The Classification of Valid and Invalid Beats of Three-Dimensional Nystagmus Eye Movement Signals Using Machine Learning Methods

Nystagmus recordings frequently include eye blinks, noise, or other corrupted segments that, with the exception of noise, cannot be dampened by filtering. We measured the spontaneous nystagmus of 107 otoneurological patients to form a training set for machine learning-based classifiers to assess and separate valid nystagmus beats from artefacts. Video-oculography was used to record three-dimensional nystagmus signals. Firstly, a procedure was implemented to accept or reject nystagmus beats according to the limits for nystagmus variables. Secondly, an expert perused all nystagmus beats manually. Thirdly, both the machine and the manual results were united to form the third variation of the training set for the machine learning-based classification. This improved accuracy results in classification; high accuracy values of up to 89% were obtained. 1. Introduction Nystagmus is a repetitive, reflexive eye movement that may be congenital, induced physiologically by vestibular or optokinetic stimuli, or occur spontaneously in vestibular patients. It is formed by to-and-fro, saw tooth-like beats that can be recorded in the horizontal, vertical, and torsional directions. Older recording techniques, such as electrooculography (EOG), recorded only horizontal and vertical movements. Video-oculography (VOG) using two small video cameras, one for each eye, also enables the recording of torsional movements. A nystagmus beat contains a slow phase followed by a shorter fast phase that returns the eyes in the opposite direction (Figure 1). Nystagmus beats are repetitive, but their configuration changes often in the course of even a short measurement. Figure 1: (a) A nystagmus beat from an ideal eye movement signal, including no noise or corruptions, contains a slow phase immediately followed by its shorter fast phase. Their amplitudes and durations are basic nystagmus variables. Durations are computed from the beginnings and of slow and fast phases and the end . Amplitudes are the differences from , , and . (b) The example shows a more realistic nystagmus beat including slightly less smooth nystagmus phases. For the analysis of nystagmus signals, it is important to distinguish slow and fast phases and separate noisy or corrupted signal locations so that they do not impair values of the nystagmus variables to be computed. The slow phase characteristics are significant for the diagnostics of vestibular neuritis, positional vertigo, acoustic neuroma, and Ménière’s disease, and the fast phases returning the eyes to the centre are of central origin (the brain). Our present