Recent Trends and Future Directions in Biosignal Processing and Applications: State-of-the-Art Reviews

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1212

Special Issue Editors


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Guest Editor
Department of Innovation Engineering (DII), University of Salento, Via Monteroni, 73100 Lecce, Italy
Interests: fault detection; sensor technologies; measurement techniques; monitoring and measurement systems; testing and characterization components; systems and monitoring equipment
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Innovation Engineering (DII), University of Salento, Via Monteroni, 73100 Lecce, Italy
Interests: biosignal processing; sensor technologies; measurement techniques; monitoring and measurement systems; medical diagnostics; systems and monitoring equipment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of Bioengineering, focused on the section "Biosignal Processing," aims to showcase review articles on the latest advancements in the acquisition, processing, analysis, and interpretation of biosignals. We invite review articles that synthesize current knowledge and future directions within the broad field of biosignal processing, highlighting its crucial role in biomedical engineering and healthcare.

The Special Issue will prioritize contributions that address practical application of novel methodologies, algorithms, and technologies. We are particularly interested in reviews that critically evaluate existing approaches and identify promising avenues for future research. Topics of interest include, but are not limited to:

  • Advanced Signal Processing Techniques: Application and limitations of various signal processing techniques (e.g., filtering, feature extraction, classification, time-frequency analysis) for specific biosignal modalities.
  • Biomedical Imaging and Image Processing: Latest advancements in medical imaging modalities (e.g., EEG, ECG, EDA, MRI, PET) and their associated image processing and analysis techniques for improved diagnostics and monitoring.
  • Biosensors and Wearable Systems: Design, develop, and apply innovative biosensors and wearable systems for continuous health monitoring, early disease detection, and personalized medicine.
  • Rehabilitation Engineering and Clinical Applications: The role of biosignal processing in rehabilitation engineering and clinical applications, including the development of advanced prosthetics, assistive devices, and therapeutic interventions.
  • AI-driven Biomedical Systems: The use of artificial intelligence and machine learning in developing smart biomedical systems, particularly focusing on the processing and interpreting of biosignals for improved diagnostic accuracy and therapeutic efficacy.
  • Smart Sensing and Predictive Modeling: Developing and applying smart sensing technologies and predictive models for early disease detection and personalized healthcare.

Dr. Andrea Cataldo
Dr. Raissa Schiavoni
Guest Editors

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Keywords

  • biosignal processing
  • biomedical engineering healthcare
  • biosignals
  • signal processing
  • data analysis
  • biomedical imaging
  • biosensors
  • wearable sensors
  • artificial intelligence (AI)
  • machine learning (ML)
  • predictive modeling
  • personalized medicine
  • early disease detection

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Published Papers (2 papers)

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Review

20 pages, 2092 KiB  
Review
Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in Hepatocellular Carcinoma: A Review of Emerging Applications for Locoregional Therapy
by Xinyi M. Li, Tu Nguyen, Hiro D. Sparks, Kyunghyun Sung and Jason Chiang
Bioengineering 2025, 12(8), 870; https://doi.org/10.3390/bioengineering12080870 - 12 Aug 2025
Abstract
Quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is emerging as a valuable tool for assessing tumor and parenchymal perfusion in the liver, playing a developing role in locoregional therapies (LRTs) for hepatocellular carcinoma (HCC). This review explores the conceptual underpinnings and early investigational [...] Read more.
Quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is emerging as a valuable tool for assessing tumor and parenchymal perfusion in the liver, playing a developing role in locoregional therapies (LRTs) for hepatocellular carcinoma (HCC). This review explores the conceptual underpinnings and early investigational stages of DCE-MRI for LRTs, including thermal ablation, transarterial chemoembolization (TACE), and transarterial radioembolization (TARE). Preclinical and early-phase studies suggest that DCE-MRI may offer valuable insights into HCC tumor microvasculature, treatment response, and therapy planning. In thermal ablation therapies, DCE-MRI provides a quantitative measurement of tumor microvasculature and perfusion, which can guide more effective energy delivery and estimation of ablation margins. For TACE, DCE-MRI parameters are proving their potential to describe treatment efficacy and predict recurrence, especially when combined with adjuvant therapies. In 90Y TARE, DCE-MRI shows promise for refining dosimetry planning by mapping tumor blood flow to improve microsphere distribution. However, despite these promising applications, there remains a profound gap between early investigational studies and clinical translation. Current quantitative DCE-MRI research is largely confined to phantom models and initial feasibility assessments, with robust retrospective data notably lacking and prospective clinical trials yet to be initiated. With continued development, DCE-MRI has the potential to personalize LRT treatment approaches and serve as an important tool to enhance patient outcomes for HCC. Full article
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20 pages, 1125 KiB  
Review
Brain-Computer Interfaces for Stroke Motor Rehabilitation
by Alessandro Tonin, Marianna Semprini, Pawel Kiper and Dante Mantini
Bioengineering 2025, 12(8), 820; https://doi.org/10.3390/bioengineering12080820 - 30 Jul 2025
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Abstract
Brain–computer interface (BCI) technology holds promise for improving motor rehabilitation in stroke patients. This review explores the immediate and long-term effects of BCI training, shedding light on the potential benefits and challenges. Clinical studies have demonstrated that BCIs yield significant immediate improvements in [...] Read more.
Brain–computer interface (BCI) technology holds promise for improving motor rehabilitation in stroke patients. This review explores the immediate and long-term effects of BCI training, shedding light on the potential benefits and challenges. Clinical studies have demonstrated that BCIs yield significant immediate improvements in motor functions following stroke. Patients can engage in BCI training safely, making it a viable option for rehabilitation. Evidence from single-group studies consistently supports the effectiveness of BCIs in enhancing patients’ performance. Despite these promising findings, the evidence regarding long-term effects remains less robust. Further studies are needed to determine whether BCI-induced changes are permanent or only last for short durations. While evaluating the outcomes of BCI, one must consider that different BCI training protocols may influence functional recovery. The characteristics of some of the paradigms that we discuss are motor imagery-based BCIs, movement-attempt-based BCIs, and brain-rhythm-based BCIs. Finally, we examine studies suggesting that integrating BCIs with other devices, such as those used for functional electrical stimulation, has the potential to enhance recovery outcomes. We conclude that, while BCIs offer immediate benefits for stroke rehabilitation, addressing long-term effects and optimizing clinical implementation remain critical areas for further investigation. Full article
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