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Electronics
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Smart Bioelectronics, Wearable Systems and E-Health
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Smart Bioelectronics, Wearable Systems and E-Health

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

Deadline for manuscript submissions: 15 January 2026 | Viewed by 1291

Special Issue Editor

Dr. Yantao Xing

E-Mail Website
Guest Editor
Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN 47408, USA
Interests: health monitoring; biomedical signal processing; wearable devices

Special Issue Information

Dear Colleagues,

This Special Issue focuses on intelligent bioelectronics, wearable systems, and e-health, aiming to highlight the integration of smart materials, embedded electronics, and digital health technologies for next-generation healthcare. As medicine moves toward more personalized, continuous, and preventive care models, the development of wearable and biointegrated systems becomes increasingly vital. These technologies enable the real-time monitoring of physiological signals, early detection of disease, and remote health management. The scope includes a wide range of interdisciplinary innovations—from flexible, biocompatible materials and implantable sensors to low-power electronics, signal processing algorithms, and AI-driven health analytics. Contributions may also address the design of secure, user-friendly e-health platforms, as well as the ethical and practical challenges of deploying such systems at scale. The purpose of this Special Issue is to present cutting-edge research that bridges materials science, biomedical engineering, and health informatics. By gathering diverse perspectives from academia and industry, the Special Issue aims to accelerate the translation of lab-based prototypes into clinically and socially impactful technologies. While a growing body of research has explored wearable sensors or biosignal analysis separately, this Special Issue emphasizes their integration into intelligent, context-aware systems. It supplements existing work by focusing on real-world applications, system-level innovation, and adaptability across diverse populations and environments. As such, this collection contributes a holistic view of how bioelectronics and digital health can work together to shape the future of medicine.

Dr. Yantao Xing
Guest Editor

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

  • health monitoring
  • intelligent bioelectronics
  • biomedical signal processing
  • personalized medicine

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Published Papers (2 papers)

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Research

20 pages, 22580 KB  
Article
Life-Threatening Ventricular Arrhythmia Identification Based on Multiple Complex Networks
by Zhipeng Cai, Menglin Yu, Jiawen Yu, Xintao Han, Jianqing Li and Yangyang Qu
Electronics 2025, 14(15), 2921; https://doi.org/10.3390/electronics14152921 - 22 Jul 2025
Viewed by 272
Abstract
Ventricular arrhythmias (VAs) are critical cardiovascular diseases that require rapid and accurate detection. Conventional approaches relying on multi-lead ECG or deep learning models have limitations in computational cost, interpretability, and real-time applicability on wearable devices. To address these issues, a lightweight and interpretable [...] Read more.
Ventricular arrhythmias (VAs) are critical cardiovascular diseases that require rapid and accurate detection. Conventional approaches relying on multi-lead ECG or deep learning models have limitations in computational cost, interpretability, and real-time applicability on wearable devices. To address these issues, a lightweight and interpretable framework based on multiple complex networks was proposed for the detection of life-threatening VAs using short-term single-lead ECG signals. The input signals were decomposed using the fixed-frequency-range empirical wavelet transform, and sub-bands were subsequently analyzed through multiscale visibility graphs, recurrence networks, cross-recurrence networks, and joint recurrence networks. Eight topological features were extracted and input into an XGBoost classifier for VA identification. Ten-fold cross-validation results on the MIT-BIH VFDB and CUDB databases demonstrated that the proposed method achieved a sensitivity of 99.02 ± 0.53%, a specificity of 98.44 ± 0.43%, and an accuracy of 98.73 ± 0.02% for 10 s ECG segments. The model also maintained robust performance on shorter segments, with 97.23 ± 0.76% sensitivity, 98.85 ± 0.95% specificity, and 96.62 ± 0.02% accuracy on 2 s segments. The results outperformed existing feature-based and deep learning approaches while preserving model interpretability. Furthermore, the proposed method supports mobile deployment, facilitating real-time use in wearable healthcare applications. Full article
(This article belongs to the Special Issue Smart Bioelectronics, Wearable Systems and E-Health)
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18 pages, 2562 KB  
Article
Data-Driven Predictive Modelling of Lifestyle Risk Factors for Cardiovascular Health
by Solomon Agyiri Kissi, Md Golam Muttaquee Talukder and Muhammad Zahid Iqbal
Electronics 2025, 14(14), 2906; https://doi.org/10.3390/electronics14142906 - 20 Jul 2025
Viewed by 821
Abstract
Cardiovascular disease (CVD) remains the foremost global cause of mortality, driven significantly by modifiable lifestyle factors. This study employs a data-driven approach to identify and evaluate these risk factors using advanced machine learning techniques. Analysing a large publicly available dataset of over 300,000 [...] Read more.
Cardiovascular disease (CVD) remains the foremost global cause of mortality, driven significantly by modifiable lifestyle factors. This study employs a data-driven approach to identify and evaluate these risk factors using advanced machine learning techniques. Analysing a large publicly available dataset of over 300,000 adult health records containing lifestyle behaviours, clinical risk factors, and self-reported health indicators, this research implemented traditional classifiers, ensemble methods, and deep learning architectures to examine the impact of behaviours such as smoking, diet, physical activity, and alcohol consumption on CVD risk. The Random Forest model demonstrated superior performance, achieving high accuracy, recall, and ROC-AUC scores. To demonstrate real-world utility, the model was deployed as an interactive Streamlit web application. This tool allows individuals to input lifestyle and health data to receive real-time CVD risk predictions, offering a novel, user-friendly prototype that bridges machine learning insights with personalised digital health engagement. This tool can facilitate personalised health monitoring and supports early detection by providing actionable insights. The findings underscore the efficacy of predictive modelling in informing targeted interventions and public health strategies. By bridging advanced analytics with practical applications, this research offers a scalable framework for reducing CVD burden, paving the way for precision medicine and improved population health outcomes through data-driven decision-making. Full article
(This article belongs to the Special Issue Smart Bioelectronics, Wearable Systems and E-Health)
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