Automatic decomposition of multichannel intramuscular EMG signals.

We describe an automatic algorithm for decomposing multichannel EMG signals into their component motor unit action potential (MUAP) trains, including signals from widely separated recording sites in which MUAPs exhibit appreciable interchannel offset and jitter. The algorithm has two phases. In the clustering phase, the distinct, recurring MUAPs in each channel are identified, the ones that correspond to the same motor units are determined by their temporal relationships, and multichannel templates are computed. In the identification stage, the MUAP discharges in the signal are identified using matched filtering and superimposition resolution techniques. The algorithm looks for the MUAPs with the largest single channel components first, using matches in one channel to guide the search in other channels, and using information from the other channels to confirm or refute each identification. For validation, the algorithm was used to decompose 10 real 6-to-8-channel EMG signals containing activity from up to 25 motor units. Comparison with expert manual decomposition showed that the algorithm identified more than 75% of the total 176 MUAP trains with an accuracy greater than 95%. The algorithm is fast, robust, and shows promise to be accurate enough to be a useful tool for decomposing multichannel signals. It is freely available at http://emglab.stanford.edu.

How much can we trust the electromechanical delay estimated by using electromyography?

In this paper different estimation techniques are evaluated for the assessment of electromechanical delay (EMD). The following techniques are compared for benchmarking purposes: envelope estimation and thresholding, with different subjective combinations of filters and thresholds, and a double threshold statistical detector (DTD). Performances are compared in terms of bias, standard deviation and erroneous detections of the estimations. DTD showed higher robustness and repeatability of results, guaranteed by the objective settings based on the statistical characteristics of the algorithm.