Abstract
In this paper we evaluate the use of the machine learning algorithms Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART) and Naive Bayes (NB) 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 show accuracies over 97%, recalls over 97% and precisions over 91% for the four models evaluated.
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References
Alcalá-Fdez, J., Sánchez, L., García, S., del Jesús, M.J., Ventura, S., Garrell, J.M., Otero, J., Romero, C., Bacardit, J., Rivas, V.M., et al.: KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Computing 13(3), 307–318 (2009). http://link.springer.com/article/10.1007/s00500-008-0323-y
Becerra, R., Joya, G., Bermúdez, R.V.G., Velázquez, L., Rodríguez, R., Pino, C.: 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.) IWANN 2013, Part II. LNCS, vol. 7903, pp. 115–123. Springer, Heidelberg (2013)
Becerra Garca, R.A.: Plataforma de procesamiento de electrooculogramas. Caso de estudio: pacientes con Ataxia Espinocerebelosa Tipo 2. Master en matemtica aplicada e informtica para la administracin, Universidad de Holgun, Holgun (2013)
Bonnet, C., Hanuka, J., Rusz, J., Rivaud-Pchoux, S., Sieger, T., Majerov, V., Serranov, T., Gaymard, B., Rika, E.: Horizontal and vertical eye movement metrics: What is important?. Clinical Neurophysiology 124(11), 2216–2229 (2013). http://www.clinph-journal.com/article/S1388245713006378/abstract
Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and regression trees. CRC Press (1984)
Bulling, A., Ward, J., Gellersen, H., Troster, G.: Eye Movement Analysis for Activity Recognition Using Electrooculography. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(4), 741–753 (2011)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995). http://link.springer.com/article/10.1007/BF00994018
Daye, P.M., Optican, L.M.: Saccade detection using a particle filter. Journal of Neuroscience Methods 235, 157–168 (2014)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research 7, 1–30 (2006). http://dl.acm.org/citation.cfm?id=1248548
Hunter, J.D.: Matplotlib: A 2D Graphics Environment. Computing in Science and Engineering 9(3), 90–95 (2007)
Inchingolo, P., Spanio, M.: On the Identification and Analysis of Saccadic Eye Movements-A Quantitative Study of the Processing Procedures. IEEE Transactions on Biomedical Engineering BME 32(9), 683–695 (1985)
Jones, E., Oliphant, T., Peterson, P.: SciPy: Open source scientific tools for Python (2001). http://www.scipy.org/, http://www.citeulike.org/group/2018/article/2644428
Juhola, M.: Median filtering is appropriate to signals of saccadic eye movements. Computers in Biology and Medicine 21(1–2), 43–49 (1991). http://www.sciencedirect.com/science/article/pii/0010482591900347
Juhola, M., Jäntti, V., Pyykkö, I., Magnusson, M., Schalén, L., Åkesson, M.: Detection of saccadic eye movements using a non-recursive adaptive digital filter. Computer Methods and Programs in Biomedicine 21(2), 81–88 (1985). http://www.sciencedirect.com/science/article/pii/0169260785900665
Magosso, E., Provini, F., Montagna, P., Ursino, M.: A wavelet based method for automatic detection of slow eye movements: A pilot study. Medical Engineering & Physics 28(9), 860–875 (2006). http://www.sciencedirect.com/science/article/pii/S1350453306000154
Marmor, M.F., Zrenner, E.: Standard for clinical electro-oculography. Documenta Ophthalmologica 85(2), 115–124 (1993). http://dx.doi.org/10.1007/BF01371127
McKinney, W.: Data Structures for Statistical Computing in Python, pp. 51–56 (2010). http://conference.scipy.org/proceedings/scipy2010/mckinney.html
Nystrm, M., Holmqvist, K.: An adaptive algorithm for fixation, saccade, and glissade detection in eyetracking data. Behavior Research Methods 42(1), 188–204 (2010). http://link.springer.com/article/10.3758/BRM.42.1.188
Oliphant, T.E.: Python for scientific computing. Computing in Science & Engineering 9(3), 10–20 (2007). http://scitation.aip.org/content/aip/journal/cise/9/3/10.1109/MCSE.2007.58
Otero-Millan, J., Castro, J.L.A., Macknik, S.L., Martinez-Conde, S.: Unsupervised clustering method to detect microsaccades. Journal of Vision 14(2), 18 (2014)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011). http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html
Pettersson, K., Jagadeesan, S., Lukander, K., Henelius, A., Hæggström, E., Müller, K.: Algorithm for automatic analysis of electro-oculographic data. Biomedical Engineering Online 12(1), 110 (2013)
Prez, F., Granger, B.E.: IPython: A System for Interactive Scientific Computing. Computing in Science & Engineering 9(3), 21–29 (2007). http://scitation.aip.org/content/aip/journal/cise/9/3/10.1109/MCSE.2007.53
Rodrguez-Labrada, R., Velzquez-Prez, L.: Eye movement abnormalities in spinocerebellar ataxias. Spinocerebellar Ataxias, pp. 59–76. Intech, Rijeka (2012)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall, Upper Saddle River (December (2009)
Salvucci, D.D., Goldberg, J.H.: Identifying fixations and saccades in eye-tracking protocols. In: ETRA 2000, pp. 71–78. ACM, New York (2000). http://doi.acm.org/10.1145/355017.355028
Silverman, B.W., Jones, M.C.: An Important Contribution to Nonparametric Discriminant Analysis and Density Estimation. International Statistical Review / Revue Internationale de Statistique 57(3), 233–238 (1989). http://www.jstor.org/stable/1403796
Tigges, P., Kathmann, N., Engel, R.R.: Identification of input variables for feature based artificial neural networks-saccade detection in EOG recordings. International Journal of Medical Informatics 45(3), 175–184 (1997). http://www.sciencedirect.com/science/article/pii/S1386505697000427
Velzquez-Prez, L., Rodrguez-Labrada, R., Garca-Rodrguez, J.C., Almaguer-Mederos, L.E., Cruz-Mario, T., Laffita-Mesa, J.M.: A comprehensive review of spinocerebellar ataxia type 2 in cuba. The Cerebellum 10(2), 184–198 (2011). http://link.springer.com/article/10.1007/s12311-011-0265-2
Witten, I.H., Frank, E., Hall, M.A.: Data Mining. Practical Machine Learning Tools and Techniques, 3 edn. Morgan Kaufmann (2011)
Wyatt, H.J.: Detecting saccades with jerk. Vision Research 38(14), 2147–2153 (1998). http://www.sciencedirect.com/science/article/pii/S0042698997004100
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Becerra-García, R. et al. (2015). Non Spontaneous Saccadic Movements Identification in Clinical Electrooculography Using Machine Learning. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9095. Springer, Cham. https://doi.org/10.1007/978-3-319-19222-2_5
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