New signal processing techniques for the decomposition of EMG signals

Abstract

This paper relates to the use of knowledge-based signal processing techniques in the decomposition of EMG signals. The aim of the research is to automatically decompose EMG signals recorded at force levels up to 20 per cent maximum voluntary contraction (MVC) into their constituent motor unit action potentials (MUAPS), and to display the MUAP shapes and firing times for the clinician. This requires the classification of nonoverlapping MUAPs and superimposed waveforms formed from overlapping MUAPs in the signal. Nonoverlapping MUAPs are classified using a statistical pattern-recognition method. The decomposition of superimposed waveforms uses a combination of procedural and knowledge-based methods. The decomposition method was tested on real and simulated EMG data recorded at force levels up to 20 per cent MVC. The different EMG signals contained up to six motor units (MUs). The new decomposition program classifies the total number of MUAP firings in an EMG signal with an accuracy always greater than 95 per cent. The decomposition program takes about 15s to classify all nonoverlapping MUAPs in EMG signal of length 1.0s and, on average, an extra 9s to classify each superimposed waveform.

EMG Signal Decomposition Using Motor Unit Potential Train Validity

“…Generally, this is done using a threshold crossing technique [1], [5][6][7], [25], [26], [45], [49], [50], [53][54][55], [58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74]. Scanning the raw or filtered signal for peaks that exceed a threshold produces a set of peaks that indicate candidate MUP positions.…”

“…The detection threshold can be a user-pre-defined absolute value [5][6][7], [53], [62][63][64][65][66][67][68], or it can be determined based on the characteristics of the signal and the noise contaminating it [7], [49], [50], [59], [60], [65][66][67][68], [75]. In practice, maximum absolute value, mean absolute value, and root mean square (RMS) value of the signal are three characteristics that may be used to set a detection threshold value.…”

“…The length of the window applied to the signal for selecting MUPs can be variable [54], [55], [59], [65], [69][70][71], or it can be fixed [1], [7], [19], [26], [45], [49], [50], [53], [54], [58], [66], [67], [72], [74][75][76][77][78][79][80][81][82]. If a variable-length window is chosen for analysis, the length of the window is adjusted based on the duration of the MUPs, for which the beginning and end of the MUPs are detected by thresholding.…”

“…To speed up decomposition, a number of algorithms use a small number of easy -to-calculate features [65], [70], [97], instead of time-domain, frequency-domain or wavelet-domain features. Loudon et al [65] represent MUPs by their morphological characteristics such as maximum peak-to-peak amplitude, maximum positive peak amplitude, total positive area, MUP duration, number of turns, and number of phases.…”

“…Loudon et al [65] represent MUPs by their morphological characteristics such as maximum peak-to-peak amplitude, maximum positive peak amplitude, total positive area, MUP duration, number of turns, and number of phases. Florestal et al [70], [97] use a set of symbolical features to represent MUPs.…”