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Challenges and Future Trends in Biomedical Signal Processing
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Challenges and Future Trends in Biomedical Signal Processing

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 2385

Special Issue Editors

Dr. Daniele Esposito

E-Mail Website
Guest Editor
Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, Italy
Interests: non-invasive biomedical sensors for monitoring physiological parameters; human–machine interfaces for assistance and rehabilitation; prostheses; exoskeletons; biosignal processing and analysis; electromyography (EMG); forcemyography (FMG); electrocardiography (ECG); forcecardiography (FCG)
Special Issues, Collections and Topics in MDPI journals
Dr. Jessica Centracchio

E-Mail Website
Guest Editor
Department of Information Technologies and Electrical Engineering, University of Naples Federico II, Naples, Italy
Interests: biomedical signal processing; cardiovascular monitoring; respiratory monitoring; wearable sensors; human–machine interfaces

Special Issue Information

Dear Colleagues,

Biomedical signal processing plays a fundamental role in modern healthcare, enabling the transformation of raw physiological signals into clinically meaningful information. Advances in algorithm design, feature extraction, data fusion, and real-time analysis are driving improvements in diagnosis, monitoring, and personalized medicine. At the same time, artificial intelligence, machine learning, and cloud- or IoT-based infrastructures are redefining how biomedical signals are processed, interpreted, and integrated into healthcare systems. Despite these advances, significant challenges remain, including robust noise reduction, reliable real-time interpretation, integration of multimodal and multisensory data, and privacy-preserving processing of large biomedical datasets. Addressing these issues is essential to fully exploit the potential of biomedical signal processing for continuous monitoring, decision support, and effective healthcare.

Possible topics include, but are not limited to, the following:

  • Advanced biomedical signal acquisition and processing techniques.
  • Artificial intelligence and machine learning for signal interpretation.
  • Multimodal and multisensor data fusion.
  • Cloud computing for real-time healthcare applications.
  • IoT-based healthcare signal processing.
  • Smart processing systems for personalized medicine.
  • Privacy-preserving methods for biomedical datasets.

This Special Issue aims to present innovative methodologies, frameworks, and applications that address current challenges and define future trends in biomedical signal processing.

We look forward to receiving your contributions.

Dr. Daniele Esposito
Dr. Jessica Centracchio
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. 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

  • biomedical signal processing
  • AI-enabled signal analysis
  • multimodal data fusion
  • IoT in healthcare
  • cloud-based processing
  • physiological monitoring
  • smart processing systems
  • real-time analysis
  • biomedical datasets

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Published Papers (3 papers)

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Research

25 pages, 8452 KB  
Article
Validation of a Wearable Photoplethysmography-Based Sensor for Compensatory Reserve Measurement Monitoring in Simulated Human Hemorrhage
by Jose M. Gonzalez, Ryan Ortiz, Krysta-Lynn Amezcua, Carlos Bedolla, Sofia I. Hernandez Torres, Erik K. Weitzel, Vijay S. Gorantla, Weihua Li, Alexander J. Aranyosi, John A. Rogers, Roozbeh Ghaffari, Victor A. Convertino and Eric J. Snider
Sensors 2026, 26(8), 2513; https://doi.org/10.3390/s26082513 - 18 Apr 2026
Viewed by 367
Abstract
Hemorrhagic shock remains a leading cause of preventable death in trauma, yet traditional vital signs may fail to reflect early blood loss before physiological compensatory mechanisms are no longer able to maintain hemodynamic stability. The Compensatory Reserve Measurement (CRM) algorithm offers early detection [...] Read more.
Hemorrhagic shock remains a leading cause of preventable death in trauma, yet traditional vital signs may fail to reflect early blood loss before physiological compensatory mechanisms are no longer able to maintain hemodynamic stability. The Compensatory Reserve Measurement (CRM) algorithm offers early detection capability using physiological waveforms but requires testing with emerging wearable sensor technologies for operational deployment. This study tested the Epicore Epidermal Patch for Imperceptible Care (EPIC) wearable healthcare device (WHD) for CRM-based hemodynamic monitoring during progressive central hypovolemia induced by lower-body negative pressure (LBNP) to simulate hemorrhage. Twenty participants underwent progressive LBNP while photoplethysmography (PPG) signals were recorded from EPIC sensors placed at the clavicle and triceps alongside a clinical-grade finger pulse oximeter for reference. Signal quality, heart-rate accuracy, and CRM predictions were evaluated across multiple filtering approaches. The triceps placement achieved signal quality comparable to the pulse oximeter reference when Chebyshev Type II filtering was applied, as well as high heart-rate accuracy. CRM derived from the EPIC sensor placed at the triceps tracked compensatory trends during progressive hypovolemia, but prediction magnitudes were inaccurate compared to calculated CRM values. In contrast, the clavicle placement consistently performed poorly across all measurements, regardless of the signal-processing approach. These findings support the feasibility of soft, flexible wearable sensors for continuous hemorrhage monitoring at the triceps location in operational environments where traditional finger-based pulse oximetry is impractical. Full article
(This article belongs to the Special Issue Challenges and Future Trends in Biomedical Signal Processing)
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18 pages, 3122 KB  
Article
KAN-DeScoD: Kolmogorov–Arnold Network Enhanced Deep Score-Based Diffusion Model for ECG Denoising
by Zhixin Shu, Deqiu Zhai, Lei Huang, Ying Zhang and Tao Liu
Sensors 2026, 26(7), 2213; https://doi.org/10.3390/s26072213 - 3 Apr 2026
Viewed by 616
Abstract
Thedeep score-based diffusion (DeScoD) model performs well in electrocardiogram (ECG) denoising tasks. However, due to the theoretical error lower bound in approximating functions with linear transformations, it often lacks flexibility when fitting non-stationary noise, baseline wander, or morphologically variable features such as QRS [...] Read more.
Thedeep score-based diffusion (DeScoD) model performs well in electrocardiogram (ECG) denoising tasks. However, due to the theoretical error lower bound in approximating functions with linear transformations, it often lacks flexibility when fitting non-stationary noise, baseline wander, or morphologically variable features such as QRS complexes in ECG signals. In this paper, we propose a Kolmogorov–Arnold network enhanced deep score-based diffusion (KAN-DeScoD) model, which is the first to integrate Kolmogorov–Arnold network (KAN) layers into an ECG denoising diffusion model. By leveraging KAN’s adaptive activation functions, which more finely capture the complex structures within ECG signals, the model’s robustness in high-noise environments, as well as the accuracy and stability of signal reconstruction, are improved. We validate the effectiveness of the proposed method on the QT Database and the MIT-BIH Noise Stress Test Database (NSTDB). Experimental results show that under different shots and noise intensities, ours outperforms the DeScoD model across multiple metrics. The research results demonstrate the effectiveness of introducing KAN, which improves the model’s robustness in high-noise environments and the accuracy of signal reconstruction. Full article
(This article belongs to the Special Issue Challenges and Future Trends in Biomedical Signal Processing)
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24 pages, 18384 KB  
Article
A Feasibility Study of Using an In-Ear EEG System for a Quantitative Assessment of Stress and Mental Workload
by Zhibo Fu, Kam Pang So, Xiaoli Wu, Arthit Khotsaenlee, Savio W. H. Wong, Chung Tin and Rosa H. M. Chan
Sensors 2026, 26(2), 442; https://doi.org/10.3390/s26020442 - 9 Jan 2026
Cited by 1 | Viewed by 720
Abstract
While electroencephalography (EEG) is effective for assessing stress and mental workload, its widespread adoption is currently hindered by the complex setup of most existing EEG systems. This article presents a new in-ear EEG system and investigates its feasibility for developing robust models to [...] Read more.
While electroencephalography (EEG) is effective for assessing stress and mental workload, its widespread adoption is currently hindered by the complex setup of most existing EEG systems. This article presents a new in-ear EEG system and investigates its feasibility for developing robust models to quantify stress and mental workload levels. The system consists of a single-channel EEG acquisition device that has a similar form factor as user-generic earpieces. All electrodes including passive, reference and bias electrodes were put on the ear, which optimized the device’s usability. We validated the system through two experiments with 66 subjects to collect EEG data under varying stress and mental workload conditions. We developed classification and regression models to predict stress and mental workload levels from the data. Cross-subject stress classification achieved 77% accuracy, while within-subject stress regression yielded an average R2 of 0.76 ± 0.20. Two-class mental workload level classification reached accuracies between 70% and 80% for the arithmetic and finger tapping tasks. Feature importance analysis revealed that frequency-domain EEG features, particularly in the alpha and beta bands, significantly contributed to the models’ performance. However, we observed lower within-subject feature variation and model accuracy for the mental rotation, potentially due to the distance between brain regions engaged and the device’s recording site. Our findings demonstrate the potential of using the presented EEG device to monitor stress and mental workload in real-time. Full article
(This article belongs to the Special Issue Challenges and Future Trends in Biomedical Signal Processing)
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