AI-Based Biomedical Signal Processing

Dear Colleagues, 

Artificial intelligence (AI) is spreading, and now influences all fields of healthcare. Typically used as statistical methods, these AI-based innovative tools are also promising in the other steps of the biomedical signal processing blockchain, including biomedical signal (i) acquisition, (ii) preprocessing, (iii) feature engineering and (iv) classification/interpretation. AI-based methods may find solutions to biomedical signal processing challenges by integrating sensors and acquisition systems, as well as preprocessing, characterizing, classifying, and interpreting biomedical signals. These solutions may be essential in all fields of healthcare, including cardiology, neurology, endocrinology, movement analysis, physical activity monitoring, assistive robotics, telemedicine, and others. Thus, this Special Issue aims to collect original research papers and/or reviews on AI-based methods for biomedical signal processing. Main topics include, but are not limited to:

• Intelligent sensors, devices and instruments for biomedical signal acquisition;
• AI-based biomedical signal preprocessing;
• Machine learning for biomedical feature extraction and selection;
• Knowledge engineering for feature interpretation;
• AI-based clinical decision making in healthcare;
• AI-based precision medicine;
• Data analytics and mining for clinical decision support;
• Ethics of AI in healthcare.

Dr. Agnese Sbrollini
Dr. Aurora Saibene
Guest Editors

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