Eliminating cardiac contamination from myoelectric control signals developed by targeted muscle reinnervation
Eliminating cardiac contamination from myoelectric control signals developed by targeted muscle reinnervation
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DOI：
10.1088/0967-3334/27/12/005
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发表时间：
2006-12-01
影响因子：
3.2
通讯作者：
Kuiken, Todd A.
中科院分区：
文献类型：
--
作者：
Zhou, Ping;Kuiken, Todd A.
心电伪影是肩关节离断假体肌电控制信号中的主要噪声。这是一个更重要的问题，有针对性的肌肉神经再支配，以发展额外的肌电部位，改善假体控制在一个双边截肢者在肩离断水平。本研究的目的是消除心电伪影的肌电假肢控制信号产生的目标肌肉神经再支配。分别研究了基于模板相减、小波阈值和自适应滤波的心电信号伪影去除方法。表面肌电图信号记录从再神经支配的胸肌截肢者。作为临床肌电假肢控制的关键参数，信号的幅度测量被用作性能指标来评估所提出的方法。考虑到商业假体控制器的临床速度要求和内存限制，还研究了不同方法用于临床应用的可行性。
The electrocardiogram (ECG) artifact is a major noise contaminating the myoelectric control signals when using shoulder disarticulation prosthesis. This is an even more significant problem with targeted muscle reinnervation to develop additional myoelectric sites for improved prosthesis control in a bilateral amputee at shoulder disarticulation level. This study aims at removal of ECG artifacts from the myoelectric prosthesis control signals produced from targeted muscle reinnervation. Three ECG artifact removal methods based on template subtracting, wavelet thresholding and adaptive filtering were investigated, respectively. Surface EMG signals were recorded from the reinnervated pectoralis muscles of the amputee. As a key parameter for clinical myoelectric prosthesis control, the amplitude measurement of the signal was used as a performance indicator to evaluate the proposed methods. The feasibility of the different methods for clinical application was also investigated with consideration of the clinical speed requirements and memory limitations of commercial prosthesis controllers.