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基于概率密度分析的脑卒中患者平衡功能检测的自动特征描述。

Automatic characterization of stroke patients' posturography based on probability density analysis.

机构信息

School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China.

Department of Neurology, Ruijin Hospital Luwan Branch Affiliated to Shanghai Jiao Tong University, Shanghai, 200000, China.

出版信息

Biomed Eng Online. 2023 Feb 4;22(1):8. doi: 10.1186/s12938-023-01069-z.

DOI:10.1186/s12938-023-01069-z
PMID:36739411
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9899377/
Abstract

OBJECTIVE

The probability density analysis was applied to automatically characterize the center of pressure (COP) data for evaluation of the stroke patients' balance ability.

METHODS

The real-time COP coordinates of 38 stroke patients with eyes open and closed during quiet standing were obtained, respectively, from a precision force platform. The COP data were analyzed and characterized by the commonly used parameters: total sway length (SL), sway radius (SR), envelope sway area (EA), and the probability density analysis based parameters: projection area (PA), skewness (SK) and kurtosis (KT), and their statistical correlations were analyzed. The differences of both conventional parameters and probability density parameters under the conditions of eyes open (EO) and eyes closed (EC) were compared.

RESULTS

The PA from probability density analysis is strongly correlated with SL and SR. Both the traditional parameters and probability density parameters in the EC state are significantly different from those in the EO state. The obtained various statokinesigrams were calculated and categorized into typical sway types through probability density function for clinical evaluation of the balance ability of stroke patients.

CONCLUSIONS

The probability density analysis of COP data can be used to characterize the posturography for evaluation of the balance ability of stroke patients.

摘要

目的

概率密度分析被应用于自动描述中心压力（COP）数据，以评估中风患者的平衡能力。

方法

分别从精密力平台获得 38 名中风患者睁眼和闭眼时安静站立的实时 COP 坐标。通过常用参数（总晃动长度（SL）、晃动半径（SR）、包络晃动面积（EA））和概率密度分析参数（投影面积（PA）、偏度（SK）和峰度（KT））对 COP 数据进行分析和特征描述，并分析它们的统计相关性。比较睁眼（EO）和闭眼（EC）条件下传统参数和概率密度参数的差异。

结果

概率密度分析的 PA 与 SL 和 SR 强烈相关。EC 状态下的传统参数和概率密度参数与 EO 状态下的参数均有显著差异。通过概率密度函数计算并分类获得各种静动态图形，以临床评估中风患者的平衡能力。

结论

COP 数据的概率密度分析可用于描述姿势描记术，以评估中风患者的平衡能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4d9/9899377/456ecc6fdba0/12938_2023_1069_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4d9/9899377/859382d8b1de/12938_2023_1069_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4d9/9899377/3f4a4e8207ea/12938_2023_1069_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4d9/9899377/a8fcf51281f5/12938_2023_1069_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4d9/9899377/4048b2c428f2/12938_2023_1069_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4d9/9899377/f058f1331d51/12938_2023_1069_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4d9/9899377/76bbd58d11e4/12938_2023_1069_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4d9/9899377/456ecc6fdba0/12938_2023_1069_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4d9/9899377/859382d8b1de/12938_2023_1069_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4d9/9899377/3f4a4e8207ea/12938_2023_1069_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4d9/9899377/a8fcf51281f5/12938_2023_1069_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4d9/9899377/4048b2c428f2/12938_2023_1069_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4d9/9899377/f058f1331d51/12938_2023_1069_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4d9/9899377/76bbd58d11e4/12938_2023_1069_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4d9/9899377/456ecc6fdba0/12938_2023_1069_Fig7_HTML.jpg

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本文引用的文献

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Impairments and Compensations of Static Balance and Plantar Load Distribution in Patients With Chronic Stroke: An Observational Study.慢性脑卒中患者静态平衡和足底负荷分布的障碍和代偿：一项观察性研究。
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