Machine learning algorithms on eye tracking trajectories to classify patients with spatial neglect.

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Serval ID
serval:BIB_C50F1D3A69E8
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Machine learning algorithms on eye tracking trajectories to classify patients with spatial neglect.
Journal
Computer methods and programs in biomedicine
Author(s)
Franceschiello B., Noto T.D., Bourgeois A., Murray M.M., Minier A., Pouget P., Richiardi J., Bartolomeo P., Anselmi F.
ISSN
1872-7565 (Electronic)
ISSN-L
0169-2607
Publication state
Published
Issued date
06/2022
Peer-reviewed
Oui
Volume
221
Pages
106929
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Abstract
Eye-movement trajectories are rich behavioral data, providing a window on how the brain processes information. We address the challenge of characterizing signs of visuo-spatial neglect from saccadic eye trajectories recorded in brain-damaged patients with spatial neglect as well as in healthy controls during a visual search task.
We establish a standardized pre-processing pipeline adaptable to other task-based eye-tracker measurements. We use traditional machine learning algorithms together with deep convolutional networks (both 1D and 2D) to automatically analyze eye trajectories.
Our top-performing machine learning models classified neglect patients vs. healthy individuals with an Area Under the ROC curve (AUC) ranging from 0.83 to 0.86. Moreover, the 1D convolutional neural network scores correlated with the degree of severity of neglect behavior as estimated with standardized paper-and-pencil tests and with the integrity of white matter tracts measured from Diffusion Tensor Imaging (DTI). Interestingly, the latter showed a clear correlation with the third branch of the superior longitudinal fasciculus (SLF), especially damaged in neglect.
The study introduces new methods for both the pre-processing and the classification of eye-movement trajectories in patients with neglect syndrome. The proposed methods can likely be applied to other types of neurological diseases opening the possibility of new computer-aided, precise, sensitive and non-invasive diagnostic tools.
Keywords
Algorithms, Diffusion Tensor Imaging, Eye-Tracking Technology, Humans, Machine Learning, Perceptual Disorders/diagnosis, Bio-markers, Deep networks, Diffusion tensor imaging, Eye-tracking, Machine learning, Neglect, Structural lesion
Pubmed
Web of science
Funding(s)
Swiss National Science Foundation / Projects / 169206
Create date
05/07/2022 8:15
Last modification date
13/01/2024 7:09
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