RT Journal Article
T1 Non spontaneous saccadic movements identification in clinical electrooculography using machine learning
A1 Becerra-García, Roberto Antonio
A1 García-Bermúdez, Rodolfo
A1 Joya-Caparrós, Gonzalo
A1 Fernández-Higuera, Abel
A1 Velázquez-Rodríguez, Camilo
A1 Velazquez-Mariño, Michel
A1 Cuevas-Beltrán, Franger
A1 García-Lagos, Francisco
A1 Rodríguez-Labrada, Roberto
K1 Aprendizaje automático (Inteligencia artificial)
AB In this paper we evaluate the use of the machine learning algorithms Support Vector Machines, K-Nearest Neighbors, CART decision 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 methods 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 advantage 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.
PB Springer
YR 2015
FD 2015
LK http://hdl.handle.net/10630/10710
UL http://hdl.handle.net/10630/10710
LA eng
NO Universidad de Málaga, Campus de excelencia de Andalucía Tech
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 2 mar 2026