MATLAB-Based Hilbert-Huang Transform Implementation

We present a complete MATLAB-based implementation of the Hilbert-Huang Transform (HHT) program. This implementation consists of several modular components, including data preprocessing, Hilbert transform, and Huang transform operations. The data preprocessing module handles data importation using MATLAB's file I/O functions, implements noise filtering algorithms (such as wavelet denoising or bandpass filters), and performs signal segmentation through windowing techniques to ensure input data quality. The Hilbert transform phase converts time series into analytic signals using MATLAB's built-in hilbert() function, which facilitates subsequent analytical processing by generating complex-valued signals containing instantaneous amplitude and phase information. The Huang transform, which forms the core of this implementation, employs the Empirical Mode Decomposition (EMD) algorithm to decompose signals into Intrinsic Mode Functions (IMFs) followed by Hilbert spectral analysis. This methodology effectively detects nonlinear and nonstationary characteristics in time series data through its adaptive decomposition capability. The program's completeness and precision, achieved through optimized MATLAB coding practices including proper boundary handling in EMD and accurate instantaneous frequency calculation, make it a powerful tool for time series research applications.