Independent component analysis for the detection of in-vivo intrinsic signals from an optical imager of retinal function

Abstract

To overcome the difficulty in detection of loss of retinal activity, a functional-Retinal Imaging Device (f-RID) was developed. The device, which is based on a modified fundus camera, seeks to detect changes in optical signals that reflect functional changes in the retina. Measured changes in reflectance in response to the visual stimulus are on the order of 0.1% to 1% of the total reflected intensity level, which makes the functional signal difficult to detect by standard methods because it is masked by other physiological signals and by noise. In this paper, we present a new Independent Component Analysis (ICA) algorithm used to analyze the video sequences from a set of experiments with different patterned stimuli from cats and humans. The ICA algorithm with priors (ICA-P) uses information about the stimulation paradigms to increase the signal detection thresholds when compared to traditional ICA algorithms. The results of the analysis show that we can detect signal levels as low as 0.01% of the total reflected intensity. Also, improvement of up to 30dB in signal detection over traditional ICA algorithms is achieved. The study found that in more than 80% of the in-vivo experiments the patterned stimuli effects on the retina can be detected and extracted.