Entropy analysis on vergence eye movement data for progressive lens acceptability in presbyopia

Presbyopia occurs from the aging process where accommodation decreases due to ciliary muscle weakness and loss of elasticity in the crystalline lens. People begin to have symptoms such as blurred vision, approximately at 40 years of age. To facilitate vision, presbyopes will wear multiple glasses, bifocals or progressive lenses. Progressive lenses can be difficult to adapt or learn how to wear. To date, patients are given little to no instruction on how to wear progressive lenses. The goal of this research is to predict who will have difficulties wearing the lenses and who will not. Other biomedical studies have shown that entropy which quantifies the energy content in physiological time-series data can be an indicator for how a person will adapt to changes or stress within the environment. Four degree convergence eye movement data (the inward turning of the eyes) from 10 people (4 Non-Presbyopes, 3 Presbyopes who have tried to wear the lenses but could not adjust, and 3 Presbyope who wear the lenses daily) were collected over 3 seconds and analyzed using sample entropy. Entropy values were obtained for each second of the response (0 tol, 1 to 2. and 2 to 3) and for the entire response (0 to 3 sec). Results were compared using a T-test and categorized using a clustering algorithm. The results showed that Sample Entropy classified the presbyopes who could adapt to the lenses compared to those who could not with 83% accuracy where the difference in entropy resulted in a P value of 0.1059.