A software package for the decomposition of long-term multichannel EMG signals using wavelet coefficients

Abstract

This paper presents a method to decompose multichannel long-term intramuscular electromyogram (EMG) signals. In contrast to existing decomposition methods which only support short registration periods or single-channel recordings of signals of constant muscle effort, the decomposition software EMG-LODEC (ElectroMyoGram LOng-term DEComposition) is especially designed for multichannel long-term recordings of signals of slight muscle movements. A wavelet-based, hierarchical cluster analysis algorithm estimates the number of classes [motor units (MUs)], distinguishes single MUAPs from superpositions, and sets up the shape of the template for each class. Using three channels and a weighted averaging method to track action potential (AP) shape changes improve the analysis. In the last step, nonclassified segments, i.e., segments containing superimposed APs, are decomposed into their units using class-mean signals. Based on experiments on simulated and long-term recorded EMG signals, our software is capable of providing reliable decompositions with satisfying accuracy. EMG-LODEC is suitable for the study of MU discharge patterns and recruitment order in healthy subjects and patients during long-term measurements.

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.…”

“…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. A MUP begins when its sample values exceed a threshold and ends when they fall below this threshold [54], [55], [59], [70], [71]. If a fixed-length window is chosen for analysis, the length of the window is set to include a fixed duration, typically 2.5 ms [1], [7], [19], [26], [27], [72], [74], [76][77][78], [81][82][83] or 6 ms [58], [66], [67].…”

“…Raw-data (time samples) and first-or second -derivative of time samples [1], [5][6][7], [19], [25], [27], [72], [74], [76][77][78][79][80], [82], [86], [87], power spectrum and Fourier transform coefficients [45], [49], [50], [62], wavelet coefficients [53][54][55], [59], [59], [79], [81], [83], [88][89][90][91][92][93][94], and principal components of wavelet coefficients [95] are features that have been used to represent and assign MUPs to MUPTs. Using power spectrum coefficients [62] or wavelet coefficients of MUPs decreases the dimensionality of the feature space and hence may improve the processing time.…”