  Eye-tracking is an accessible and non-invasive technology that provides
information about a subject's motor and cognitive abilities. As such, it has
proven to be a valuable resource in the study of neurodegenerative diseases
such as Parkinson's disease. Saccade experiments, in particular, have proven
useful in the diagnosis and staging of Parkinson's disease. However, to date,
no single eye-movement biomarker has been found to conclusively differentiate
patients from healthy controls. In the present work, we investigate the use of
state-of-the-art deep learning algorithms to perform Parkinson's disease
classification using eye-tracking data from saccade experiments. In contrast to
previous work, instead of using hand-crafted features from the saccades, we use
raw $\sim1.5\,s$ long fixation intervals recorded during the preparatory phase
before each trial. Using these short time series as input we implement two
different classification models, InceptionTime and ROCKET. We find that the
models are able to learn the classification task and generalize to unseen
subjects. InceptionTime achieves $78\%$ accuracy, while ROCKET achieves $88\%$
accuracy. We also employ a novel method for pruning the ROCKET model to improve
interpretability and generalizability, achieving an accuracy of $96\%$. Our
results suggest that fixation data has low inter-subject variability and
potentially carries useful information about brain cognitive and motor
conditions, making it suitable for use with machine learning in the discovery
of disease-relevant biomarkers.
