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Advances in Signal Processing for Biomedical Applications and Healthcare
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Advances in Signal Processing for Biomedical Applications and Healthcare

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 6574

Special Issue Editors

Dr. Cataldo Guaragnella

E-Mail Website
Guest Editor
Dipartimento di Ingegneria Elettrica e dell’Informazione, Politecnico di Bari, Via E. Orabona 4, 70125 Bari, Italy
Interests: signal processing; signal; image and video coding; pattern recognition; multidimensional signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Maria Rizzi

E-Mail Website
Guest Editor
Department of Electrics and Information Engineering, Politecnico di Bari, 70125 Bari, Italy
Interests: computer aided detection and diagnosis systems for biomedical signals; monitoring systems for health-care; analysis and synthesis of digital electronic systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Various technologies in the areas of communication/computer networks and innovations in healthcare have introduced a radical change in medical environment including patient diagnostic data and patient biological signal facilities and processing. In fact, new ideas, services, processes, and products have been introduced with a view to improving treatment, diagnosis, education, outreach, prevention and research with long-term goals of enhancing quality, safety, outcomes, efficiency and costs. At present, healthcare technologies include several parameter categories such as devices (equipment and supplies), medical and surgical procedures (e.g., laparoscopy), support systems (e.g., telehealth, telemedicine), and organizing and administrative systems. Projections into the future predict an even greater role of technology in medical practice and healthcare.

This Special Issue addresses the most recent research in medical application and healthcare. Topics of interest for this Special Issue include, but are not limited to, the following:

  • Development of computer-aided detection/diagnosis systems for medical applications;
  • Effective trends for biosignal/bioimage processing and analysis for monitoring treatment efficacy;
  • Advances in healthcare monitoring systems;
  • Digital technologies for supporting healthcare;
  • Algorithms and techniques for signal, image and video processing in healthcare;
  • Multidimensional digital signal processing to support diagnosis systems.

For this Special Issue, original research articles and reviews are welcome. We look forward to receiving your contributions.

Dr. Cataldo Guaragnella
Dr. Maria Rizzi
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. 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

  • computer-aided diagnosis systems
  • digital technologies for healthcare
  • biomedical signal/image/video processing
  • algorithms and techniques for healthcare

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Published Papers (5 papers)

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Research

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19 pages, 2612 KiB  
Article
Kalman Filter-Based Epidemiological Model for Post-COVID-19 Era Surveillance and Prediction
by Yuanyou Shi, Xinhang Zhu, Xinhe Zhu, Baiqi Cheng and Yongmin Zhong
Sensors 2025, 25(8), 2507; https://doi.org/10.3390/s25082507 - 16 Apr 2025
Viewed by 174
Abstract
In the post-COVID-19 era, the dynamic spread of COVID-19 poses new challenges to epidemiological modelling, particularly due to the absence of large-scale screening and the growing complexity introduced by immune failure and reinfections. This paper proposes an AEIHD (antibody-acquired, exposed, infected, hospitalised, and [...] Read more.
In the post-COVID-19 era, the dynamic spread of COVID-19 poses new challenges to epidemiological modelling, particularly due to the absence of large-scale screening and the growing complexity introduced by immune failure and reinfections. This paper proposes an AEIHD (antibody-acquired, exposed, infected, hospitalised, and deceased) model to analyse and predict COVID-19 transmission dynamics in the post-COVID-19 era. This model removes the susceptible compartment and combines the recovered and vaccinated compartments into an “antibody-acquired” compartment. It also introduces a new hospitalised compartment to monitor severe cases. The model incorporates an antibody-acquired infection rate to account for immune failure. The Extended Kalman Filter based on the AEIHD model is proposed for real-time state and parameter estimation, overcoming the limitations of fixed-parameter approaches and enhancing adaptability to nonlinear dynamics. Simulation studies based on reported data from Australia validate the AEIHD model, demonstrating its capability to accurately capture COVID-19 transmission dynamics with limited statistical information. The proposed approach addresses the key limitations of traditional SIR and SEIR models by integrating hospitalisation data and time-varying parameters, offering a robust framework for monitoring and predicting epidemic behaviours in the post-COVID-19 era. It also provides a valuable tool for public health decision-making and resource allocation to handle rapidly evolving epidemiology. Full article
(This article belongs to the Special Issue Advances in Signal Processing for Biomedical Applications and Healthcare)
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21 pages, 2788 KiB  
Article
Comparative Assessment of Multimodal Sensor Data Quality Collected Using Android and iOS Smartphones in Real-World Settings
by Ramzi Halabi, Rahavi Selvarajan, Zixiong Lin, Calvin Herd, Xueying Li, Jana Kabrit, Meghasyam Tummalacherla, Elias Chaibub Neto and Abhishek Pratap
Sensors 2024, 24(19), 6246; https://doi.org/10.3390/s24196246 - 26 Sep 2024
Viewed by 1624
Abstract
Healthcare researchers are increasingly utilizing smartphone sensor data as a scalable and cost-effective approach to studying individualized health-related behaviors in real-world settings. However, to develop reliable and robust digital behavioral signatures that may help in the early prediction of the individualized disease trajectory [...] Read more.
Healthcare researchers are increasingly utilizing smartphone sensor data as a scalable and cost-effective approach to studying individualized health-related behaviors in real-world settings. However, to develop reliable and robust digital behavioral signatures that may help in the early prediction of the individualized disease trajectory and future prognosis, there is a critical need to quantify the potential variability that may be present in the underlying sensor data due to variations in the smartphone hardware and software used by large population. Using sensor data collected in real-world settings from 3000 participants’ smartphones for up to 84 days, we compared differences in the completeness, correctness, and consistency of the three most common smartphone sensors—the accelerometer, gyroscope, and GPS— within and across Android and iOS devices. Our findings show considerable variation in sensor data quality within and across Android and iOS devices. Sensor data from iOS devices showed significantly lower levels of anomalous point density (APD) compared to Android across all sensors (p  <  1 × 10−4). iOS devices showed a considerably lower missing data ratio (MDR) for the accelerometer compared to the GPS data (p  <  1 × 10−4). Notably, the quality features derived from raw sensor data across devices alone could predict the device type (Android vs. iOS) with an up to 0.98 accuracy 95% CI [0.977, 0.982]. Such significant differences in sensor data quantity and quality gathered from iOS and Android platforms could lead to considerable variation in health-related inference derived from heterogenous consumer-owned smartphones. Our research highlights the importance of assessing, measuring, and adjusting for such critical differences in smartphone sensor-based assessments. Understanding the factors contributing to the variation in sensor data based on daily device usage will help develop reliable, standardized, inclusive, and practically applicable digital behavioral patterns that may be linked to health outcomes in real-world settings. Full article
(This article belongs to the Special Issue Advances in Signal Processing for Biomedical Applications and Healthcare)
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17 pages, 48171 KiB  
Article
S5Utis: Structured State-Space Sequence SegNeXt UNet-like Tongue Image Segmentation in Traditional Chinese Medicine
by Donglei Song, Hongda Zhang, Lida Shi, Hao Xu and Ying Xu
Sensors 2024, 24(13), 4046; https://doi.org/10.3390/s24134046 - 21 Jun 2024
Viewed by 1372
Abstract
Intelligent Traditional Chinese Medicine can provide people with a convenient way to participate in daily health care. The ease of acceptance of Traditional Chinese Medicine is also a major advantage in promoting health management. In Traditional Chinese Medicine, tongue imaging is an important [...] Read more.
Intelligent Traditional Chinese Medicine can provide people with a convenient way to participate in daily health care. The ease of acceptance of Traditional Chinese Medicine is also a major advantage in promoting health management. In Traditional Chinese Medicine, tongue imaging is an important step in the examination process. The segmentation and processing of the tongue image directly affects the results of intelligent Traditional Chinese Medicine diagnosis. As intelligent Traditional Chinese Medicine continues to develop, remote diagnosis and patient participation will play important roles. Smartphone sensor cameras can provide irreplaceable data collection capabilities in enhancing interaction in smart Traditional Chinese Medicine. However, these factors lead to differences in the size and quality of the captured images due to factors such as differences in shooting equipment, professionalism of the photographer, and the subject’s cooperation. Most current tongue image segmentation algorithms are based on data collected by professional tongue diagnosis instruments in standard environments, and are not able to demonstrate the tongue image segmentation effect in complex environments. Therefore, we propose a segmentation algorithm for tongue images collected in complex multi-device and multi-user environments. We use convolutional attention and extend state space models to the 2D environment in the encoder. Then, cross-layer connection fusion is used in the decoder part to fuse shallow texture and deep semantic features. Through segmentation experiments on tongue image datasets collected by patients and doctors in real-world settings, our algorithm significantly improves segmentation performance and accuracy. Full article
(This article belongs to the Special Issue Advances in Signal Processing for Biomedical Applications and Healthcare)
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18 pages, 3559 KiB  
Article
Novel Metric for Non-Invasive Beat-to-Beat Blood Pressure Measurements Demonstrates Physiological Blood Pressure Fluctuations during Pregnancy
by David Zimmermann, Hagen Malberg and Martin Schmidt
Sensors 2024, 24(10), 3151; https://doi.org/10.3390/s24103151 - 15 May 2024
Viewed by 1618
Abstract
Beat-to-beat (B2B) variability in biomedical signals has been shown to have high diagnostic power in the treatment of various cardiovascular and autonomic disorders. In recent years, new techniques and devices have been developed to enable non-invasive blood pressure (BP) measurements. In this work, [...] Read more.
Beat-to-beat (B2B) variability in biomedical signals has been shown to have high diagnostic power in the treatment of various cardiovascular and autonomic disorders. In recent years, new techniques and devices have been developed to enable non-invasive blood pressure (BP) measurements. In this work, we aim to establish the concept of two-dimensional signal warping, an approved method from ECG signal processing, for non-invasive continuous BP signals. To this end, we introduce a novel BP-specific beat annotation algorithm and a B2B-BP fluctuation (B2B-BPF) metric novel for BP measurements that considers the entire BP waveform. In addition to careful validation with synthetic data, we applied the generated analysis pipeline to non-invasive continuous BP signals of 44 healthy pregnant women (30.9 ± 5.7 years) between the 21st and 30th week of gestation (WOG). In line with established variability metrics, a significant increase (p < 0.05) in B2B-BPF can be observed with advancing WOGs. Our processing pipeline enables robust extraction of B2B-BPF, demonstrates the influence of various factors such as increasing WOG or exercise on blood pressure during pregnancy, and indicates the potential of novel non-invasive biosignal sensing techniques in diagnostics. The results represent B2B-BP changes in healthy pregnant women and allow for future comparison with those signals acquired from women with hypertensive disorders. Full article
(This article belongs to the Special Issue Advances in Signal Processing for Biomedical Applications and Healthcare)
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17 pages, 889 KiB  
Perspective
Issues and Limitations on the Road to Fair and Inclusive AI Solutions for Biomedical Challenges
by Oliver Faust, Massimo Salvi, Prabal Datta Barua, Subrata Chakraborty, Filippo Molinari and U. Rajendra Acharya
Sensors 2025, 25(1), 205; https://doi.org/10.3390/s25010205 - 2 Jan 2025
Cited by 2 | Viewed by 1115
Abstract
Objective: In this paper, we explore the correlation between performance reporting and the development of inclusive AI solutions for biomedical problems. Our study examines the critical aspects of bias and noise in the context of medical decision support, aiming to provide actionable solutions. [...] Read more.
Objective: In this paper, we explore the correlation between performance reporting and the development of inclusive AI solutions for biomedical problems. Our study examines the critical aspects of bias and noise in the context of medical decision support, aiming to provide actionable solutions. Contributions: A key contribution of our work is the recognition that measurement processes introduce noise and bias arising from human data interpretation and selection. We introduce the concept of “noise-bias cascade” to explain their interconnected nature. While current AI models handle noise well, bias remains a significant obstacle in achieving practical performance in these models. Our analysis spans the entire AI development lifecycle, from data collection to model deployment. Recommendations: To effectively mitigate bias, we assert the need to implement additional measures such as rigorous study design; appropriate statistical analysis; transparent reporting; and diverse research representation. Furthermore, we strongly recommend the integration of uncertainty measures during model deployment to ensure the utmost fairness and inclusivity. These comprehensive recommendations aim to minimize both bias and noise, thereby improving the performance of future medical decision support systems. Full article
(This article belongs to the Special Issue Advances in Signal Processing for Biomedical Applications and Healthcare)
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