Suppr超能文献

人体直立站立姿势平衡测定中的多元统计分析(第二篇报告)——姿势图的模式识别

[Multivariate statistical analysis in stabilometry in human upright standing (second report)--pattern recognition of a stabilogram].

作者信息

Yagi K

出版信息

Nihon Jibiinkoka Gakkai Kaiho. 1989 Jun;92(6):909-22. doi: 10.3950/jibiinkoka.92.909.

Abstract

A pattern of a figure which is displayed by an X-Y recorder or by a polygraph in stabilometry may present several qualitative facts about the standing ability of a test-subject. The purpose of this study was to make a quantitative evaluation of the body stability in both normal and ataxic patients who were standing erect. The technique utilised was pattern-recognition, composed of principal component analysis and discriminant analysis. Stabilometrical examinations were carried out in 20 normal subjects, 39 with peripheral vestibular disturbance and 11 with disturbance of the central origin. Each examinee was ordered to keep an upright posture for 60 seconds both with the eyes open and with the eyes closed. The following parameters were used: the area of the ellipse for rejection by a statokinesigram, the maximum width and the total length of body excursion, the root mean square, the velocity, the acceleration and the average frequency in the horizontal plane. These were the same parameters that were used in the first report. Each component predicting a specific character in stabilometry was identified by this study of principal component analysis. The relationship between the components in order and its prediction were as follows: the first component predicted the size of the body movement, the second the force, the third the density, the fourth the difference between body movement with the eyes open and with the eyes closed and the fifth the direction of body sway. Furthermore, the principal components were successfully utilized to allow the classification of the test subjects into three groups: those who were normal, those with peripheral vestibular disturbance and those with disturbance of the central origin. Discriminant functions were also used to classify the above mentioned three groups and another five groups, namely: those who were normal, those with vestibular neuronitis, those with positional vertigo of the benign paroxysmal type, those with Ménière's disease and those with cerebellar ataxia. In classification of the three groups, the significant parameters used for discrimination were as follows (P less than 0.05): the root mean square with the eyes open, the distance and the maximum width of body excursion in the Y direction with the eyes open, the area of the ellipse for rejection both with the eyes open and with the eyes closed, the maximum width and the average frequency in the X direction with the eyes closed.(ABSTRACT TRUNCATED AT 400 WORDS)

摘要

由X - Y记录仪或用于姿势稳定测量的测谎仪所显示的图形模式,可能呈现出有关受试对象站立能力的几个定性事实。本研究的目的是对直立站立的正常患者和共济失调患者的身体稳定性进行定量评估。所采用的技术是模式识别,由主成分分析和判别分析组成。对20名正常受试者、39名患有外周前庭功能障碍的患者和11名患有中枢性障碍的患者进行了姿势稳定测量检查。要求每位受试者睁眼和闭眼各保持直立姿势60秒。使用了以下参数:姿势描记图中排除椭圆的面积、身体偏移的最大宽度和总长度、均方根、速度、加速度以及水平面中的平均频率。这些是在第一篇报告中使用的相同参数。通过主成分分析的这项研究,确定了姿势稳定测量中预测特定特征的每个成分。各成分与其预测之间的关系如下:第一个成分预测身体运动的大小,第二个成分预测力,第三个成分预测密度,第四个成分预测睁眼和闭眼时身体运动的差异,第五个成分预测身体摆动的方向。此外,主成分成功地用于将受试对象分为三组:正常组、外周前庭功能障碍组和中枢性障碍组。判别函数也用于对上述三组以及另外五组进行分类,即:正常组、前庭神经炎组、良性阵发性位置性眩晕组、梅尼埃病组和小脑共济失调组。在对这三组进行分类时,用于判别的显著参数如下(P小于0.05):睁眼时的均方根、睁眼时Y方向身体偏移的距离和最大宽度、睁眼和闭眼时排除椭圆的面积、闭眼时X方向的最大宽度和平均频率。(摘要截选至400字)

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验