Extraction of Nystagmus Patterns from Eye-Tracker Data with Convolutional Sparse Coding
Signal Processing
2020-12-01 v1 Machine Learning
Abstract
The analysis of the Nystagmus waveforms from eye-tracking records is crucial for the clinicial interpretation of this pathological movement. A major issue to automatize this analysis is the presence of natural eye movements and eye blink artefacts that are mixed with the signal of interest. We propose a method based on Convolutional Dictionary Learning that is able to automaticcaly highlight the Nystagmus waveforms, separating the natural motion from the pathological movements. We show on simulated signals that our method can indeed improve the pattern recovery rate and provide clinical examples to illustrate how this algorithm performs.
Cite
@article{arxiv.2011.14962,
title = {Extraction of Nystagmus Patterns from Eye-Tracker Data with Convolutional Sparse Coding},
author = {Clément Lalanne and Maxence Rateaux and Laurent Oudre and Matthieu Robert and Thomas Moreau},
journal= {arXiv preprint arXiv:2011.14962},
year = {2020}
}