The Effect of ECG Interference on Pattern-Recognition-Based Myoelectric Control for Targeted Muscle Reinnervated Patients
The Effect of ECG Interference on Pattern-Recognition-Based Myoelectric Control for Targeted Muscle Reinnervated Patients
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DOI：
10.1109/tbme.2008.2010392
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发表时间：
2009-09-01
影响因子：
4.6
通讯作者：
Kuiken, Todd A.
中科院分区：
文献类型：
--
作者：
Hargrove, Levi;Zhou, Ping;Kuiken, Todd A.
有针对性的肌肉神经支配已被引入作为一种有效的神经机器接口。对于肩关节离断患者，神经转移的有效部位涉及胸肌，因为这些肌肉在肢体缺失的情况下几乎没有什么用处。因此，从重新神经支配的肌肉测量到的肌电信号可能会被大量心电图干扰所破坏。本文研究了心电图对动力上肢假肢肌电控制模式分类方案准确性的影响。结果表明，在临床测量中通常遇到的心电图干扰对分类准确性影响不大，但会影响用于传达运动速度的肌电活动的估计（通常称为比例控制）。大约 100 Hz 的高通滤波似乎可以有效减轻 ECG 干扰的影响。
Targeted muscle reinnervation has been introduced as an effective neural machine interface. In the case of a shoulder disarticulation patient, an effective site for a nerve transfer involves the pectoralis muscles, as these perform little useful function with a missing limb. Consequently, the myoelectric signals measured from the reinnervated muscles may be corrupted by a large amount of ECG interference. This paper investigates the effect of ECG upon the accuracy of a pattern-classification-based scheme for myoelectric control of powered upper limb prostheses. The results suggest that ECG interference, at levels typically encountered in a clinical measurement, has little effect upon classification accuracy, but can affect the estimate of myoelectric activity used to convey the velocity of motion (commonly referred to as proportional control). High-pass filtering at approximately 100 Hz appears to effectively mitigate the effect of ECG interference.