1
Radiology Department, Allied Faculty, Mazandaran University of Medical Sciences, Sari, Iran
2
Department of Mathematics, Tehran North Branch, Islamic Azad University, Tehran, Iran
Abstract
Huntington's disease (HD) is a congenital, progressive, neurodegenerative disorder characterized by cognitive, motor, and psychological disorders. Clinical diagnosis of HD relies on the manifestation of movement abnormalities. In this study, we introduce a mathematical method for HD detection using step spacing. We used 16 walking signals as control and 20 walking signals as HD. We took a step back from the walking distance signals. Then, using fractal dimensions and statistical features, the control was classified and HD and 97.22% accuracy were obtained.
Allahverdy, A., Golchin, M. (2020). Detecting Huntington Patient Using Chaotic Features of Gait Time Series. Journal of Advances in Computer Research, 11(1), 27-32.
MLA
Armin Allahverdy; Mahboobeh Golchin. "Detecting Huntington Patient Using Chaotic Features of Gait Time Series". Journal of Advances in Computer Research, 11, 1, 2020, 27-32.
HARVARD
Allahverdy, A., Golchin, M. (2020). 'Detecting Huntington Patient Using Chaotic Features of Gait Time Series', Journal of Advances in Computer Research, 11(1), pp. 27-32.
VANCOUVER
Allahverdy, A., Golchin, M. Detecting Huntington Patient Using Chaotic Features of Gait Time Series. Journal of Advances in Computer Research, 2020; 11(1): 27-32.
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