Parallelization of an Algorithm for Automatic Classification of Medical Data

Abstract

In this paper, we present the optimization and parallelization of a state-of-the-art algorithm for automatic classification, in order to perform real-time classification of clinical data. The parallelization has been carried out so that the algorithm can be used in real time in standard computers, or in high performance computing servers. The fastest versions have been obtained carrying out most of the computations in Graphics Processing Units (GPUs). The algorithms obtained have been tested in a case of automatic classification of electroencephalographic signals from patients.