Local Mean Decomposition of Signals with Algorithm Implementation Details

Local Mean Decomposition (LMD) is applied to signals to obtain a series of Product Functions (PF components) with physical significance. The LMD method operates through an iterative algorithm that involves: 1) identifying local extrema points, 2) computing moving averages to create envelope estimates, and 3) employing a sifting process to separate frequency components. This signal processing technique decomposes signals into different frequency components while extracting the physical meaning of each component. The implementation typically requires functions for extremum detection, spline interpolation for envelope fitting, and convergence criteria for the sifting iteration. By applying LMD to signals, we can better understand signal characteristics and extract more useful information. The method is therefore widely used across various fields including audio processing (where it helps isolate harmonic components), image processing (for texture analysis), and biomedical engineering (particularly in ECG and EEG signal analysis for feature extraction).