基于HHT的肌电信号分析方法及其在痉挛评定中的应用

发布时间：2018-02-09 12:52

本文关键词： 肌电信号熵希尔伯特-黄变换去噪痉挛　出处：《合肥工业大学》2017年硕士论文　论文类型：学位论文

【摘要】：表面肌电信号(surface Electromyographic signal,sEMG)是肌肉参与活动的各运动单元电位综合作用的结果,能够表征神经肌肉系统的功能与状态。利用表面肌电信号进行痉挛检测的方法在临床医学、康复医学等领域均有重要的实用价值。本文针对脑卒中上肢痉挛患者进行被动牵引时的肌电信号,重点探讨sEMG的去噪及特征提取等关键技术,将其应用于痉挛牵张反射起始点的检测,结合角度实现痉挛等级量化评定。设计信号采集系统,针对不同痉挛等级患者,进行肘关节被动伸展过程中相关数据的采集实验。由于采集过程伴有诸多干扰信号,分析噪声来源结合sEMG的时频分布情况,提出基于EMD熵的阈值去噪方法,通过与小波阈值去噪法进行统计对比,证明该方法消噪效果良好,去除大量噪声的同时保留更多的细节信息。为准确获取sEMG特征,针对肌电信号的非线性非平稳性,提出基于HHT边际谱熵的特征提取方法。根据肌电信号在不同功能状态下表现出的复杂度变化的特点,结合痉挛前后信号的变化规律,利用HHT边际谱熵检测牵张反射起始点的方法,结合角度信息实现牵张反射阈值的判定。对采集到的全部sEMG进行去噪、特征提取,最终获得全部信号的牵张反射阈值(Stretch reflex threshold,SRT),利用Spearman分析SRT与MAS量表的相关性,实验结果证明SRT与MAS评分显著相关(r=-0.921,*P0.01),效度良好,为临床痉挛评定提供一种客观定量的分析手段。
[Abstract]:Surface Electromyographic signal emg is the result of the combined action of motor unit potentials involved in muscle activities, which can characterize the function and state of neuromuscular system. Rehabilitation medicine and other fields have important practical value. This paper focuses on the key techniques of sEMG denoising and feature extraction in view of the electromyography (EMG) signals of stroke patients with upper limb spasm during passive traction. It was applied to the detection of the starting point of spasmodic reflex, and the quantitative evaluation of the grade of spasm was realized by combining with the angle. A signal acquisition system was designed for patients with different spasticity grades. In this paper, the data acquisition experiment during passive extension of elbow joint is carried out. Because there are many interference signals in the acquisition process, the noise source is analyzed in combination with the time-frequency distribution of sEMG, and a threshold de-noising method based on EMD entropy is proposed. Through statistical comparison with wavelet threshold denoising method, it is proved that this method has good denoising effect, removes a large amount of noise and retains more detail information. In order to obtain sEMG features accurately, the nonlinear nonstationarity of EMG signals is studied. A feature extraction method based on HHT's marginal spectral entropy is proposed. According to the complexity of EMG signals in different functional states, combined with the changes of signals before and after spasms, The method of detecting the starting point of distraction reflection using HHT marginal spectral entropy and combining angle information to judge the threshold of distraction reflection is realized. All the collected sEMG are de-noised and the features are extracted. Finally, the stretch reflex threshold of all signals was obtained, and the correlation between SRT and MAS was analyzed by Spearman. The experimental results showed that the SRT and MAS scores were significantly correlated with MAS score, and the validity was good, which provided an objective and quantitative analysis method for clinical spasm evaluation.
【学位授予单位】：合肥工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R741.044;TN911.7

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