MUAP number estimates in surface EMG: template-matching methods and their performance boundaries.
Estimates of the number of motor unit action potential (MUAP)s appearing in the surface electromyogram (EMG) signal, which offers potentially valuable information about motor unit recruitment and firing rates, are likely to provide a more accurate reflection of the neural command to muscle than are current EMG quantification methods. In this paper, we show that the basic shapes of surface MUAPs recorded from the first dorsal interosseous (FDI) muscle can ideally be represented by a small number of waveforms. On the basis of this, we seek to estimate the number of MUAPs present in standard surface EMG records, using template-matching techniques to identify MUAP occurrences. Our simulation study indicates that the performance of template-matching methods for MUAP number estimation is mainly constrained by the MUAP superposition in the signal, and the maximum number of MUAPs allowed in the signal for a good estimation is determined by the duration of MUAPs. To further explore this from experimental surface EMG signals, we compare the recordings from a selective multiple concentric ring electrode against those derived from a standard differential EMG electrode situated over the same muscle. We conclude that the ring surface electrode only slightly reduces the MUAP duration and the less MUAP superposition rate contained in the signal is mainly achieved by reducing the pick up area of the electrode. Using a template-matching method, although the number of MUAPs can be approximately estimated based on a very selective surface EMG recording at low force levels, the maximum number of MUAPs correctly estimated from the surface EMG is constrained by the MUAP duration.
Zhou
Ping
P
Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA. p-zhou@northwestern.edu
Rymer
William Z
WZ
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004576:Electromyography / Q000379:methods*; D005385:Fingers / Q000502:physiology; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009046:Motor Neurons / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods