# Glaucoma Classification Through SSVEP-Derived ON- and OFF-Pathway Features

## TL;DR

This study explores how electrical signals from the eyes can help distinguish glaucoma patients from healthy individuals, focusing on signal phase and pathway differences.

## Contribution

The study introduces a novel approach to glaucoma classification by analyzing phase features and comparing ON- and OFF-pathway SSVEP responses.

## Key findings

- Signal phase features significantly outperformed amplitude features in classifying glaucoma patients.
- OFF-pathway amplitude features improved classification accuracy when signal-to-noise was high.
- Electrophysiological delay estimates should be considered in future diagnostic tools for glaucoma.

## Abstract

This work aims to evaluate the relative contribution of the amplitude and phase of both ON- and OFF-pathway biased steady-state visually evoked potentials (SSVEPs) to the classification of patients with glaucoma from healthy controls.

SSVEPs were recorded for sawtooth luminance increments (ON-biasing) and decrements (OFF-biasing), modulating at a temporal frequency of 2.73 Hz. SSVEP data from 98 adults with glaucoma and 71 controls were used to train a set of logistic regressions. Data were partitioned prior to training to investigate the relative contribution to classification for amplitude and phase features derived from ON- versus OFF-pathway stimulation.

We report moderate overall classification accuracy (area under the curve ∼0.7). Classification based solely on signal phase features significantly outperformed classification based solely on signal amplitude features. Classification using OFF-pathway biasing features produced a statistically significant improvement in classification only when training on signal amplitude features. This OFF advantage was not conserved in a dataset with low signal-to-noise eyes removed.

Our findings highlight the informational value of signal phase, a metric often omitted in applications of the SSVEP to glaucoma and other optic neuropathies. Additionally, our results suggest that OFF-pathway amplitude features may be less vulnerable to the limitations imposed by a low signal-to-noise ratio. However, they are not indicative of a gross difference in glaucoma classification performance between ON- and OFF-pathway biased features.

Electrophysiological estimates of visual signal delay should be considered in future clinical diagnostic tools as they make a material contribution to the classification of glaucomatous eyes.