Towards high accuracy classification of MER signals for target localization in Parkinson's disease
In recent years Microelectrode recording (MER) analysis has proved to be a powerful localization tool of basal ganglia for Parkinson disease's treatment, especially the Subthalamic Nucleus (STN). In this paper, a signal-dependent method is presented for identification of the STN and other brain zones in Parkinsonian patients. The proposed method, refereed as optimal wavelet feature extraction method (OWFE), is constructed by lifting schemes (LS), which are a flexible and fast implementation of the wavelet transform (WT). The operators in the LS are optimized by means of Genetic Algorithms and Lagrange multipliers considering information contained in MER signals. Then a basic Bayesian classifier (LDC) is used to identify STN and other types of basal ganglia nuclei. The proposed method introduced several advantages from similar works reported in literature. First, the method is signal-dependent and non a priori information is required to decompose the MER signal. Second, the classification accuracy is mostly depended on the feature selection stage because it is not enhanced by elaborated classifiers such as support vector machines or hidden Markov models. Finally, the generalization property of the OWFE has been validated with two databases and different types of classifiers such as k-NN classifier and quadratic Bayesian classifier (QDC). Results have shown that proposed method is able to identify the STN with average accuracy superior than 97%.