Parametric classification of segments in ocular nystagmus.

Abstract

A new method for nystagmus classification, using system identification techniques, is presented. We formulate a system whose input is head position and whose output is eye position. We approximate this system with an autoregressive with exogenous input (ARX) 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 is produced that marks slow and fast phases as well as blinks and bad data segments. Tests with simulated and real data are presented and indicate that the segment classification is remarkably insensitive to recording noise and that it is more robust than previous techniques. Operator intervention is minimal. We expect the method to be applicable for all types of ocular nystagmus. Here, however, we illustrate our results only in the context of the vestibuloocular reflex (VOR). A discussion explains how this method can also be applied for optokinetic (OKN) or pursuit nystagmus.