Analysis of visually guided tracking performance in Parkinson's disease

• Published in: 2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom) pp. 164 - 169
• Main Authors: Yi Liu, Chonho Lee, Bu-Sung Lee, Stevenson, James K. R., McKeown, Martin J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2014
• Subjects: Accuracy; Clustering algorithms; Data mining; Decision trees; Dyskinesia; Medical diagnostic imaging; Noise; Parkinson's disease; Tracking performance
• DOI: 10.1109/HealthCom.2014.7001835

Recent studies have suggested significant differences in motor performances of Parkinson's Disease (PD) patients who have L-dopa induced dyskinesias (LIDs), even when off of L-dopa medication. The pathophysiology of LIDs remains obscure, so applying data-mining techniques to the patients' motor performance may provide some heuristic insight. This paper investigated visually-guided tracking performance of PD patients using data mining techniques to reveal the differences between dyskinesia and non-dyskinesia patients. We found that K-means clustering of the root mean square (RMS) tracking error at faster tracking speeds and with ambiguous visual stimuli was able to effectively discriminate between the two groups with 77.8% accuracy. Decision tree classification was less accurate (68.4%) and determined that years since diagnosis was the best feature to distinguish between groups. Our results suggest that data mining methodologies may provide novel insights into features of the neurovegetative disease.