Novel feature extraction method based on fuzzy entropy and wavelet packet transform for myoelectric control

In this paper, a novel feature extraction method based on the utilization of wavelet packet transform (WPT) and the concept of fuzzy entropy is presented. The method acts in steps, were in the first step the WPT is employed to generate a wavelet decomposition tree from which many features are extracted. In the second step, a new algorithm to compute the fuzzy entropy is developed and adopted as a measure of information content to judge on features suitability in classification, by setting a threshold and removing the features that fall under a certain threshold. In the third step, principle component analysis (PCA) is employed to reduce the dimensionality of the generated feature set. As an application, the new algorithm is employed in multifunction myoelectric control problem to prove its efficiency. Accurate results (99% accuracy) are acquired from using only a small subset of the original feature set generated by the wavelet tree. The obtained results indicate that the generated features are of maximum relevance and with minimum degree of redundancy.