Hilbert-Huang Transform and EEMD Decomposition - Signal Processing Implementation

This article discusses advanced signal processing methodologies, specifically focusing on the Hilbert-Huang Transform (HHT) and Ensemble Empirical Mode Decomposition (EEMD). These sophisticated techniques enable the extraction of Intrinsic Mode Functions (IMFs) from complex signals. The implementation typically involves sifting algorithms that iteratively extract oscillatory modes, followed by Hilbert spectral analysis for instantaneous frequency computation. We emphasize extracting the first four highest-energy IMFs, which often contain the most significant signal components. In practical code implementation, this process requires calculating the energy content of each IMF through integration of squared amplitude values. The HHT and EEMD methods are widely applied in signal processing domains, helping researchers better understand signal characteristics and extract meaningful information. Through algorithmic decomposition and spectral transformation, these techniques provide valuable insights for signal analysis applications. This article aims to provide readers with comprehensive understanding of these methodologies and enhance their knowledge of modern signal processing approaches.