On the Understanding of Anomalies in the Oculography Data and Their Classification with an Application of Fuzzy Aggregators

Modern medicine has been increasingly using information technologies and computer systems to improve decision-making processes. Important examples of such activities are the collection, processing, and analysis of oculographic data. The correct interpretation of the measurement results plays a very important role here. Medical examinations based on eye-tracking allow for early detection of many diseases or disfunctions of human brain. Moreover, it can be seen that implementation of such methods improves human-computer interfaces. In this study, we propose an innovative solution based on ten approaches of anomaly detection with the use of fuzzy aggregation of their results. Also, our experiment is supported by the analysis of obtained anomaly scores by the specialists in the field of medicine. The results of the automatic and expert evaluation show high potential of our method. There are also some differences in the perception of the anomaly by machine learning techniques and experts’ judgement. Hence, in this paper we try to understand and explain them.