Application of EMD and EEMD Transform in Signal Denoising

Application of EMD and EEMD Transform in Signal Denoising (includes EEMD implementation code). EMD (Empirical Mode Decomposition) and EEMD (Ensemble Empirical Mode Decomposition) are widely used methods in signal processing for effective noise removal. EMD is an adaptive data-driven signal decomposition technique that breaks down signals into multiple Intrinsic Mode Functions (IMFs) for subsequent reconstruction. The algorithm operates iteratively by identifying local extrema, constructing envelopes via spline interpolation, and extracting IMFs until the residual becomes monotonic. EEMD enhances the standard EMD by introducing controlled noise and ensemble averaging, which improves handling of nonlinear and non-stationary signals. The EEMD implementation typically involves adding white noise to the original signal, performing multiple EMD decompositions, and averaging the resulting IMFs to suppress mode mixing artifacts. These methods find extensive applications in signal processing, filtering, vibration analysis, and biomedical signal analysis. The provided EEMD code demonstrates practical implementation with parameters for noise amplitude and ensemble size customization.