Latest Wearable Biosensors—2nd Edition

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Wearable Biosensors".

Deadline for manuscript submissions: 25 October 2025 | Viewed by 752

Special Issue Editors


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Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA
Interests: low−power RF/analog integrated circuits & System−on−a−Chip (SoC) design and test; interdisciplinary research on medical electronics, biosensors & biosignal processing
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Department of Otolaryngology, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
Interests: head and neck surgery; speech and hearing sciences
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Guest Editor
Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
Interests: analog and mixed-signal IC design; biomedical IC design
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Special Issue Information

Dear Colleagues,

After the successful publication of the first edition of the Special Issue "Latest Wearable Biosensors", we are pleased to announce the exciting second edition! Facing a rapidly aging global population, the future of healthcare will need to become heavily personalized to reduce its enormous associated costs and become “smart”. With this, clinicians need to have certain continuously monitored biomarkers for evaluating patients’ health conditions individually. The topic of wearable sensors has attracted much more attention recently; with the rapid development of several key advanced technologies, wearable biosensors are now able to provide practical real-time monitoring of patients’ health and well-being. Wearable biosensors can be used to track vital signs, steps, sleep, stress, hydration, oxygen content and glucose levels in the blood, amongst other measures. Furthermore, they can be easily connected wirelessly to cellphone applications, making them user friendly, inexpensive, and ubiquitous. Research and development on smart wearable biosensors for personalized e-Health services has been conducted around the world. On this note, the rapid worldwide development and public embrace of wearable biosensors (e.g., Fitbit™ and various smart watches) supports the need for another dedicated Special Issue in this area. These wearable systems for health monitoring may feature different types of miniature biosensors (say, printable and wearable patches) capable of measuring different physiological parameters: vital signs (heart rate, respiration rate, body and skin temperature, oxygen saturation, etc.), skin conductance, blood pressure, electrocardiogram (ECG) measurements, electroencephalography (EEG) measurements, acceleration, and rotation. Furthermore, one can also use the measured data to predict/assess mood changes, cardio health, fitness conditions, posture, cognition state, sleep quality, fall detection, seizure prediction, and for various other monitoring purposes to improve subjects' quality of life, health management, disease control status, and even survival rates from emergency rescue operators. With the latest breakthroughs in AI (artificial intelligence), one can expect the mass production of long-lasting, accurate, robust and smart wearable biosensors with a high sensitivity and selectivity. These may hold the key to replacing many traditional diagnostic medical devices to enable timely personalized e-Health treatment and intervention, thus reducing healthcare costs and delivering a higher quality of life and well-being for patients.

Prof. Dr. Donald Y.C. Lie
Dr. Tam Q. Nguyen
Prof. Dr. Chungchih Hung
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. Biosensors is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • accelerometer activity biosensors
  • electrocardiogram (ECG)
  • electroencephalography (EEG)
  • mobile phone monitoring
  • neuroimaging skin conductance
  • smart phone
  • smart sensor
  • vital signs
  • wearable biosensors
  • wearable sensors

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Published Papers (1 paper)

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Research

16 pages, 3497 KB  
Article
Utilizing Circadian Heart Rate Variability Features and Machine Learning for Estimating Left Ventricular Ejection Fraction Levels in Hypertensive Patients: A Composite Multiscale Entropy Analysis
by Nanxiang Zhang, Qi Pan, Shuo Yang, Leen Huang, Jianan Yin, Hai Lin, Xiang Huang, Chonglong Ding, Xinyan Zou, Yongjun Zheng and Jinxin Zhang
Biosensors 2025, 15(7), 442; https://doi.org/10.3390/bios15070442 - 10 Jul 2025
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Abstract
Background: Early identification of left ventricular ejection fraction (LVEF) levels during the progression of hypertension is essential to prevent cardiac deterioration. However, achieving a non-invasive, cost-effective, and definitive assessment is challenging. It has prompted us to develop a comprehensive machine learning framework for [...] Read more.
Background: Early identification of left ventricular ejection fraction (LVEF) levels during the progression of hypertension is essential to prevent cardiac deterioration. However, achieving a non-invasive, cost-effective, and definitive assessment is challenging. It has prompted us to develop a comprehensive machine learning framework for the automatic quantitative estimation of LVEF levels from electrocardiography (ECG) signals. Methods: We enrolled 200 hypertensive patients from Zhongshan City, Guangdong Province, China, from 1 November 2022 to 1 January 2025. Participants underwent 24 h Holter monitoring and echocardiography for LVEF estimation. We developed a comprehensive machine learning framework that initiated with preprocessed ECG signal in one-hour intervals to extract CMSE-based heart rate variability (HRV) features, then utilized machine learning models such as linear regression (LR), Support Vector Machines (SVMs), and random forests (RFs) with recursive feature elimination for optimal LVEF estimation. Results: The LR model, notably during early night interval (20:00–21:00), achieved a RMSE of 4.61% and a MAE of 3.74%, highlighting its superiority. Compared with other similar studies, key CMSE parameters (Scales 1, 5, Slope 1–5, and Area 1–5) can effectively enhance regression models’ estimation performance. Conclusion: Our findings suggest that CMSE-derived circadian HRV features from Holter ECG could serve as a non-invasive, cost-effective, and interpretable solution for LVEF assessment in community settings. From a machine learning interpretable perspective, the proposed method emphasized CMSE’s clinical potential in capturing autonomic dynamics and cardiac function fluctuations. Full article
(This article belongs to the Special Issue Latest Wearable Biosensors—2nd Edition)
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