Challenges and Future Trends in Biomedical Signal Processing

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

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