Fractal measures of video-recorded trajectories can classify motor subtypes in Parkinson’s disease
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Fractal measures of video-recorded trajectories can classify motor subtypes in Parkinson’s diseaseDate
2016-06-16Citation
Figueiredo TC, Vivas J, Peña N, Miranda JGV. Fractal measures of video-recorded trajectories can classify motor subtypes in Parkinson’s disease. Physica A. 2016;462:12-20
Abstract
[Abstract] Parkinson’s Disease is one of the most prevalent neurodegenerative diseases in the world and affects millions of individuals worldwide. The clinical criteria for classification of motor subtypes in Parkinson’s Disease are subjective and may be misleading when symptoms are not clearly identifiable. A video recording protocol was used to measure hand tremor of 14 individuals with Parkinson’s Disease and 7 healthy subjects. A method for motor subtype classification was proposed based on the spectral distribution of the movement and compared with the existing clinical criteria. Box-counting dimension and Hurst Exponent calculated from the trajectories were used as the relevant measures for the statistical tests. The classification based on the power-spectrum is shown to be well suited to separate patients with and without tremor from healthy subjects and could provide clinicians with a tool to aid in the diagnosis of patients in an early stage of the disease.
Keywords
Fractal dimension
Hurst exponent
Parkinson's disease
Motor subtypes
Hurst exponent
Parkinson's disease
Motor subtypes
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Atribución-NoComercial-SinDerivadas 3.0 España
ISSN
0378-4371