Nonlinear gait dynamics discrimination method for Parkinson gait risk assessment

A technique of nonlinear dynamics and risk assessment, applied in the field of nonlinear gait dynamics discriminant map, which can solve problems such as insufficient characterization of dynamic essential characteristics

Inactive Publication Date: 2019-08-23
HANGZHOU DIANZI UNIV
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  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

In essence, the human gait signal can be regarded as a complex non-stationary signal generated by a complex dynamic system. It is obviously not enough to describe the dynamic nature of Parkinson's gait only by using time-frequency parameters.

Method used

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  • Nonlinear gait dynamics discrimination method for Parkinson gait risk assessment
  • Nonlinear gait dynamics discrimination method for Parkinson gait risk assessment
  • Nonlinear gait dynamics discrimination method for Parkinson gait risk assessment

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Experimental program
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Embodiment

[0062] Such as figure 1 As shown, it is a flow chart for Parkinson's gait risk assessment and discrimination in an embodiment of the present invention, including the following steps:

[0063] Step 1 obtains the gait gait time parameter series data of Parkinson's patient and control group: the present invention is to make research result true and reliable, verify its better effect, and data set is by (http: / / www.physionet.org / gait dynamics database provided by physiobank / database / gaitndd). The gait signal was obtained through pressure sensors placed on the soles of the subjects' shoes. Currently the database provides the following gait parameters: elapsed time (s), left foot stride duration (s), right foot stride duration (s), left foot swing duration (s), right foot swing duration (s) , Left foot swinging time (stride percentage), right foot swinging time (stride percentage), left foot standing time (s), right foot standing time (s), left foot standing time (stride percenta...

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Abstract

The invention discloses a nonlinear gait dynamics discrimination method for Parkinson gait risk assessment. The method proposes a multi-dimensional feature discriminant diagram based on nonlinear dynamic characteristics for gait risk assessment of Parkinson patients, and in combination with a support vector machine, a non-linear dynamics feature and a graphical display method, makes 5-dimensionalnon-linear dynamic features graphically represented in a two-dimensional plane. The method is applicable to gait risk assessment in Parkinson patients. In a normal population, if multiple indicators of the nonlinear gait dynamics discriminant diagram exceed the cut-off point of the discriminant diagram, it indicates that the probability of having the disease or the severity of the disease is greatly increased, and it is necessary to cause individual's own close concern and further examination by doctors.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a non-linear gait dynamics discriminant diagram for Parkinson's gait risk assessment. Background technique [0002] Parkinson's disease is a neurodegenerative disease that mainly occurs in middle-aged and elderly people, with movement disorders as the main clinical symptoms. At present, the treatment methods for Parkinson's disease mainly include drug therapy and surgical treatment. In the early stage of the disease, the physical behaviors caused by Parkinson's disease, such as resting tremor and other clinical symptoms, can usually be well controlled clinically. However, with the development of Parkinson's disease patients, symptoms such as gait changes and tremors will seriously affect the quality of life of patients, requiring different degrees of surgical intervention. If the risk and severity of the disease can be correctly assessed, and the treatmen...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/11
CPCA61B5/112A61B5/4082A61B5/7267
Inventor 江俊敏邓木清冯小仍张敬曹九稳
Owner HANGZHOU DIANZI UNIV
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