Detecting Huntington Patient Using Chaotic Features of Gait Time Series

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 ۱۶ walking signals as control and ۲۰ 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 ۹۷.۲۲% accuracy were obtained.

Keywords:

HD , Gait signal , Stride time interval , fractal dimension , Statistical features

Authors

Armin Allahverdy

Radiology Department, Allied Faculty, Mazandaran University of Medical Sciences, Sari, Iran

Mahboobeh Golchin

Department of Mathematics, Tehran North Branch, Islamic Azad University, Tehran, Iran

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لینک ثابت به این Paper:

https://civilica.com/doc/1194354

شناسه ملی سند علمی:

JR_JACR-11-1_003

تاریخ نمایه سازی: 13 اردیبهشت 1400

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Allahverdy, Armin and Golchin, Mahboobeh,1399,Detecting Huntington Patient Using Chaotic Features of Gait Time Series,https://civilica.com/doc/1194354

Scientometrics

The specifications of the publisher center of this Paper are as follows:

Ranking of University of Mazandaran

Type of center: دانشگاه دولتی

Paper count: 17,348

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