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本文采用小儿脑瘫患者步行时下肢的表面肌电信号(sEMG),对其步态运动的特征参数进行分析,拟达到对小儿脑瘫患者的临床严重程度进行评估的目的。首先采用综合轮廓法(IP)、样本熵(Samp EN)和平滑非线性能量算子(SNEO)三种方法分别检测仿真步行状态下,下肢双侧腓肠肌激活时的sEMG信号,并对这些算法得到的结果进行精度和运算时间的比较研究,最后确定了三种算法中性能比较优良的SNEO算法,然后再利用实测的小儿脑瘫患者的sEMG信号,对患儿步态活动段进行检测和标定。研究结果表明:三种算法在sEMG步态活动段的划分中精度的差异没有统计学意义,但SNEO算法具有运算速度快的优点,适用于sEMG信号的步态活动段检测;小儿脑瘫患者的脑瘫程度与其sEMG信号的步态活动段平均长度呈正相关关系,三种不同程度脑瘫患儿的步态活动段长度差异具有统计学意义。通过本文研究结果,我们提出或许可以考虑将步态活动段平均长度作为一种评估脑瘫程度的辅助定量化指标的新思路。
In this paper, the sEMG of the lower extremities in pediatric cerebral palsy patients was used to analyze the characteristic parameters of their gait motions, and the aim was to evaluate the clinical severity of children with cerebral palsy. Firstly, the sEMG signals of the gastrocnemius muscle of the lower extremities under simulated walking state were detected by using three methods: the comprehensive profile method (IP), the sample entropy (Samp EN) and the smoothed nonlinear energy operator (SNEO) Finally, the SNEO algorithm with good performance in the three algorithms was finally determined, and then the sEMG signal of the children with cerebral palsy was used to detect and calibrate the gait activity of the children. The results show that the accuracy of the three algorithms in the classification of sEMG gait activity is not statistically significant, but SNEO algorithm has the advantages of fast computing speed, suitable for sEMG signal gait activity segment detection; cerebral palsy patients with cerebral palsy There was a positive correlation between the degree of sEMG signal and the average length of gait activity segments. There were significant differences in the length of gait activity among children with different degrees of cerebral palsy. Through the results of this study, we propose that we may consider the average length of gait activity segment as a new quantitative evaluation of cerebral palsy of the new ideas.