IOVS European Journal of Biochemistry
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Originally published In Press as doi:10.1167/iovs.08-1710 on April 30, 2008
 (Investigative Ophthalmology and Visual Science. 2008;49:3413-3423.)
© 2008 by The Association for Research in Vision and Ophthalmology, Inc.
DOI:  10.1167/iovs.08-1710
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Wavelet Analysis in Infantile Nystagmus Syndrome: Limitations and Abilities

Larry A. Abel,1 Zhong I. Wang,2,3 and Louis F. Dell'Osso2,3,4

1From the Department of Optometry and Vision Sciences, University of Melbourne, Melbourne, Australia; the 2Daroff-Dell'Osso Ocular Motility Laboratory, Louis Stokes Cleveland Department of Veterans Affairs Medical Center and Case Medical School, Cleveland, Ohio; and the 3Departments of Biomedical Engineering and 4Neurology, Case Western Reserve University and University Hospitals of Cleveland, Cleveland, Ohio.

PURPOSE. To investigate the proper usage of wavelet analysis in infantile nystagmus syndrome (INS) and determine its limitations and abilities.

METHODS. Data were analyzed from accurate eye-movement recordings of INS patients. Wavelet analysis was performed to examine the foveation characteristics, morphologic characteristics and time variation in different INS waveforms. Also compared were the wavelet analysis and the expanded nystagmus acuity function (NAFX) analysis on sections of pre- and post-tenotomy data.

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 used. The limitations of wavelet analysis outweighed its abilities in INS waveform-characteristic examination.






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