Machine Learning Techniques for Advanced Biomedical Signal Processing and Analysis
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 February 2026 | Viewed by 121
Special Issue Editor
Interests: machine learning; biomedical signal processing; computational intelligence; dynamic systems; optimization theory/applications; soft computing; data analysis
Special Issue Information
Dear Colleagues,
This Special Issue aims to explore cutting-edge advancements in the application of machine learning (ML) and artificial intelligence (AI) to biomedical signal processing. With the rapid growth of wearable devices, high-resolution medical sensors, and computational power, ML-based approaches have become indispensable in extracting meaningful information from complex biomedical signals such as EEG, ECG, EMG, MRI, and others. This Special Issue seeks original research and review articles that address novel algorithms, deep learning architectures, signal enhancement techniques, and interpretable AI models for biomedical applications. Topics of interest include, but are not limited to, automated diagnosis, real-time signal analysis, noise reduction, feature extraction, and multimodal data fusion. Contributions that bridge the gap between theoretical ML advancements and practical clinical implementations are particularly welcome. Overall, the goal is to foster interdisciplinary collaboration among researchers in signal processing, machine learning, and healthcare to drive innovation in precision medicine and personalized healthcare solutions.
Dr. Marcos Flavio D'Angelo
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- biomedical signal processing
- deep learning
- EEG/ECG/EMG analysis
- AI in healthcare
- signal denoising
- feature extraction
- medical diagnostics
- multimodal data fusion
- explainable AI
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