Fuzzy rough sets theory applied to parameters of eye movements can help to predict effects of different treatments in Parkinson’s patients

Abstract

Parkinson (PD) is the second most common neurodegenerative disease (ND) with characteristic movement disorders. There are well defined standard procedures to measure disease stage (Hohen Yahr scale), progression and effects of treatments (UPDRS – unified Parkinson Disease Rate Scale). But these procedures can only be performed by experienced neurologist and they are partly subjective. The purpose of our work was to test objective and noninvasive method that may help to estimate disease stage by measuring fast and slow eye movements (EM). It was demonstrated earlier that EM changes in PD. We have measured reflexive saccades (RS) and slow pursuit ocular movements (POM) in four sessions related to different treatments. With help of fuzzy rough sets theory (FRST) we have related measurements with expert’s opinion by generalizing experimental finding by fuzzy rules. In order to test our approach, we have divided our measurements into training and testing sets. In the second test, we have removed expert’s decisions and predicted them from the training set in two situations: on the basis of only classical neurological measurements and on the basis of EM measurements. We have observed, on 12 PD patients basis, an increase in predictions accuracy when eye movements were included as condition attributes. Our results with help of the FRST suggest that EM measurements may become an important diagnostic tool in PD.