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

Abstract

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.