MATLAB Implementation of EMD Decomposition with Signal Analysis Features

The provided content can be enhanced with additional technical information. The EMD (Empirical Mode Decomposition) program finds extensive applications in signal processing and vibration analysis domains. From an implementation perspective, the MATLAB code typically utilizes the sifting process algorithm to decompose signals into intrinsic mode functions (IMFs), which involves iterative interpolation between local extrema and mean envelope calculation. Regarding the analytical components: instantaneous frequency computation often employs the Hilbert transform via MATLAB's hilbert() function, while instantaneous amplitude is derived from the analytic signal's magnitude. The marginal spectrum implementation involves integrating the Hilbert spectrum over time, providing frequency distribution statistics. The Hilbert spectrum visualization uses time-frequency representation techniques, commonly implemented through spectrogram functions or custom plotting routines. Practical application cases include ECG signal analysis in medical diagnostics (using IMFs to separate cardiac rhythm components) and vibration signal analysis in engineering (identifying machinery fault frequencies through Hilbert spectrum inspection). The MATLAB implementation typically involves key functions like emd() for decomposition, hilbert() for transform operations, and specialized plotting functions for spectrum visualization. This comprehensive approach helps users better understand EMD decomposition methodology and its associated analytical concepts through practical code implementation.