Zhou Ping, Kuiken Todd A
Neural Engineering Center for Artificial Limbs, Rehabilitation Institute of Chicago, Chicago, IL, USA.
Physiol Meas. 2006 Dec;27(12):1311-27. doi: 10.1088/0967-3334/27/12/005. Epub 2006 Oct 26.
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.
在使用肩关节离断假肢时，心电图（ECG）伪迹是污染肌电控制信号的主要噪声。对于在肩关节离断水平的双侧截肢者中进行有针对性的肌肉再支配以开发额外的肌电部位来改善假肢控制而言，这是一个更为显著的问题。本研究旨在从有针对性的肌肉再支配产生的肌电假肢控制信号中去除ECG伪迹。分别研究了基于模板减法、小波阈值处理和自适应滤波的三种ECG伪迹去除方法。从截肢者再支配的胸肌记录表面肌电信号。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。还考虑了商业假肢控制器的临床速度要求和内存限制，研究了不同方法在临床应用中的可行性。
