Wavelet Decomposition for Wavelet Entropy Calculation and Signal Feature Analysis

MATLAB 793B 209 views 0 downloads 1 credits
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Resource Overview

Implementation of wavelet decomposition for entropy computation combined with wavelet packet techniques for signal feature extraction, specifically adapted for EEG signal analysis and characterization

Detailed Documentation

In this paper, we employ wavelet decomposition to calculate wavelet entropy and utilize wavelet packet technology for signal feature analysis, with particular focus on electroencephalogram (EEG) signal characteristics. These methodologies enable us to adapt to and analyze EEG signal features, thereby obtaining more detailed and comprehensive information. The implementation involves decomposing signals using wavelet transforms at multiple resolution levels, calculating energy distribution across frequency bands, and computing entropy measures to quantify signal complexity. For wavelet packet analysis, the algorithm performs adaptive frequency band partitioning to extract discriminative features from non-stationary biological signals like EEG.

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