  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.
