Current Trends in Feature Extraction and Classification Methodologies of Biomedical Signals

Area of Science:

• Biomedical Engineering
• Signal Processing
• Data Science

Background:

• Biomedical signal and image processing is crucial for understanding dynamic bio-signal behavior.
• Signal processing techniques are vital for assessing, reconfiguring, and improving the efficiency of analogue and digital signals.
• Feature extraction is key to uncovering hidden characteristics within signals.

Purpose of the Study:

• To investigate various feature extraction methods for biomedical signals.
• To explore feature transformation techniques relevant to bio-signal analysis.
• To provide an overview of classifiers and datasets used in biomedical signal processing.

Main Methods:

• Focus on time-domain, frequency-domain, and transformed-domain feature extraction.
• Utilize feature extraction for data reduction, comparison, and dimensionality reduction.
• Employ feature extraction to create robust patterns for classifier systems.

Main Results:

• Feature extraction reveals hidden characteristic information in input signals.
• Methods enable accurate signal representation with reduced dimensions.
• Identified methods facilitate efficient and robust pattern structures for classification.

Conclusions:

• Feature extraction is essential for effective biomedical signal analysis.
• The study provides a foundation for selecting appropriate methods, classifiers, and datasets.
• Understanding these components enhances the utility of biomedical signals in research.