Non spontaneous saccadic movements identification in clinical electrooculography using machine learning Roberto Becerra-Gar´ ıa 1 , Rodolfo Garc´ ıa-Berm´ udez 3 , Gonzalo Joya-Caparr´ os 2 , Abel Fern´ andez-Higuera 1 , Camilo Vel´ azquez-Rodr´ ıguez 1 , Michel Vel´ azquez-Mari˜ no 1 , Franger Cuevas-Beltr´ an 1 , Francisco Garcia-Lagos 2 , and Roberto Rodr´ ıguez-Labrada 4 1 Universidad de Holgu´ ın, Grupo de Procesamiento de Datos Biom´ edicos (GPDB), Holgu´ ın, Cuba idertator,afernandezh,cvelazquezr,mvelazquez@facinf.uho.edu.cu, fcuevas@facii.uho.edu.cu 2 Universidad de M´alaga, M´ alaga, Espa˜ na gjoya@uma.es, lagos@dte.uma.es 3 Universidad Laica Eloy Alfaro de Manab´ ı, Facultad de Inform´ atica, Manta, Ecuador rodolfo.garcia@live.uleam.edu.ec 4 Centro para la Investigaci´ on y Rehabilitaci´ on de las Ataxias Hereditarias, Holgu´ ın, Cuba roberto@ataxia.hlg.sld.cu Abstract. In this paper we evaluate the use of the machine learning algorithms Support Vector Machines, K-Nearest Neighbors, CART de- cision trees and Naive Bayes to identify non spontaneous saccades in clinical electrooculography tests. Our approach tries to solve problems like the use of manually established thresholds present in classical meth- ods like identification by velocity threshold (I-VT) or identification by dispersion threshold (I-DT). We propose a modification to an adaptive threshold estimation algorithm for detecting signal impulses without the need of any user input. Also, a set of features were selected to take ad- vantage of intrinsic characteristics of clinical electrooculography tests. The models were evaluated with signals recorded to subjects affected by Spinocerebellar Ataxia type 2 (SCA2). Results obtained by the algorithm shows accuracies over 97%, recalls over 97% and precisions over 91% for the four models evaluated. Keywords: Saccade identification, clinical electrooculography, classifi- cation 1 Introduction The alteration of eye movements is one of the symptoms of many neurological diseases like Parkinsons syndrome, spinocerebellar ataxias or the Niemann-Pick