Signal Denoising Using Empirical Mode Decomposition (EMD)

In the given text, the topic being discussed is signal denoising using the Empirical Mode Decomposition (EMD) technique. Signal denoising is a crucial process in signal processing where the objective is to eliminate unwanted noise while preserving essential signal information. The EMD method operates by adaptively decomposing a signal into a set of oscillatory components called Intrinsic Mode Functions (IMFs) through a sifting process. From an implementation perspective, the EMD algorithm typically involves iterative steps: identifying local extrema, constructing upper and lower envelopes using spline interpolation, and extracting IMFs until a stopping criterion is met. For denoising applications, noise components are typically identified in the higher-order IMFs and can be removed through thresholding techniques or selective reconstruction. By analyzing and attenuating noise-contributed IMFs, a cleaner denoised signal is obtained through reconstruction of the remaining IMFs. Key implementation considerations include boundary condition handling, sifting iteration limits, and appropriate threshold selection strategies for different noise types.