Signal-dependent wavelets for electromyogram classification.
In the study, an efficient method to perform supervised classification of surface electromyogram (EMG) signals is proposed. The method is based on the choice of a relevant representation space and its optimisation with respect to a training set. As EMG signals are the summation of compact-support waveforms (the motor unit action potentials), a natural tool for their representation is the discrete dyadic wavelet transform. The feature space was thus built from the marginals of a discrete wavelet decomposition. The mother wavelet was designed to minimise the probability of classification error estimated on the learning set (supervised classification). As a representative example, the method was applied to simulate surface EMG signals generated by motor units with different degrees of short-term synchronisation. The proposed approach was able to distinguish surface EMG signals with degrees of synchronisation that differed by 10%, with a misclassification rate of 8%. The performance of a spectral-based classification (error rate approximately 33%) and of the classification with Daubechies wavelet (21%) was significantly poorer than with the proposed wavelet optimisation. The method can be used for a number of different application fields of surface EMG classification, as the feature space is adapted to the characteristics of the signal that discriminate between classes.
Maitrot
A
A
Institut de Recherche en Communication et Cybernétique de Nantes, France.
Lucas
M F
MF
Doncarli
C
C
Farina
D
D
D000200:Action Potentials / Q000502:physiology; D004576:Electromyography / Q000145:classification / Q000379:methods*; D006801:Humans; D009046:Motor Neurons / Q000502:physiology; D012815:Signal Processing, Computer-Assisted