Machine Learning Algorithms for Biomedical Signal Processing

Dear Colleagues,

Biomedical signal processing aims to provide greater insights into the analysis of the information flows from physiological signals using a variety of mathematical formulae and algorithms.

Many researchers have proposed various novel algorithms and mathematical methods to analyze biomedical signals that significantly pushing the state of the art of biomeasurement tools into a wide range of high-level tasks. Such progress can help to gain a greater perception and to make better decisions in clinical assessments.

The purpose of this Special Issue is to present recent advances in signal processing and machine learning for biomedical signal analysis. We are focusing on original research works in this field, covering new theories, implementations, and mathematical analysis and modeling of time series in living systems and biomedical signals. Potential topics of interest are related to recent advances in machine learning and signal analysis and processing but are not limited to:

• Biomedical signal processing and analysis;
• Biomedical image processing and analysis;
• Neural rehabilitation engineering;
• Biomedical big data processing;
• Signal/image processing for human machine interface;
• Time-frequency and nonstationary biosignal analysis;
• Machine learning in biomedical applications;
• Biometrics with biomedical signals;
• Statistical pattern recognition.

Dr. Maryam Ravan
Dr. Ganesh Naik
Guest Editor

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