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中文
Yunfeng Wu

Associate professor

Supervisor of Master's Candidates


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Date of Employment:2009-12-14

School/Department:信息学院

Education Level:博士研究生毕业

Business Address:厦门大学翔安校区西部片区6号楼(睿信楼)202室

Gender:Male

Contact Information:yunfengwu@xmu.edu.cn

Degree:Doctor of Engineering (D.Eng.)

Status:在职

Alma Mater:北京邮电大学

Discipline:生物医学工程
信号与信息处理

Academic Honor:

2013   Outstanding talents in the new century

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Analysis of altered gait cycle duration in amyotrophic lateral sclerosis based on nonparametric probability density function estimation

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DOI number:10.1016/j.medengphy.2010.10.023

Journal:Medical Engineering & Physics

Abstract:Human locomotion is regulated by the central nervous system (CNS). The neurophysiological changes in the CNS due to amyotrophic lateral sclerosis (ALS) may cause altered gait cycle duration (stride interval) or other gait rhythm. This article used a statistical method to analyze the altered stride interval in patients with ALS. We first estimated the probability density functions (PDFs) of stride interval from the outlier-processed gait rhythm time series, by using the nonparametric Parzen-window approach. Based on the PDFs estimated, the mean of the left-foot stride interval and the modified Kullback-Leibler divergence (MKLD) can be computed to serve as dominant features. In the classification experiments, the least squares support vector machine (LS-SVM) with Gaussian kernels was applied to distinguish the stride patterns in ALS patients. According to the results obtained with the stride interval time series recorded from 16 healthy control subjects and 13 patients with ALS, the key findings of the present study are summarized as follows. (1) It is observed that the mean of stride interval computed based on the PDF for the left foot is correlated with that for the right foot in patients with ALS. (2) The MKLD parameter of the gait in ALS is significantly different from that in healthy controls. (3) The diagnostic performance of the nonlinear LS-SVM, evaluated by the leave-one-out cross-validation method, is superior to that obtained by the linear discriminant analysis. The LS-SVM can effectively separate the stride patterns between the groups of healthy controls and ALS patients with an overall accurate rate of 82.8% and an area of 0.869 under the receiver operating characteristic curve.

Co-author:Lei Shi

First Author:Yunfeng Wu*

Indexed by:Article

Volume:33

Issue:3

Page Number:347-355

Translation or Not:no

Date of Publication:2011-04-01

Included Journals:SCI

Links to published journals:https://doi.org/10.1016/j.medengphy.2010.10.023