Pupillometric recordings to detect glaucoma.

Objective. Glaucoma is the second cause of vision loss with early diagnosis having significantly better prognosis. We propose the use of hippus, the steady-state pupil oscillations, obtained from an eye-tracker for computerised detection of glaucoma.Approach. Pupillary data were recorded using a commercial eye-tracker device directly to the laptop. A total of 40 glaucoma patients and 30 age-matched controls were recruited for the study. The signals were de-noised, and the entropy of the steady-state oscillations was obtained for two light intensities, 34 and 100 cd m-2.Main results. The results show that at 100 cd m-2, there was significant difference (p < 0.05) between the sample entropy of the healthy eyes (0.55 ± 0.017) and glaucoma eyes (0.7 ± 0.034). The results at 34 cd m-2were also significantly different, though to a lesser extent.Significance. Entropy of the pupillary oscillations, or hippus, obtained using an eye-tracking device showed a significant difference between glaucoma and healthy eyes. The method used commercially available inexpensive hardware and thus has the potential for wide-scale deployment for computerized detection of glaucoma.

Sample entropy analysis of pupillary signals in glaucoma patients and control via light-induced pupillometry.

This paper evaluated the pupillary light reflex of glaucomatous eyes in the presence of constant lighting via light-induced pupillometry using sample entropy. The study used 20 patients and 15 controls, applied three different light intensities to their eyes, and recorded the behavior of the pupil. This study has validated that there is a difference in the entropy of pupillary data in glaucoma and healthy eyes. We concluded that entropy analysis is an excellent method to differentiate glaucoma eyes with the control through light-induced pupillometry. Hence, pupillometry has potential clinical applications in glaucoma investigation.