Cross-Comparison of Three Electromyogram Decomposition Algorithms Assessed With Experimental and Simulated Data

Abstract

The reliability of clinical and scientific information provided by algorithms that automatically decompose the electromyogram (EMG) depends on the algorithms' accuracies. We used experimental and simulated data to assess the agreement and accuracy of three publicly available decomposition algorithms-EMGlab (McGill , 2005) (single channel data only), Fuzzy Expert (Erim and Lim, 2008) and Montreal (Florestal , 2009). Data consisted of quadrifilar needle EMGs from the tibialis anterior of 12 subjects at 10%, 20% and 50% maximum voluntary contraction (MVC); single channel needle EMGs from the biceps brachii of 10 controls and 10 patients during contractions just above threshold; and matched simulated data. Performance was assessed via agreement between pairs of algorithms for experimental data and accuracy with respect to the known decomposition for simulated data. For the quadrifilar experimental data, median agreements between the Montreal and Fuzzy Expert algorithms at 10%, 20%, and 50% MVC were 95%, 86%, and 64%, respectively. For the single channel control and patient data, median agreements between the three algorithm pairs were statistically similar at ∼ 97% and ∼ 92%, respectively. Accuracy on the simulated data exceeded this performance. Agreement/accuracy was strongly related to the Decomposability Index (Florestal , 2009). When agreement was high between algorithm pairs applied to simulated data, so was accuracy.

A simulation study on the depth information of motor units

“…So, the arc AC and side AC are more effective than side BC in clinical practices. At present, the decomposition algorithms of the SEMG signal [23][24][25] have been developed, and the MUAP can be obtained, therefore, SEMG represents a promising non-invasive tool for future clinical applications.…”

Performance of three electromyogram decomposition algorithms as a function of signal to noise ratio: Assessment with experimental and simulated data

“…Three-channel quadrifilar needle EMGs had been acquired from the dominant leg of seven young (three male, four female; aged 18-30 years) and five elderly (two male, three female; aged 65 years or older) healthy subjects at the University of Massachusetts [25]. Briefly, the skin over the tibialis anterior (TA) muscle was cleaned with rubbing alcohol and a 27-gauge four-wire quadrifilar needle electrode was inserted into the belly of the TA muscle, avoiding the innervation zone.…”

“…In our prior full report [25], we contrasted the performance of three automated algorithms [1]- [3] that are publicly available for use in the MATLAB environment, with DI as our primary measure of the difficulty/challenge expected from each recording. Since algorithm performance varies depending on the data set analyzed, the same data were presented to each algorithm.…”

“…Thus, we present a companion cross-comparison of the performance of the three decomposition algorithms, as a function of the SNR MU . Readers are advised to review our prior full report [25], which contains complete project methods, etc.…”

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We have previously published a full report [25] comparing the performance of three automated electromyogram (EMG) decomposition algorithms. In our prior report, the primary measure of decomposition difficulty/challenge for each data record was the "Decomposability Index" of Florestal et al

[3].

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