The detection of Freezing of Gait in Parkinson's disease patients using EEG signals based on Wavelet decomposition

Freezing of Gait (FOG) is one of the most disabling gait disturbances of Parkinson's disease (PD). The experience has often been described as feeling like their feet have been glued to the floor while trying to walk and as such it is a common cause of falling in PD patients. In this paper, EEG subbands Wavelet Energy and Total Wavelet Entropy were extracted using the multiresolution decomposition of EEG signal based on the Discrete Wavelet Transform and were used to analyze the dynamics in the EEG during freezing. The Back Propagation Neural Network classifier has the ability to identify the onset of freezing of PD patients during walking using these features with average values of accuracy, sensitivity and specificity are around 75 %. This results have proved the feasibility of utilized EEG in future treatment of FOG. © 2012 IEEE