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.
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.