In this paper, we compare the performance of two different methods for the task of electrooculogram saccadic points classification in Patients with Ataxia SCA2: Multilayer Perceptrons (MLP) and Random Forest. First we segment the recordings of 6 subjects into ranges of saccadic and non-saccadic points as the basis of supervised learning. Then, we randomly select a set of cases based on the velocity profile near each selected point for training and validation purposes using percent split scheme. Obtained results show that both methods have similar performance in classification matter, and seem to be suitable to solve theproblem of saccadic point classification in electrooculographic records from subjects with Ataxia SCA2.
