The effect of ECG interference on pattern-recognition-based myoelectric control for targeted muscle reinnervated patients.
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.
Hargrove
Levi
L
Neural Engineering Center for Artificial Limbs, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA. l-hargrove@northwestern.edu
Zhou
Ping
P
Englehart
Kevin
K
Kuiken
Todd A
TA
D000672:Amputation Stumps; D001186:Artificial Limbs; D004562:Electrocardiography; D004576:Electromyography / Q000379:methods*; D006801:Humans; D008328:Man-Machine Systems; D010363:Pattern Recognition, Automated / Q000379:methods*; D010369:Pectoralis Muscles / Q000294:innervation; D012782:Shoulder