Alterations in oculomotor performance are among the first observable physical alterations during presymptomatic stages of Huntington's disease (HD). Quantifiable measurements of oculomotor performance have been studied as possible markers of disease status and progression in presymptomatic and early symptomatic stages of HD, on the basis of traditional analysis methods. Whether oculomotor performance can be used to classify individuals according to HD disease stage has yet to be explored via the application of machine-learning methods. In the present study, we report the application of the support vector machine (SVM) algorithm to oculomotor features pooled from a four-task psychophysical experiment. We were able to automatically distinguish control participants from presymptomatic HD (pre-HD) participants with an accuracy of 73.47 %, a sensitivity of 74.31 %, and a specificity of 72.64 %; to distinguish control participants from HD patients with an accuracy of 81.84 %, a sensitivity of 76.19 %, and a specificity of 87.48 %; and to distinguish pre-HD participants from HD patients with an accuracy of 83.54 %, a sensitivity of 92.62 %, and a specificity of 74.45 %. These results demonstrate that the application of supervised classification methods to oculomotor features is a valuable and promising approach to the automatic detection of disease stage in HD.
Keywords: Classification; Huntington; Oculomotor; Saccade; Support vector machines.