1-D Convlutional Neural Networks for the Analysis of Pupil Size Variations in Scotopic Conditions
This work addresses the need for automated, data-driven methods in pupil analysis for medical diagnostics, though it is incremental as it adapts existing neural network techniques to a specific domain.
The authors tackled the problem of analyzing pupil size variations for cognitive state assessment by applying 1-D convolutional neural networks directly to raw pupil data, achieving high average accuracy in classifying Parkinson's disease patients versus healthy controls in scotopic conditions.
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 arousal and cognitive state. Current methods for pupil analysis are limited to descriptive statistics, struggle in handling the wide inter-subjects variability and must be coupled with a long series of pre-processing signal operations. In this we present a data-driven approach in which 1-D Convolutional Neural Networks are applied directly to the raw pupil size data. To test its effectiveness, we apply our method in a binary classification task with two different groups of subjects: a group of elderly patients with Parkinson disease (PDs), a condition in which pupil abnormalities have been extensively reported, and a group of healthy adults subjects (HCs). Long-range registration (10 minutes) of the pupil size were collected in scotopic conditions (complete darkness, 0 lux). 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. Dataset and codes are released for reproducibility and benchmarking purposes.