Relationship between Glaucoma and Complexity Measures of the Electroretinogram

Glaucoma is one of the leading causes of vision loss in the world. Early diagnosis is essential to prevent irreversible vision loss. Such early diagnosis requires the use of tests of visual function rather than tests of anatomical structure. Existing tests of visual function can suffer from the use of bulky expensive equipment and poor subject performance. In this work we describe a novel analysis of the electroretinogram to extract features of complexity and detect glaucoma. Cohorts of participants with and without glaucoma had electroretinogram (ERG) testing. The stimulus used to evoke the signal was that used for the Photopic Negative Response (PhNR), a response known to be reduced in glaucoma. There were 18 control and 21 glaucoma participants. Significant differences were found in PhNR amplitude −5.5 mV vs −3.6mV (p<0.01), Shannon Entropy 2.99 vs 2.80 (p<0.01) and Higuch Fractal Dimension 1.068 vs 1.082 (p<0.02). Differences in box count and Katz Fractal Dimension and Sample Entropy were not significant. These complexity measures may have potential for machine learning classifiers for glaucoma.