Classification of human rotation test results using parametric modeling and multivariate statistics

The usefulness of vestibular testing is directly related to the accuracy of the test interpretations. Two factors, subjective analysis of large test data sets and failure to make appropriate age corrections, tend to reduce test accuracy. Correction of these problems can be accomplished by application of physiologically based models of vestibular function and multivariate classification techniques to the test data, thereby creating a more objective test interpretation procedure. Herein we report Our results on the use of this strategy for analysis of sinusoidal harmonic acceleration (SHA) test interpretation. For each patient, models reduce the large set of SHA test variables to three key parameters: asymptotic gain, vestibule-ocular reflex time constant, and bias. In addition, the new technique objectively adjusts these parameters for the patient's age. Finally, each patient's set of parameters are statistically classified as either normal or as unilateral peripheral deficit. Based on learning sets of 57 normals and 30 patients with a full unilateral peripheral deficit, this new technique resulted in a misclassification rate between the categories of normal and full unilateral loss of 3.4%, comparing favorably to the present method's misclassification rate between normal and abnormal of 13.8%. We also analyzed and classified a test group consisting of patients with possible partial unilateral deficits using the same classification function as the normal and full unilateral learning sets. Even though the classifier was not optimized for the partial group, results seemed favorable relative to the human interpreter. These results validate the accuracy and utility of physiological parametric models and multivariate statistical classification in SHA test interpretation.