Feasibility of spirography features for objective assessment of motor function in Parkinson's disease

Publication date: Available online 31 March 2017
Source:Artificial Intelligence in Medicine
Author(s): Aleksander Sadikov, Vida Groznik, Martin Možina, Jure Žabkar, Dag Nyholm, Mevludin Memedi, Dejan Georgiev
ObjectiveParkinson's disease (PD) is currently incurable, however proper treatment can ease the symptoms and significantly improve the quality of life of patients. Since PD is a chronic disease, its efficient monitoring and management is very important. The objective of this paper was to investigate the feasibility of using the features and methodology of a spirography application, originally designed to detect early Parkinson's disease (PD) motoric symptoms, for automatically assessing motor symptoms of advanced PD patients experiencing motor fluctuations. More specifically, the aim was to objectively assess motor symptoms related to bradykinesias (slowness of movements occurring as a result of under-medication) and dyskinesias (involuntary movements occurring as a result of over-medication).Materials and methodsThis work combined spirography data and clinical assessments from a longitudinal clinical study in Sweden with the features and pre-processing methodology of a Slovenian spirography application. The study involved 65 advanced PD patients and over 30,000 spiral-drawing measurements over the course of three years. Machine learning methods were used to learn to predict the "cause" (bradykinesia or dyskinesia) of upper limb motor dysfunctions as assessed by a clinician who observed animated spirals in a web interface. The classification model was also tested for comprehensibility. For this purpose a visualisation technique was used to present visual clues to clinicians as to which parts of the spiral drawing (or its animation) are important for the given classification.ResultsUsing the machine learning methods with feature descriptions and pre-processing from the Slovenian application resulted in 86% classification accuracy and over 0.90 AUC. The clinicians also rated the computer's visual explanations of its classifications as at least meaningful if not necessarily helpful in over 90% of the cases.ConclusionsThe relatively high classication accuracy and AUC demonstrates the usefulness of this approach for objective monitoring of PD patients. The positive evaluation of computer's explanations suggests the potential use of this methodology in a decision support setting.



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