Analysis of Eye Tracking Data from Parkinson’s Patients using Machine Learning

Abstract: Parkinson’s disease is a brain disorder associated with reduced dopamine levels in the brain, affecting cognition and motor control in the human brain. One of the motor controls that can be affected is eye movements and can therefore be critically affected in patients with Parkinson’s disease. Eye movement can be measured using eye trackers, and this data can be used for analyzing the eye movement characteristics in Parkinson’s disease. The eye movement analysis provides the possibility of diagnostics and can therefore lead to further insights into Parkinson’s disease. In this thesis, feature extraction of clinical relevance in diagnosing Parkinson’s patients from eye movement data is studied. We have used an autoencoder (AE) constructed to learn micro and macro-scaled representation for eye movements and constructed three different models. Learning of the AEs was evaluated using the F1 score, and differences were statistically assessed using the Wilcoxon sign rank test. Extracted features from data based on patients and healthy subjects were visualized using t-SNE. Using the extracted features, we have measured differences in features using cosine and Mahalanobis distances. We have furthermore clustered the features using fuzzy c-means. Qualities of the generated clusters were assessed by F1-score, fuzzy partition coefficient, Dunn’s index and silhouette index. Based on successful tests using a test data set of a previous publication, we believe that the network used in this thesis has learned to represent natural eye movement from subjects allowed to move their eye freely. However, distances, visualizations, clustering all suggest that latent representations from the autoencoder do not provide a good separation of data from patients and healthy subjects. We, therefore, conclude that a micro-macro autoencoder does not suit the purpose of generating a latent representation of saccade movements of the type used in this thesis.