Feature Extraction Algorithm Based on Empirical Mode Decomposition

My custom-developed feature extraction algorithm based on Empirical Mode Decomposition (EMD) is a method that converts non-stationary signals into combined stationary components through signal decomposition. The algorithm implements an iterative sifting process that extracts intrinsic mode functions (IMFs) representing different frequency components, enabling better understanding and analysis of signal characteristics. EMD operates as a data-driven approach that performs adaptive decomposition based on local signal characteristics without requiring predetermined basis functions. By analyzing and processing each IMF component through Python/Matlab implementations involving envelope detection and mean curve calculation, we obtain more comprehensive and accurate feature extraction results. This EMD-based feature extraction algorithm demonstrates extensive applications across multiple domains including signal processing, image analysis, and speech recognition. My implementation provides an effective tool for understanding and analyzing non-stationary signal features, supporting subsequent research and practical applications through customizable decomposition parameters and component selection.