Characterizing Parkinson's desease using EMG fractional linear prediction

In this paper, we propose a modeling technique for the surface electromyographic (sEMG) signals based on the fractional linear prediction (FLP). To our knowledge, this is the first time application (use) of the FLP modeling to sEMG Data. This study is motivated by the ability of FLP modeling for characterizing a waveform with a reduced set of parameters. The FLP is applied on real sEMG data recorded on the Soleus muscles under walking conditions and preliminary results are obtained. The dynamics of FLP coefficients for persons suffering from Parkinson's disease (PD) were shown to be lower than those for healthy subjects. This suggests less adjustment possibilities in the neuromuscular response of the PD subjects compared to the healthy subjects. Perspectives are the evaluation of fractal components of nonstationary EMG data in connection with FLP evolution.