基于表面肌电信号的小儿脑瘫步态活动段检测研究

发布时间：2018-07-21 13:26

【摘要】：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。
[Abstract]:The surface electromyography (SEMG) of the lower limbs of children with cerebral palsy was used to analyze the characteristic parameters of gait movement in order to evaluate the clinical severity of children with cerebral palsy. Firstly, the synthetic contour method (IP), sample entropy (Samp en) and smooth nonlinear energy operator (SNEO) were used to detect the SEMG signals when the lower extremity gastrocnemius muscles were activated. The accuracy and operation time of these algorithms are compared and studied. Finally, the SNEO algorithm with better performance is determined, and then the SEMG signal of children with cerebral palsy is measured. Gait movement segment was detected and calibrated. The results show that there is no significant difference in the accuracy of the three algorithms in the division of SEMG gait segments, but the Sneo algorithm has the advantages of fast operation speed and is suitable for gait segment detection of SEMG signals. There was a positive correlation between the degree of cerebral palsy and the average gait length of SEMG signal in children with cerebral palsy. Based on the results of this study, we suggest that we can consider the average gait length as an auxiliary quantitative index to evaluate the degree of cerebral palsy.
【作者单位】：中国科学技术大学电子科学与技术系;安徽交通职业技术学院城市轨道交通与信息工程系;
【基金】：国家自然科学基金资助项目(61271138)
【分类号】：R742.3

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