Omer Colak

Measuring complexity of dynamical systems is
a mighty tool for electrophysiological signal processing.
There are plenty of entropies for estimating complexity
measure. Approximate entropy (ApEn), sample entropy
(SampEn), fuzzy entropy (FuzzyEn), wavelet entropy (WE)
and wavelet packet entropy (WPE) was used for surface
EMG feature extraction for face movements classification.
Linear discriminant analysis (LDA) selected for
classification. Classification performance was determined by
mean square error (MSE) for different window sizes. Fuzzy
entropy is the most robust and succeeding method of them.
Principal component analysis used to improve classification
performance however just results of approximate entropy
feature were refined. MSE of wavelet entropy and wavelet
packet entropy are also decent methods for this classification
problem.