Wavelet decomposition of the blink reflex R2 component enables improved discrimination of multiple sclerosis

Objectives: The blink reflex R2 component was subjected to wavelet decomposition for time feature extraction in order to classify the functional status of patients with multiple sclerosis. Methods: The blink reflex was recorded bilaterally with unilateral stimulation of the supra-orbital nerve in 37 normal subjects and 9 patients with multiple sclerosis (MS). The late component, R2, was subjected to time-frequency decomposition using the Daubechies-4 wavelet. Using the time-frequency coefficients, the mean time of the R2 wave as well as the standard deviation of the R2 interval were calculated in each trial. The wavelet transform enables noise reduction by allowing selective use of frequency bands with high signal-to-noise ratio for time feature extraction; therefore automatic estimation of time parameters is robust. The distribution densities of the mean and the standard deviation of the R2 wave duration for the set of trials for each subject were computed. Results: An appreciable difference in the densities of the two parameters extracted in the wavelet domain was seen between normals and patients. This is in contrast to the onset latency of R2 which poorly discriminates MS patients from normals. Conclusion: The results suggest that the mean and standard deviation of the R2-time robustly estimated using wavelet decomposition can be used to support clinical diagnosis in tracking the functional status of patients with diseases like multiple sclerosis.