Spike removal through multiscale wavelet and entropy analysis of ocular motor noise: a case study in patients with cerebellar disease.

Wavelet decomposition of ocular motor signals was investigated with a view to its use for noise analysis and filtering. Ocular motor noise may be physiological, depending on brain activities, or experimental, depending on the eye recording machine, head movements and blinks. Experimental noise, such as spikes, must be removed, preserving noise due to neuro-physiological activities. The proposed method uses wavelet multiscale decomposition to remove spikes and optimizes the procedure by means of the covariance of the eye signals. To measure the noise on eye motor control, we used the wavelet entropy. The method was tested on patients with cerebellar disorders and healthy subjects. A significant difference in wavelet entropy was observed, indicating this quantity as a valuable measure of physiological motor noise.