1-D Convlutional Neural Networks for the Analysis of Pupil Size
Variations in Scotopic Conditions
- URL: http://arxiv.org/abs/2002.02383v2
- Date: Fri, 19 Jun 2020 15:49:51 GMT
- Title: 1-D Convlutional Neural Networks for the Analysis of Pupil Size
Variations in Scotopic Conditions
- Authors: Dario Zanca, Alessandra Rufa
- Abstract summary: 1-D convolutional neural network models are trained for classification of short-range sequences.
Model provides prediction with high average accuracy on a hold out test set.
- Score: 79.71065005161566
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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