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IEEE Trans Biomed Eng. 2009 Sep;56(9):2197-201. doi: 10.1109/TBME.2008.2010392.

The effect of ECG interference on pattern-recognition-based myoelectric control for targeted muscle reinnervated patients.

Author information

1
Neural Engineering Center for Artificial Limbs, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA. l-hargrove@northwestern.edu

Abstract

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.

PMID:
19692302
DOI:
10.1109/TBME.2008.2010392
[Indexed for MEDLINE]

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