Authors:
Siti Anizah Muhamed
1
;
Rachel Newby
2
;
3
;
1
;
Stephen L. Smith
1
;
Jane Alty
2
;
1
;
Stuart Jamieson
2
;
1
and
Peter Kempster
3
;
4
Affiliations:
1
University of York, United Kingdom
;
2
Leeds General Infirmary, United Kingdom
;
3
Monash Medical Centre, Australia
;
4
Monash University, Australia
Keyword(s):
Parkinson’s Disease, Evolutionary Algorithms, Cartesian Genetic Programming, Bradykinesia, Finger Tapping, Hand Pronation-Supination, Hand Opening-closing.
Abstract:
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 inves
tigated with the possibility of informing clinical assessment.
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