different feature sets for hemoglobin. The prediction models used were XGBoost, support vector machines, and logistic regression. The results showed that the optimal performance of the 30 feature sets screened using the Chi-square test was achieved by the XGBoost model with a coefficient of determination (

R^{2}

) of 0.997, root mean square error (RMSE) of 0.762 g/L, and mean absolute error (MAE) of 0.325 g/L. The study showed that the four-wavelength-based PPG signal feature parameters with the XGBoost algorithm could effectively achieve non-invasive detection of hemoglobin, providing a new measurement method in clinical practice. Full article

(This article belongs to the Special Issue Electronic Devices and Systems for Biomedical Applications)

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22 pages, 6336 KiB

Open AccessFeature PaperArticle

The 5G-FR1 Signals: Beams of the Phased Antennas Array and Time-Recurrence of Emissions with Consequences on Human Exposure

by Delia Bianca Deaconescu and Simona Miclaus

Electronics 2023, 12(2), 297; https://doi.org/10.3390/electronics12020297 - 06 Jan 2023

Cited by 1 | Viewed by 2342

Abstract

The fifth generation (5G) of mobile communication technology poses lots of questions while introducing significant improvements compared with previous generations. The most sensitive question is related to the safety of human exposure. The aim of present work was to analyze, with a few chosen examples, two of the most significant features of 5G emissions: the extreme spatial variability of the exposure and the nonlinear dynamics characteristics of the temporal variability of the exposure. Two models of patch antenna arrays operating at 3.7 GHz with varying beam forming and beam steering capabilities were considered for an analysis of the specific absorption rate of electromagnetic energy deposition in tissues of a head model. This allowed clear emphasis on the influence of the antenna geometry and feeding peculiarities on the spatial variability of exposure. The second approach implemented the original idea of following the nonlinear recurrence behavior of exposure in time, and underlined the time variability characteristics of emissions with a real-life mobile phone running different 5G applications. Time series of the emitted electric-field strengths were recorded by means a real-time spectrum analyzer and two near-field probes differently positioned in the beam. The presence of laminar emissions, chaotic emissions, determinism and recurrence in the exposures prove the potential for recurrence quantification in predicting time variability features of 5G exposure. Overall, the impact of 5G signals on living bodies, with the highest possible man-made spatial and temporal variability, may have very unpredictable bio-medical consequences. Full article

(This article belongs to the Special Issue Electronic Devices and Systems for Biomedical Applications)

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27 pages, 14302 KiB

Open AccessArticle

Deep-Learning-Based System for Assisting People with Alzheimer’s Disease

by Dan Munteanu, Catalina Bejan, Nicoleta Munteanu, Cristina Zamfir, Mile Vasić, Stefan-Mihai Petrea and Dragos Cristea

Electronics 2022, 11(19), 3229; https://doi.org/10.3390/electronics11193229 - 08 Oct 2022

Cited by 5 | Viewed by 3183

Abstract

People with Alzheimer’s disease are at risk of malnutrition, overeating, and dehydration because short-term memory loss can lead to confusion. They need a caregiver to ensure they adhere to the main meals of the day and are properly hydrated. The purpose of this paper is to present an artificial intelligence system prototype based on deep learning algorithms aiming to help Alzheimer’s disease patients regain part of the normal individual comfort and independence. The proposed system uses artificial intelligence to recognize human activity in video, being able to identify the times when the monitored person is feeding or hydrating, reminding them using audio messages that they forgot to eat or drink or that they ate too much. It also allows for the remote supervision and management of the nutrition program by a caregiver. The paper includes the study, search, training, and use of models and algorithms specific to the field of deep learning applied to computer vision to classify images, detect objects in images, and recognize human activity video streams. This research shows that, even using standard computational hardware, neural networks’ training provided good predictive capabilities for the models (image classification 96%, object detection 74%, and activity analysis 78%), with the training performed in less than 48 h, while the resulting model deployed on the portable development board offered fast response times—that is, two seconds. Thus, the current study emphasizes the importance of artificial intelligence used in helping both people with Alzheimer’s disease and their caregivers, filling an empty slot in the smart assistance software domain. Full article

(This article belongs to the Special Issue Electronic Devices and Systems for Biomedical Applications)

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19 pages, 12058 KiB

Open AccessArticle

System for Non-Contact and Multispectral Examination of Blood Supply to Cutaneous Tissue

by Michal Labuda, Maros Smondrk, Branko Babusiak and Stefan Borik

Electronics 2022, 11(18), 2958; https://doi.org/10.3390/electronics11182958 - 18 Sep 2022

Cited by 1 | Viewed by 1290

Abstract

The presented system for non-contact examination of tissue perfusion is one of the tools for complex examination of human body tissues (skin, subcutaneous) and their mutual interactions, including blood flow and activity under various external stimuli. In our system, optical radiation, with wavelengths of 525 nm, 625 nm, and 940 nm, was used to investigate the perfusion and properties of skin tissue. Our work presents that it is possible to obtain comprehensive information about the cardiovascular system and skin tissue perfusion by a suitable combination of wavelengths, light intensity, and homogeneous illumination distribution with a properly chosen sensing device—a camera. The proposed system consists of an illumination device that emits light of the three wavelengths mentioned above and thus makes it possible to investigate the skin tissue structures and their interrelationships in terms of their blood supply and interactions with each other. Full article

(This article belongs to the Special Issue Electronic Devices and Systems for Biomedical Applications)

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15 pages, 6917 KiB

Open AccessArticle

Telemedicine System Applicability Using Drones in Pandemic Emergency Medical Situations

by Paul Lucian Nedelea, Tudor Ovidiu Popa, Emilian Manolescu, Catalin Bouros, Gabriela Grigorasi, Doru Andritoi, Catalin Pascale, Avramescu Andrei and Diana Carmen Cimpoesu

Electronics 2022, 11(14), 2160; https://doi.org/10.3390/electronics11142160 - 10 Jul 2022

Cited by 7 | Viewed by 2854

Abstract

Drones have evolved significantly in recent years, acquiring greater autonomy and carrier capacity. Therefore, drones can play a substantial role in civil medicine, especially in emergency situations or for the detection and monitoring of disease spread, such as during the COVID-19 pandemic. The aim of this paper is to present the real possibilities of using drones in field rescue operations, as well as in nonsegregated airspace, in order to obtain solutions for monitoring activities and aerial work in support of the public health system in crisis situations. The particularity of our conceptual system is the use of a “swarm” of fast drones for aerial reconnaissance that operate in conjunction, thus optimizing both the search and identification time while also increasing the information area and the operability of the system. We also included a drone with an RF relay, which was connected to a hub drone. If needed, a carrier drone with medical supplies or portable devices can be integrated, which can also offer two-way audio and video communication capabilities. All of these are controlled from a mobile command center, in real time, connected also to the national dispatch center to shorten the travel time to the patient, provide support with basic but life-saving equipment, and offer the opportunity to access remote or difficult-to-reach places. In conclusion, the use of drones for medical purposes brings many advantages, such as quick help, shortened travel time to the patient, support with basic but life-saving equipment, and the opportunity to access remote or difficult-to-reach places. Full article

(This article belongs to the Special Issue Electronic Devices and Systems for Biomedical Applications)

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19 pages, 3371 KiB

Open AccessArticle

CXAI: Explaining Convolutional Neural Networks for Medical Imaging Diagnostic

by Zakaria Rguibi, Abdelmajid Hajami, Dya Zitouni, Amine Elqaraoui and Anas Bedraoui

Electronics 2022, 11(11), 1775; https://doi.org/10.3390/electronics11111775 - 02 Jun 2022

Cited by 4 | Viewed by 5535

Abstract

Deep learning models have been increasingly applied to medical images for tasks such as lesion detection, segmentation, and diagnosis. However, the field suffers from the lack of concrete definitions for usable explanations in different settings. To identify specific aspects of explainability that may catalyse building trust in deep learning models, we will use some techniques to demonstrate many aspects of explaining convolutional neural networks in a medical imaging context. One important factor influencing clinician’s trust is how well a model can justify its predictions or outcomes. Clinicians need understandable explanations about why a machine-learned prediction was made so they can assess whether it is accurate and clinically useful. The provision of appropriate explanations has been generally understood to be critical for establishing trust in deep learning models. However, there lacks a clear understanding on what constitutes an explanation that is both understandable and useful across different domains such as medical image analysis, which hampers efforts towards developing explanatory tool sets specifically tailored towards these tasks. In this paper, we investigated two major directions for explaining convolutional neural networks: feature-based post hoc explanatory methods that try to explain already trained and fixed target models and preliminary analysis and choice of the model architecture with an accuracy of 98% ± 0.156% from 36 CNN architectures with different configurations. Full article

(This article belongs to the Special Issue Electronic Devices and Systems for Biomedical Applications)

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13 pages, 4083 KiB

Open AccessArticle

Statistical Description of SaO₂–SpO₂ Relationship for Model of Oxygenation in Premature Infants

by Veronika Rafl-Huttova, Jakub Rafl, Knut Möller, Thomas E. Bachman, Petr Kudrna and Martin Rozanek

Electronics 2022, 11(9), 1314; https://doi.org/10.3390/electronics11091314 - 21 Apr 2022

Viewed by 1565

Abstract

A pulse oximeter model linking arterial (SaO₂) and peripheral (SpO₂) oxygen saturation is the terminal part of a mathematical model of neonatal oxygen transport. Previous studies have confirmed the overestimation of oxygen saturation measured by pulse oximetry in neonates compared to arterial oxygen saturation and the large variability of measured values over time caused by measurement inaccuracies. This work aimed to determine the SpO₂ measurement noise that affects the biased SpO₂ value at each time point and integrate the noise description with the systematic bias between SaO₂ and SpO₂. The SaO₂–SpO₂ bias was based on previously published clinical data from pathological patients younger than 60 days requiring ventilatory support. The statistical properties of the random SpO₂ measurement noise were estimated from the SpO₂ continuous recordings of 21 pathological and 21 physiological neonates. The result of the work is a comprehensive characterization of the properties of a pulse oximeter model describing the transfer of the input SaO₂ value to the output SpO₂ value, including the bias and noise typical for the bedside monitoring of neonates. These results will help to improve a computer model of neonatal oxygen transport. Full article

(This article belongs to the Special Issue Electronic Devices and Systems for Biomedical Applications)

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10 pages, 2769 KiB

Open AccessArticle

System for Game-like Therapy in Balance Issues Using Audiovisual Feedback and Force Platform

by Markéta Janatová, Jakub Pětioký, Kristýna Hoidekrová, Tomáš Veselý, Karel Hána, Pavel Smrčka, Lubomír Štěpánek, Marcela Lippert-Grünerová and Jaroslav Jeřábek

Electronics 2022, 11(8), 1179; https://doi.org/10.3390/electronics11081179 - 08 Apr 2022

Cited by 2 | Viewed by 1462

Abstract

Background: The aim of the work is to verify the usability of a stabilometric platform and audiovisual feedback in the group-based therapy of patients with vertebral algic syndrome, to analyze an immediate effect after a single therapeutic unit, and to analyze differences between male and female probands. Methods: The study included 189 patients (90 male, age 55 ± 12 and 89 female, age 52 ± 12). All patients received group balance therapy with a portable medical device, Homebalance MA. The intervention consisted of measurement of quiet stance and a reference training scene before and after 15 min of game-like balance training with audiovisual feedback. Results: A statistically significantly lower value of the body sway trajectory during quiet stance was detected in men than in women. After a single therapy session, there was a statistically significant improvement in quiet stance with visual feedback, and marginal statistically significant improvement in the time required to complete the reference training scene. Conclusions: Homebalance MA is a utilizable tool for group therapy. The use of group game-like balance training increases the availability of physiotherapeutic intervention for a larger number of patients, while maintaining the positive effect of the therapy. Full article

(This article belongs to the Special Issue Electronic Devices and Systems for Biomedical Applications)

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10 pages, 977 KiB

Open AccessArticle

Measuring of the Energy Expenditure during Balance Training Using Wearable Electronics

by Tomáš Veselý, Markéta Janatová, Pavel Smrčka, Martin Vítězník, Radim Kliment and Karel Hána

Electronics 2022, 11(7), 1096; https://doi.org/10.3390/electronics11071096 - 30 Mar 2022

Cited by 2 | Viewed by 1545

Abstract

Homebalance Stability medical device, based on audio-visual feedback and Nintendo Wii Balance Board, is a suitable tool for telerehabilitation of balance issues in patients with brain damage. The main goal was to expand the system by energy expenditure measurements and to verify the usability of the telemetric mobile device FlexiGuard. We used the FlexiGuard system (developed at our institute) and Oxycon (JAEGER^® Oxycon Mobile, Germany) to measure the energy expenditure. We performed measurements on eight probands. Each proband underwent six activities for a total length of 90 min. During these activities, we measured energy expenditure using Oxycon and heart rate using the FlexiGuard system, from which we calculated the energy expenditure. By comparing the energy expenditure from measuring the heart rate with the FlexiGuard system with that from the Oxycon reference device, we verified the applicability of the FlexiGuard system for estimation energy expenditure. The average deviation from the reference instrument was under 30%. The conventional method, such as Oxycon, cannot be used during home therapy. Therefore, we upgraded the platform of our telemetry system (FlexiGuard), which can measure the heart rate and calculate the energy expenditure. Full article

(This article belongs to the Special Issue Electronic Devices and Systems for Biomedical Applications)

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10 pages, 2703 KiB

Open AccessBrief Report

Linear, High Dynamic Range Isolated Skin Resistance Transducer Circuit for Neurophysiological Research in Individuals after Spinal Cord Injury

by Martin Vítězník, Tomáš Veselý, Radim Kliment, Pavel Smrčka and Jiri Kriz

Electronics 2022, 11(7), 1121; https://doi.org/10.3390/electronics11071121 - 01 Apr 2022

Viewed by 1315

Abstract

The quantification of skin resistance in individuals after spinal cord injury for the purpose of neurophysiological research is difficult, mainly as a consequence of decreased activity of sweat glands in the injured human organism. In this original work, we propose a custom electrical skin resistance transducer, featuring extremely low patient auxiliary current, linear response and high dynamic range. After the design and fabrication of the prototype device, we conducted preliminary benchmark tests. We found that our prototype transducer was able to linearly report a broad range of resistance presented to its input terminals, which is not usually found in skin resistance research instrumentation. The basic design idea is viable and, following further research, an improved version of presented prototype device may be used for the purpose of neurophysiological research in individuals after spinal cord injury. Full article

(This article belongs to the Special Issue Electronic Devices and Systems for Biomedical Applications)

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