Parametric classification of segments in ocular nystagmus

A method for nystagmus classification that uses system identification techniques is presented. A system is formulated whose input is head position and whose output is eye position. This system is approximated with an autoregressive with exogenous input model which relates the input and output (transfer function) regardless of the temporal profile for the sensory stimulation. The system is then identified using a least squares criteria and three indicators are produced. From these a flag that marks slow and fast phases as well as blinks is produced. Bad data segment classification is remarkably insensitive to recording noise. The method is more robust than previous techniques. Operator intervention is minimal. The method should be applicable for all types of ocular nystagmus. Results are given in the context of the vestibuloocular reflex. How this method can be applied for optokinetic or pursuit nystagmus is discussed.