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L. A. Abel, Z. I. Wang, L. F. Dell'Osso; Wavelet Analysis in Infantile Nystagmus Syndrome: Limitations and Abilities. Invest. Ophthalmol. Vis. Sci. 2008;49(13):135. doi: https://doi.org/.
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© ARVO (1962-2015); The Authors (2016-present)
To investigate the utility of wavelet analysis identifying functionally important features of the waveforms seen in Infantile Nystagmus Syndrome (INS) and to determine its limitations and abilities.
We analyzed data obtained from accurate eye-movement recordings on INS patients. Continuous wavelet transforms using Haar wavelets were 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. All analyses were done in MATLAB using the wavelet toolbox and for the NAFX analysis, OMLAB software (OMtools, available from http://www.omlab.org).
Wavelet analysis showed that different INS waveforms were characterized by distinct wavelet spectra, as represented by the coefficient amplitudes. Changes in the waveforms across time were reflected in concurrent spectrum variations. However, wavelet analysis was not effective in detecting the perceptually important foveation periods, especially in a complex INS waveform. The NAFX, on the other hand, was a much more direct and sensitive method of evaluating post-surgical waveform changes.
Unlike Fourier analysis, wavelet analysis captures both the moment-by-moment and longer-term variability seen in INS. Wavelet spectra might be useful in the development of waveform classifiers, perhaps using neural networks. However, it was relatively ineffective in identifying those times when the target image was stably located on or near the fovea. 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. The limitations of wavelet analysis outweighed its abilities in identifying clinically important INS waveform characteristics.
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