No abstract

In this work we consider a linesearch globalization of the local primal-dual interiorpoint Newton method for nonlinear programming recently introduced by El-Bakry, Tapia, Tsuchiya and Zhang. Our linesearch uses a merit function that is a modification of the standard augmented Lagrangian function and a weak notion of centrality.We establish a global convergence theory and present rather promising numerical experimentation.

In this paper we discuss an efficient methodology for the characterization of Microelectrode Recordings (MER) obtained during deep brain stimulation surgery for Parkinson's disease using Support Vector Machines and present the results of a preliminary study. The methodology is based in two algorithms: (1) an algorithm extracts multiple computational features from the microelectrode neurophysiology, and (2) integrates them in the support vector machines algorithm for classification. It has been applied to the problem of the recognition of subcortical structures: thalamus nucleus, zona incerta, subthalamic nucleus and substantia nigra. The SVM (support vector machines) algorithm performed quite well achieving 99.4% correct classification. In conclusion, the use of a computer-based system, like the one described in this paper, is intended to avoid human subjectivity in the localization of the subcortical structures and mainly the subthalamic nucleus (STN) for neurostimulation.

No abstract