Objective: To assess whether MDS-UPDRS grade one Parkinson’s disease (PD) bradykinesia can be accurately detected by a novel non-invasive device that analyses a standard finger tapping (FT) clinical assessment using evolutionary computation methods.

Background: Bradykinesia is the fundamental motor feature of PD but may be difficult to detect clinically, especially in the early stages. The most common reason that movement disorder specialists misdiagnose PD for other tremulous disorders is misinterpretation of bradykinesia, which is a complex clinical sign encompassing movements with slow speed, small amplitude, irregular rhythm, brief pauses and progressive decrements. Clinical ascertainment of the presence and severity of bradykinesia relies on subjective interpretation and integration of these components during a dynamic test. Not surprisingly, there is inter-rater variability for assessing bradykinesia, particularly when the severity is slight or mild. The need exists for a simple, non-invasive test that can provide an accurate, objective measurement of slight bradykinesia.

Methods: Forty-nine PD patients and 41 healthy controls (HC) performed FT for 30 seconds with each hand separately, whilst wearing electromagnetic tracking sensors on the index finger and thumb. Movement data was analysed by purpose written evolutionary computation algorithms. The algorithm prediction of the diagnostic group was compared to clinical assessment using receiver operator characteristic (ROC) curves and was further validated by testing on an independent sample of FT data collected from 13 PD patients and 9 HC in an international centre.

Results: 45 of the PD FT assessments exhibited MDS-UPDRS grade one ‘slight’ bradykinesia and all 82 HC FT assessments were MDS-UPDRS grade zero ‘normal’. The grade one PD FT kinematic data was accurately distinguished from HC FT dominant hand data with an area under ROC curve of 0.952 (p < 0.001), equivalent to sensitivity/specificity of 0.91/0.93 at the threshold of equal trade-off. In the validation sample, the algorithm correctly classified 93% of the ‘slight’ bradykinesia FT data.

Conclusions: This technology is able to accurately detect the subtlest clinical grade of bradykinesia. Potential applications include aiding accurate early diagnosis of PD or screening populations for epidemiological studies.

Earlier version presented at WCN Vienna 2013.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/clinically-slight-bradykinesia-in-parkinsons-disease-is-accurately-detected-using-evolutionary-computation-analysis-of-finger-tapping/