End-to-End Models for the Analysis of Pupil Size Variations and Diagnosis of Parkinson's Disease
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It is well known that a systematic analysis of the pupil size variations, recorded by means of an eye-tracker, is a rich source of information about a subject's cognitive state. In this work we present end-to-end models for the diagnosis of Parkinson's disease (PD) based on the raw pupil size signal. Long-range registration (10 minutes) of the pupil size were collected in scotopic conditions (complete darkness, 0 lux) on 21 healthy subjects and 15 subjects diagnosed with PD. 1-D convolutional neural network models are trained for classification of short-range sequences (10 to 60 seconds of registration). The model provides prediction with high average accuracy on a hold out test set. A temporal analysis of the model performance allowed the characterization of pupil's size variations in PD and healthy subjects during a resting state. Dataset and codes are released for reproducibility and benchmarking purposes.
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