ECG signal feature extraction trends in methods and applications

Area of Science:

• Biomedical Signal Processing
• Artificial Intelligence in Healthcare
• Digital Health

Background:

• Physiological signal analysis is crucial for automated data analysis in medicine.
• Large datasets with thousands of features are common in biomedical signal acquisition.
• Feature extraction is vital for reducing dimensionality and improving efficiency in machine learning.

Purpose of the Study:

• To review common feature extraction techniques for electrocardiogram (ECG) signals.
• To provide pseudocode for replicating discussed methods.
• To explore the integration of deep features and machine learning for signal analysis pipelines.

Main Methods:

• ECG signal processing and feature extraction across time, frequency, and time-frequency domains.
• Decomposition and sparse domain techniques for signal analysis.
• Discussion of deep features and machine learning integration.

Main Results:

• Feature extraction enables signal dimensionality reduction and data compaction.
• Filtered redundant data enhances the efficiency of machine learning models.
• Pseudocode facilitates the replication of methods by researchers.

Conclusions:

• Feature extraction is a critical step in ECG signal analysis for AI applications.
• The review provides a comprehensive overview of techniques and future directions.
• Integration of feature extraction with machine learning optimizes automated biomedical applications.