Abstract
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 seems to be suitable to solve the problem of saccadic point classification in electrooculographic records from subjects with Ataxia SCA2.
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Becerra, R., Joya, G., García Bermúdez, R.V., Velázquez, L., Rodríguez, R., Pino, C. (2013). Saccadic Points Classification Using Multilayer Perceptron and Random Forest Classifiers in EOG Recordings of Patients with Ataxia SCA2. In: Rojas, I., Joya, G., Cabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38682-4_14
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DOI: https://doi.org/10.1007/978-3-642-38682-4_14
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