Extraction of Nystagmus Patterns from Eye-Tracker Data with Convolutional Sparse Coding - Archive ouverte HAL Access content directly
Conference Papers Year : 2020

Extraction of Nystagmus Patterns from Eye-Tracker Data with Convolutional Sparse Coding

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.
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Dates and versions

hal-03022547 , version 1 (24-11-2020)

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Clément Lalanne, Maxence Rateaux, Laurent Oudre, Matthieu Robert, Thomas Moreau. Extraction of Nystagmus Patterns from Eye-Tracker Data with Convolutional Sparse Coding. EMBC 2020 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society, Jul 2020, Montreal / Virtuel, Canada. pp.928-931, ⟨10.1109/EMBC44109.2020.9175621⟩. ⟨hal-03022547⟩
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