4-Level Wavelet Packet Decomposition and Feature Vector Space Construction

This article provides a comprehensive examination of 4-level wavelet packet decomposition and the methodology for constructing feature vector spaces. We will systematically analyze each procedural step while incorporating MATLAB code demonstrations and algorithm explanations. The implementation approach involves utilizing wavelet packet decomposition functions (e.g., wpdec in MATLAB) to achieve 4-level signal decomposition, followed by feature extraction from terminal nodes using statistical measures like energy, entropy, and standard deviation. The resulting features are then organized into multidimensional vectors to form the feature space. Detailed explanations and practical examples ensure thorough comprehension of both theoretical concepts and practical implementation techniques. This resource aims to enhance readers understanding of wavelet packet analysis and feature space construction methodologies applicable to signal processing and pattern recognition applications.