Automatic analysis of eye tracking data for medical diagnosis

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.