Feature Extraction for 1D Signal Time Series Using MATLAB

This article presents MATLAB-based implementation approaches for feature extraction from 1D signal time series, focusing on Independent Component Analysis (ICA) and wavelet packet decomposition methods. During feature extraction, signal preprocessing such as denoising or filtering can be applied first using functions like `filter()` or wavelet denoising techniques. The ICA method, implementable through MATLAB's `fastica()` function or the Signal Processing Toolbox, extracts independent components from mixed signals to obtain more representative features by separating source signals from their mixtures. Simultaneously, the wavelet packet-based approach utilizes functions like `wpdec()` for wavelet packet decomposition and `wprcoef()` for reconstruction coefficients, enabling detailed signal analysis across different frequency bands. This method performs multi-level wavelet transform and decomposition to obtain sub-signals in various frequency ranges, allowing extraction of more detailed characteristic information through energy features or statistical parameters. Both methods, combined with proper preprocessing techniques, provide accurate and comprehensive feature representations for 1D time series analysis in MATLAB implementations.