Objective Evaluation of Bradykinesia in Parkinson's Disease using Evolutionary Algorithms

Bradykinesia, a slowing of movement, is the fundamental motor feature of Parkinson’s disease (PD) and the only physical sign that is obligatory for diagnosis. The complex nature of Bradykinesia makes it difficult to reliably identify, particularly as the early stages of the disease. This paper presents an extension of previous studies, applying evolutionary algorithms to movement data obtained from the standard clinical finger tapping (FT) test to characterise Bradykinesia. In this study, hand pronation-supination (PS) and hand opening-closing (HO) tasks are also considered. Cartesian Genetic Programming (CGP), is the evolutionary algorithm used to train and validate classifiers using features extracted from movement recordings of 20 controls and 22 PD patients. Features were selected based on the current clinical definition of Bradykinesia. The results show the potential of HO and PS to be used as effective classifiers with an accuracy of 84%. Discriminative features were also investigated with the possibility of informing clinical assessment.