Table of Contents
Advances in Artificial Neural Systems
Volume 2013 (2013), Article ID 972412, 11 pages
http://dx.doi.org/10.1155/2013/972412
Research Article

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

1Computer Science , School of Information Sciences, University of Tampere, 33014 Tampere, Finland
2Department of Otorhinolaryngology & Head and Neck Surgery, University of Helsinki and Helsinki University Central Hospital, HUS, 00029 Helsinki, Finland

Received 30 June 2013; Revised 27 September 2013; Accepted 18 October 2013

Academic Editor: Christian Mayr

Copyright © 2013 Martti Juhola et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.