Wavelet Analysis in Infantile Nystagmus Syndrome: Limitations and Abilities

¹ Optometry & Vision Sciences, University of Melbourne, Carlton, Victoria, Australia
² Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States

^* To whom correspondence should be addressed. E-mail: label{at}unimelb.edu.au.

	Abstract

Objectives. To investigate the proper usage of wavelet analysis in Infantile Nystagmus Syndrome (INS) and determine its limitations and abilities. Materials and Methods. We analyzed data obtained from accurate eye-movement recordings on INS patients. Wavelet analysis was performed to examine the foveation characteristics, morphological characteristics and time variation in different INS waveforms. We also compared the wavelet analysis and the eXpanded Nystagmus Acuity Function (NAFX) analysis on sections of pre- and post-tenotomy data. We used the Matlab (The MathWorks, Natick, MA) wavelet toolbox for the analysis. All the NAFX analysis was done in MATLAB environment using OMLAB software (OMtools, downloadable from http://www.omlab.org). Results. Wavelet spectra showed some sensitivity to different features of INS waveforms and reflected their variations across time. However, wavelet analysis was not effective in detecting foveation periods, especially in a complicated INS waveform. NAFX, on the other hand, was a much more direct way of evaluating waveform changes after nystagmus treatments. Conclusions. Wavelet analysis is a tool that performs, with difficulty, some things that can be done faster and better by directly operating on the nystagmus waveform itself. It appears, however, to be insensitive to the subtle but visually important improvements brought about by INS therapies. Wavelet analysis may have a role in developing automated waveform classifiers where its time-dependent characterization of the waveform can be utilized. The limitations of wavelet analysis outweighed its abilities in INS waveform-characteristic examination.

Key Words: nystagmus, congenital nystagmus, eye movements, time series analysis, wavelet transform