Development of robust video-oculography system for non-invasive autonomic nerve quantification

There are many scientific literatures those have reported a relationship between autonomic nerves activity and neuropathy, such as depression, Alzheimer's disease. The quantification of autonomic nerves is expected to serve as a tool for quantifying the severity of the disease or for its early detection. Video-oculography is known as a non-invasive and reliable procedure of estimating autonomic nerves activity by a measurement of pupil response to a light stimulus. However, current video-oculography only performs a thresholding to detect the pupil area and therefore measuring the accurate transition of a pupil area is often difficult due to eyelid overlap, effects of blinking, eyelashes and so on. In this paper, we developed a video-oculography system consisting of a video-oculography scope with a newly-introduced stimulus controller and a robust pupil tracking algorithm. We evaluated the accuracy of our video-oculography system with respect to each impediment type and estimated typical pupillary light-reflex parameters, used in clinical practice. Our system achieved an extraction accuracy of 0.968 in the F-measure in average on a total of 9,714 tested image frames from 35 movies, and it showed much more robust over the most common impediment, the eyelid overlap, than other methods. In addition, we confirmed that our system could estimate pupillary light-reflex parameters appropriately.