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

Objectives: The blink reflex R2 component was subjected to wavelet decompos ition for time feature extraction in order to classify the functional statu s of patients with multiple sclerosis. Methods: The blink reflex was recorded bilaterally with unilateral stimulat ion of the supra-orbital nerve in 37 normal subjects and 9 patients with mu ltiple sclerosis (MS). The late component, R2, was subjected to time-freque ncy decomposition using the Daubechies-4 wavelet. Using the time-frequency coefficients, the mean time of the R2 wave as well as the standard deviatio n of the R2 interval were calculated in each trial. The wavelet transform e nables noise reduction by allowing selective use of frequency bands with hi gh signal-to-noise ratio for time feature extraction; therefore automatic e stimation of time parameters is robust. The distribution densities of the m ean and the standard deviation of the R2 wave duration for the set of trial s for each subject were computed. Results: An appreciable difference in the densities of the two parameters e xtracted in the wavelet domain was seen between normals and patients. This is in contrast to the onset latency of R2 which poorly discriminates MS pat ients 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 supp ort clinical diagnosis in tracking the functional status of patients with d iseases like multiple sclerosis. (C) 2000 Elsevier Science Ireland Ltd. All rights reserved.