首页> 外文期刊>Journal of electromyography and kinesiology: Official journal of the International Society of Electrophysiological Kinesiology >Kinesiological surface electromyography in normal children: Range of normal activity and pattern analysis
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Kinesiological surface electromyography in normal children: Range of normal activity and pattern analysis

机译:正常儿童的运动学表面肌电图:正常活动范围和模式分析

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To document the range of activity and patterns of normal surface kinesiological electromyography in normal children, 87 children ranging from age 3 to 18 years who were developing normally were evaluated at free walking speed. Analysis was performed on 6307 gait cycles from 11 different muscles with custom software. We devised an algorithm to do a computer-based KEMG curve pattern analysis to sort out curves that did not match the physiologic muscle activity pattern, We also devised a combined preset amplitude and statistics-based criteria to satisfactorily determine the onset/cessation of KEMG activity. The measurements of timing and duration of activity, body height and weight, body mass index, cadence, stride length, and age were obtained for statistical analysis. From the pattern recognition, the medial and lateral hamstrings, gluteus maximus and medius, and gastrocnemius muscles had the highest percentage of clinically relevant curves. In most muscle groups, The KEMG curves had an average background activity of about 11-15% of maximum amplitude. The surface KEMG curves from normal subjects have 12.7% cycles that did not match their physiologic activity pattern. The timing and duration of KEMG activity was poorly or only fairly correlated with age, body height, body weight, and body mass index. The ensemble average of the linear envelope KEMG curves of each muscle could be applied as a normal database in a clinical gait analysis laboratory focusing on pediatric patients. (c) 2006 Elsevier Ltd. All rights reserved.
机译:为了记录正常儿童正常表面运动肌电图的活动范围和模式,对3岁至18岁正常发育的87名儿童以自由行走速度进行了评估。使用定制软件对来自11种不同肌肉的6307步态周期进行了分析。我们设计了一种算法,可以进行基于计算机的KEMG曲线模式分析,以找出与生理肌肉活动模式不匹配的曲线,还设计了一种组合的基于振幅和统计学的标准,可以令人满意地确定KEMG活动的发生/停止。获得活动时间和持续时间,身高和体重,体重指数,节奏,步幅长度和年龄的测量值以进行统计分析。从模式识别来看,内侧和外侧绳肌,臀大肌和臀肌以及腓肠肌的临床相关曲线比例最高。在大多数肌肉组中,KEMG曲线的平均背景活动约为最大振幅的11-15%。正常受试者的表面KEMG曲线具有12.7%的周期,与他们的生理活动模式不匹配。 KEMG活动的时机和持续时间与年龄,身高,体重和体重指数之间的相关性很弱或仅有相当的相关性。在专注于小儿患者的临床步态分析实验室中,每条肌肉的线性包络KEMG曲线的集合平均可以用作正常数据库。 (c)2006 Elsevier Ltd.保留所有权利。

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