Accuracy assessment of CKC high-density surface EMG decomposition in biceps femoris muscle

Abstract

The aim of this study was to assess the accuracy of the Convolution Kernel Compensation (CKC) method in decomposing high-definition surface EMG (HDsEMG) signals from the pennate biceps femoris long-head muscle. Although the CKC method has already been thoroughly assessed in parallel-fibered muscles, there are several factors that could hinder its performance in pennate muscles. Namely, HDsEMG signals from pennate and parallel-fibered muscles differ considerably in terms of the number of detectable motor units (MUs) and the spatial distribution of the motor-unit action potentials (MUAPs). In this study, monopolar surface EMG signals were recorded from 5 normal subjects during low-force voluntary isometric contractions using a 92-channel electrode grid with 8 mm inter-electrode distances. Intramuscular EMG (iEMG) signals were recorded concurrently using monopolar needles. The HDsEMG and iEMG signals were independently decomposed into MUAP trains, and the iEMG results were verified using a rigorous a-posteriori statistical analysis. HDsEMG decomposition identified from 2 to 30 MUAP trains per contraction. 3±2 of these trains were also reliably detected by iEMG decomposition. The measured CKC decomposition accuracy of these common trains over a selected 10 second interval was 91.5±5.8 %. The other trains were not assessed. The significant factors that affected CKC decomposition accuracy were the number of HDsEMG channels that were free of technical artifact and the distinguishability of the MUAPs in the HDsEMG signal (P<0.05). These results show that the CKC method reliably identifies at least a subset of MUAP trains in HDsEMG signals from low force contractions in pennate muscles.

Preferential distribution of nociceptive input to motoneurons with muscle units in the cranial portion of the upper trapezius muscle

“…The convolution kernel compensation method, introduced in Zazula (2004, 2007) and validated in numerous previous studies Holobar et al 2009Holobar et al , 2010Holobar et al , 2012Marateb et al 2011), was used to decompose the acquired EMG signals into contributions of individual motor units. Once identified, discharge times of individual motor units were dynamically tracked over the entire EMG signal, taking into account potential changes in shapes of motor unit action potentials (MUAPs), such as those caused by small arm movements and fatigue (Holobar et al , 2010(Holobar et al , 2012.…”

Electromyographic (EMG) Decomposition

“…As a result, the number of constituent MUAP trains is generally lower in iEMG signals, and the MUAPs from the different active MUs are more distinguishable than they are in sEMG signals (22). Also, iEMG decomposition can be used to validate sEMG decomposition (23). Accordingly, iEMG decomposition is discussed here first.…”

“…The convolution kernel compensation (CKC) algorithm of Holobar and Zazula (74, 77) has been reported to be able to decompose as many as 30 MUAP trains from an HDsEMG signal (23).…”

“…This procedure is repeated for every identified MU, as depicted in Figure 16. Note that MUAP waveforms are not explicitly used in this procedure, and so the CKC method can be used with various acquisition systems and with muscles of various architectures (23,79). An efficient signal-based metric of CKC decomposition accuracy has been proposed (80).…”

“…Decomposition accuracy can also be estimated separately for each MUAP train based on the "decomposability index" of the MUAP train ( Figure 17). The decomposability index takes into account the distinguishability of the MUAP from the other MUAPs in the signal as well as in the overall signal complexity (23,27,48,79,99,100). It is defined as the distance in feature space between the MUAP and the next most similar MUAP (or the distance between the MUAP and the baseline, whichever is smaller) divided by the root-mean-square signal amplitude.…”