Improved algorithm for classification of ocular nystagmus

An improved algorithm for classification of nystagmus was designed allowing the sorting of response segments even in severely non-linear patients and subjects with abnormally large phase shifts. The algorithm employs a model-based approach that was developed by Rey and Galiana. The improved classification algorithm consists of two essential stages. In the first stage the eye velocity response is classified to obtain initial estimates of the slow phase eye velocity intervals. In the second stage, the slow phase estimates are used to identify a response phase shift and nonlinearity, and compensate for their effects. Multiple tests on simulated data and experimental data obtained from clinical subjects are presented. The results of the tests demonstrate that the algorithm is able to analyze the patient data with a high accuracy even in the presence of noise, eye-blinks and other artifacts.