Automating, analyzing and improving pupillometry with machine learning algorithms

Kalmár, György and Büki, Alexandra and Kékesi, Gabriella and Horváth, Gyöngyi and Nyúl, László G.: Automating, analyzing and improving pupillometry with machine learning algorithms. In: Acta cybernetica, (24) 2. pp. 197-209. (2019)

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The investigation of the pupillary light reflex (PLR) is a well-known method to provide information about the functionality of the autonomic nervous system. Pupillometry, a non-invasive technique, was applied to study the PLR alterations in a new, schizophrenia-like rat substrain, named WISKET. The pupil responses to light impulses were recorded with an infrared camera; the videos were automatically processed and features were extracted from the pupillograms. Besides the classical statistical analysis (ANOVA), feature selection and classification were applied to reveal the significant differences in the PLR parameters between the control and WISKET animals. Based on these results, the disadvantages of this method were analyzed and the measurement setup was redesigned and improved. The pupil segmentation method has also been adapted to the new videos. 2564 images were annotated manually and used to train a fully-convolutional neural network to produce pupil mask images. The method was evaluated on 329 test images and achieved 4% median relative error. With the new setup, the pupil detection became reliable and the new data acquisition offers robustness to the experiments.

Item Type: Article
Journal or Publication Title: Acta cybernetica
Date: 2019
Volume: 24
Number: 2
ISSN: 0324-721X
Page Range: pp. 197-209
Uncontrolled Keywords: Pupillometria, Algoritmus
Additional Information: Bibliogr.: p. 206-209. ; összefoglalás angol nyelven
Date Deposited: 2020. Mar. 17. 10:45
Last Modified: 2020. Apr. 01. 09:11

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