Automatic Analysis of Eye Tracking Data for Medical Diagnosis

Created on March 30, 2009

2009   ·   paper   ·   papers

Automatic Analysis of Eye Tracking Data for Medical Diagnosis

Galgani, F., Sun, Y., Lanzi, P. L., Leigh, J.

  • Location: Nashville, TN
  • URL: www.ieee-ssci.org/index.php?q=node/14
  • PDF: cidm2009.pdf

image

  • Caption: Eye movement for a single image for a five seconds lapse of time: (a) a control subject; (b) a subject diagnosed with ADHD
  • Credit: Y. Sun, EVL

Several studies have analyzed the link between mental dysfunctions and eye movements, using eye tracking techniques to determine where a person is looking, that is, the fixations. In this paper, we present a novel methodology to improve current diagnosis and evaluation methods of attention disorders. We have developed and tested several data-mining methodologies suitable for the automatic analysis and visualization of eye tracking data. In particular three novel methods of classification of subjects are proposed: (i) a method that uses Expectation Maximization to classify according to statistical likelihood of fixations locations; (ii) a procedure based on the Levenshtein distance method to compare sequences of fixations; and (iii) a method based on the analysis of the transitions frequencies of fixations between regions. Results of evaluation of classification accuracy are finally presented.