Forward autoregressive modeling for stride process analysis in patients with idiopathic Parkinson´s disease

In this paper, we derive forward autoregressive models to describe the stochastic process underlying stride interval series related to idiopathic Parkinson´s disease. The parameters of the autoregressive model that specify pole locations in the complex z-plane were used as dominant features for the separation of gait series of healthy subjects and patients with Parkinson´s disease. Based on the autoregressive parameters, linear discriminant analysis and support vector machines can provide classification accurate rates over 74% and area larger than 0.8 under the receiver operating characteristic curve. The results obtained show that the autoregressive model parameters could be useful for classification of stride series.