کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6951098 1451649 2017 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Measuring signal fluctuations in gait rhythm time series of patients with Parkinson's disease using entropy parameters
ترجمه فارسی عنوان
نوسانات سیگنال اندازه گیری در سری زمانی ریتم راه رفتن بیماران مبتلا به بیماری پارکینسون با استفاده از پارامترهای آنتروپی
کلمات کلیدی
آنتروپی تقریبی تحلیل ظاهر، تجزیه و تحلیل رگرسیون خطی کلی، بیماری پارکینسون، سیگنال تبدیل می شود، زمان قدم زدن، آنتروپی نمادین
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی
Gait rhythm disturbances due to abnormal strides indicate the degenerative mobility regulation of motor neurons affected by Parkinson's disease (PD). The aim of this work is to compute the approximate entropy (ApEn), normalized symbolic entropy (NSE), and signal turns count (STC) parameters for the measurements of stride fluctuations in PD. Generalized linear regression analysis (GLRA) and support vector machine (SVM) techniques were employed to implement nonlinear gait pattern classifications. The classification performance was evaluated in terms of overall accuracy, sensitivity, specificity, precision, Matthews correlation coefficient (MCC), and area under the receiver operating characteristic (ROC) curve. Our experimental results indicated that the ApEn, NSE, and STC parameters computed from the stride series of PD patients were all significantly larger (Wilcoxon rank-sum test: p < 0.01) than those of healthy control subjects. Based on the distinct features of ApEn, NSE, and STC, the SVM provided an accuracy rate of 84.48% and MCC of 0.7107, which are better than those of the GLRA (accuracy: 82.76%, MCC: 0.6552). The SVM and GLRA methods were able to distinguish PD gait patterns from healthy control cases with area of 0.9049 (SVM sensitivity: 0.7241, specificity: 0.9655) and 0.9037 (GLRA sensitivity: 0.8276, specificity: 0.8276) under the ROC curve, respectively, which are better or comparable with the classification results achieved by the other popular pattern classification methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biomedical Signal Processing and Control - Volume 31, January 2017, Pages 265-271
نویسندگان
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