Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
9.8
5.6
Journals
Biosensors
Special Issues
Portable Bioelectronic Devices for Telemedicine, Healthcare and Sports Applications
share announcement

Portable Bioelectronic Devices for Telemedicine, Healthcare and Sports Applications

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 6947

Special Issue Editors

Dr. András Dér

E-Mail Website
Guest Editor
Institute of Biophysics, HUN-REN Biological Research Center, 6701 Szeged, Hungary
Interests: integrated optics; electric properties of proteins and cells; lab-on-a-chip devices for photonic and biotechnological applications; statistical analysis of bioelectronics signals
Special Issues, Collections and Topics in MDPI journals
Dr. István Szendi

E-Mail Website
Guest Editor
Institute of Psychology, University of Szeged, Szeged, Hungary
Interests: psychiatry, cognitive neurosciences, neurophenomenology
Dr. Gergely Vadai

E-Mail Website
Guest Editor
Department of Technical Informatics, University of Szeged, Szeged, Hungary
Interests: human dynamics, measurement and analysis of physiological and motion signals, noise and fluctuations

Special Issue Information

Dear Colleagues,

It is our pleasure to announce a forthcoming Special Issue of the MDPI journal Biosensors, entitled “Portable bioelectronic devices for telemedicine, healthcare and sports applications”.

The convergence of advanced sensors, wireless connectivity, and data analytics has recently led to a revolution in the application of wearable bioelectronic devices capable of non-invasively registering real-time physiological signals. Using advanced signal-processing tools, they have revitalized telemedicine, healthcare, and sports applications, enabling real-time monitoring, diagnosis, treatment, and performance optimization. Some notable examples are Holter electrocardiography (ECG) instruments, wearable EEG monitors, fitness bands, and smartwatches, to name a few. Meanwhile, various digital signal-processing techniques are used to evaluate bioelectronic signals collected from wearable devices.

The goal of this Special Issue is to promote these developments by providing an overview of various existing technological and data processing approaches for portable bioelectronic devices, as well as stimulating cooperation among experts in different areas. Accordingly, we invite regular research papers and review articles that focus on novel methodological developments in wearable bioelectronic device technology and related evaluation tools for utilization in basic and applied sciences. We look forward to receiving your submissions.

Dr. András Dér
Dr. István Szendi
Dr. Gergely Vadai
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 250 words) can be sent to the Editorial Office for assessment.

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 2200 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

  • Holter electrocardiography (ECG)
  • wearable EEG monitors
  • fitness bands
  • smartwatches
  • sensors
  • actigraphy
  • physical activity
  • sleep patterns
  • blood pressure
  • blood sugar
  • digital signal-processing techniques
  • artificial intelligence
  • biomechanical models
  • healthcare
  • remote monitoring
  • personalized
  • data-driven interventions
  • psychiatry
  • sports
  • daily routine
  • behavioral science

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 1285 KB  
Article
Nonlinear Feature-Based MI Detection Supported by DWT and EMD on ECG: A High-Performance Decision Support Approach
by Ali Narin and Merve Keser
Biosensors 2026, 16(3), 150; https://doi.org/10.3390/bios16030150 - 4 Mar 2026
Viewed by 623
Abstract
Myocardial infarction (MI) is a life-threatening cardiovascular disorder caused by a partial or complete interruption of oxygenated blood flow to the myocardium, leading to high mortality rates if not diagnosed promptly. Although electrocardiogram (ECG) signals are widely used due to their non-invasive and [...] Read more.
Myocardial infarction (MI) is a life-threatening cardiovascular disorder caused by a partial or complete interruption of oxygenated blood flow to the myocardium, leading to high mortality rates if not diagnosed promptly. Although electrocardiogram (ECG) signals are widely used due to their non-invasive and low-cost nature, MI-specific abnormalities may be subtle and subject to inter-observer variability. Therefore, reliable artificial intelligence-based decision support systems are essential to enhance diagnostic classification accuracy. In this study, only the Lead II derivation from 12-lead ECG recordings of 52 healthy individuals and 148 MI patients was analyzed. To effectively characterize the non-stationary nature of ECG signals, a hybrid time–frequency feature extraction framework was employed. Five-level intrinsic mode functions and wavelet detail and approximation coefficients were obtained using Empirical Mode Decomposition and Discrete Wavelet Transform with a Daubechies-6 wavelet. From these components, 390 times, nonlinear and complexity-based features were extracted using 23 entropy-driven measures. Particle Swarm Optimization was applied to select the most discriminative feature subset, significantly enhancing classification performance. The optimized features were evaluated using Support Vector Machines, Artificial Neural Networks, k-Nearest Neighbors, and Bagged Tree classifiers. The Bagged Trees classifier achieved the best classification performance with an overall correct classification rate of 97.6%. The results demonstrate that the proposed hybrid feature representation combined with PSO-based selection provides a robust and reliable framework for MI detection, offering strong potential for clinical decision support applications. Full article
(This article belongs to the Special Issue Portable Bioelectronic Devices for Telemedicine, Healthcare and Sports Applications)
►▼ Show Figures

Figure 1

25 pages, 20305 KB  
Article
Real-Time Detection of Industrial Respirator Fit Using Embedded Breath Sensors and Machine Learning Algorithms
by Pablo Aqueveque, Pedro Pinacho-Davidson, Emilio Ramos, Sergio Sobarzo, Francisco Pastene and Anibal S. Morales
Biosensors 2025, 15(11), 745; https://doi.org/10.3390/bios15110745 - 5 Nov 2025
Cited by 1 | Viewed by 1202
Abstract
Maintaining an effective facial seal is critical for the performance of tight-fitting industrial respirators used in high-risk sectors such as mining, manufacturing, and construction. Traditional fit verification methods—Qualitative Fit Testing (QLFT) and Quantitative Fit Testing (QNFT)—are limited to periodic assessments and cannot detect [...] Read more.
Maintaining an effective facial seal is critical for the performance of tight-fitting industrial respirators used in high-risk sectors such as mining, manufacturing, and construction. Traditional fit verification methods—Qualitative Fit Testing (QLFT) and Quantitative Fit Testing (QNFT)—are limited to periodic assessments and cannot detect fit degradation during active use. This study presents a real-time fit detection system based on embedded breath sensors and machine learning algorithms. A compact sensor module inside the respirator continuously measures pressure, temperature, and humidity, transmitting data via Bluetooth Low Energy (BLE) to a smartphone for on-device inference. This system functions as a multimodal biosensor: intra-mask pressure tracks flow-driven mechanical dynamics, while temperature and humidity capture the thermal–hygrometric signature of exhaled breath. Their cycle-synchronous patterns provide an indirect yet reliable readout of respirator–face sealing in real time. Data were collected from 20 healthy volunteers under fit and misfit conditions using OSHA-standardized procedures, generating over 10,000 labeled breathing cycles. Statistical features extracted from segmented signals were used to train Random Forest, Support Vector Machine (SVM), and XGBoost classifiers. Model development and validation were conducted using variable-size sliding windows depending on the person’s breathing cycles, k-fold cross-validation, and leave-one-subject-out (LOSO) evaluation. The best-performing models achieved F1 scores approaching or exceeding 95%. This approach enables continuous, non-invasive fit monitoring and real-time alerts during work shifts. Unlike conventional techniques, the system relies on internal physiological signals rather than external particle measurements, providing a scalable, cost-effective, and field-deployable solution to enhance occupational safety and regulatory compliance. Full article
(This article belongs to the Special Issue Portable Bioelectronic Devices for Telemedicine, Healthcare and Sports Applications)
►▼ Show Figures

Figure 1

17 pages, 1319 KB  
Article
End of Apnea Event Prediction Leveraging EEG Signals and Interpretable Machine Learning
by Hisham ElMoaqet, Abdullah Ahmed, Mutaz Ryalat, Natheer Almtireen, Matthew Salanitro, Martin Glos and Thomas Penzel
Biosensors 2025, 15(11), 732; https://doi.org/10.3390/bios15110732 - 2 Nov 2025
Viewed by 1206
Abstract
Obstructive sleep apnea is a prevalent sleep disorder with serious health implications. While previous studies focused on detecting apnea events, little is known about the factors that determine whether an apnea episode continues or terminates. Understanding these mechanisms is crucial for optimizing treatment [...] Read more.
Obstructive sleep apnea is a prevalent sleep disorder with serious health implications. While previous studies focused on detecting apnea events, little is known about the factors that determine whether an apnea episode continues or terminates. Understanding these mechanisms is crucial for optimizing treatment strategies. In this study, we analyzed 30-s brain activity segments during continuous and ending apnea events to identify neurophysiological markers of event termination, with particular emphasis on the most influential EEG features. Frequency-domain and complexity features were extracted, and several ensemble machine learning models were trained and evaluated. Our results show that the Extra Trees model achieved the highest performance, with an accuracy of 0.88, F1-score for ending apnea of 0.87, and an area under the receiver operating characteristic curve of 0.95. Feature importance analyses and SHAP visualizations highlighted frequency-band energy, Teager–Kaiser energy, and signal complexity as key contributors. Temporal analyses revealed how these features evolve during apnea termination. These findings suggest that cortical activation and transient arousal processes play a decisive role in ending apnea events and may facilitate the development of more advanced adaptive or closed-loop sleep apnea therapies. Full article
(This article belongs to the Special Issue Portable Bioelectronic Devices for Telemedicine, Healthcare and Sports Applications)
►▼ Show Figures

Figure 1

21 pages, 1507 KB  
Article
A Multi-Domain Feature Fusion CNN for Myocardial Infarction Detection and Localization
by Yunfan Chen, Jinxing Ye, Yuting Li, Zhe Luo, Jieqiang Luo and Xiangkui Wan
Biosensors 2025, 15(6), 392; https://doi.org/10.3390/bios15060392 - 17 Jun 2025
Cited by 6 | Viewed by 2605
Abstract
Myocardial infarction (MI) is a critical cardiovascular disease characterized by extensive myocardial necrosis occurring within a short timeframe. Traditional MI detection and localization techniques predominantly utilize single-domain features as input. However, relying solely on single-domain features of the electrocardiogram (ECG) proves challenging for [...] Read more.
Myocardial infarction (MI) is a critical cardiovascular disease characterized by extensive myocardial necrosis occurring within a short timeframe. Traditional MI detection and localization techniques predominantly utilize single-domain features as input. However, relying solely on single-domain features of the electrocardiogram (ECG) proves challenging for accurate MI detection and localization due to the inability of these features to fully capture the complexity and variability in cardiac electrical activity. To address this, we propose a multi-domain feature fusion convolutional neural network (MFF–CNN) that integrates the time domain, frequency domain, and time-frequency domain features of ECG for automatic MI detection and localization. Initially, we generate 2D frequency domain and time-frequency domain images to combine with single-dimensional time domain features, forming multi-domain input features to overcome the limitations inherent in single-domain approaches. Subsequently, we introduce a novel MFF–CNN comprising a 1D CNN and two 2D CNNs for multi-domain feature learning and MI detection and localization. The experimental results demonstrate that in rigorous inter-patient validation, our method achieves 99.98% detection accuracy and 84.86% localization accuracy. This represents a 3.43% absolute improvement in detection and a 16.97% enhancement in localization over state-of-the-art methods. We believe that our approach will greatly benefit future research on cardiovascular disease. Full article
(This article belongs to the Special Issue Portable Bioelectronic Devices for Telemedicine, Healthcare and Sports Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop