Glaucoma classification through SSVEP derived ON- and OFF-pathway features

Recent evidence from small animal models and human electrophysiology suggests that the OFF-pathway is more vulnerable to glaucomatous insult than the ON-pathway. Thus, OFF-pathway based measurements of visual function may be useful in the diagnosis of Glaucoma. The steady-state visually evoked potential (SSVEP) can be used to non-invasively make such functional measurements. Here, we examine whether OFF- and ON-pathway biasing SSVEP measurements differently predict glaucoma diagnosis using a large cohort of 98 glaucoma patients and 71 controls. Using both a logistic regression with k-fold cross-validation and a random forest classifier, we show that OFF-pathway biasing features produce a small improvement in predictive accuracy over ON-pathway biasing features. However, despite our inclusion of many more response features and the retention of both participants' eyes, our classifier did not perform as well as previous reports that used the isolated-check VEP. This is likely a result of the relatively small amount of data we collected for each participant, but may also be explained by the absence of any train-test splitting in preexisting work. Nevertheless, our results support further exploration of the diagnostic potential of OFF-pathway biasing functional biomarkers for glaucoma.