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

Abstract

Nystagmus recordings frequently include eye blinks, noise, or other corrupted segments that, with the exception of noise, cannot be dampened by filtering. Wemeasured the spontaneous nystagmus of 107 otoneurological patients to forma training set for machine learning-based classifiers to assess and separate valid nystagmus beats from artefacts. Video-oculography was used to record threedimensional 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.