An automatic non-invasive method for Parkinson's disease classification

In this work a wavelet transform approach is explored to classify Parkinson's subjects and healthy subjects using their gait cycle variables. The results show that the proposed method can efficiently extract relevant features from the different levels of the wavelet towards the classification of Parkinson's and healthy subjects and thus, the present work is a potential candidate for the automatic noninvasive neurodegenerative disease classification.