Abstract
A novel electromyography (EMG) signal decomposition framework is presented for the thorough and precise analysis of intramuscular EMG signals. This framework first detects all of the active motor unit action potentials (MUAPs) and assigns single MUAP segments to their corresponding motor units. MUAP waveforms that are found to be superimposed are then resolved into their constituent single MUAPs using a peel-off approach and similarly assigned. The method is composed of six stages of analytical procedures: preprocessing, segmentation, alignment and feature extraction, clustering and refinement, supervised classification, and superimposed waveform resolution. The performance of the proposed decomposition framework was evaluated using both synthetic EMG signals and real recordings obtained from healthy and stroke participants. The overall detection rate of MUAPs was 100% for both synthetic and real signals. The average accuracy for synthetic EMG signals was 87.23%. Average assignment accuracies of 88.63 and 94.45% were achieved for the real EMG signals obtained from healthy and stroke participants, respectively. Results demonstrated the ability of the developed framework to decompose intramuscular EMG signals with improved accuracy and efficiency, which we believe will greatly benefit the clinical utility of EMG for the diagnosis and rehabilitation of motor impairments in stroke patients.
Highlights
Electromyography (EMG) signals carry information regarding the motor unit action potential trains (MUAPTs) generated by the motor units (MUs) that are recruited during muscle contraction
The goal of this EMG decomposition framework is to identify complete motor unit action potentials (MUAPs) segments in the EMG signal and classify them accurately into their constituent MUAPTs. Both the template waveforms and firing rates of the MUAPTs are largely dependent on the configuration of the needle electrode, the relative position of electrode to the muscle fibers, the level of contraction, and the pathological condition of the muscle
We developed an EMG signal decomposition framework based on a novel MUAP segmentation method and the resolution of superimposed MUAP waveforms
Summary
Electromyography (EMG) signals carry information regarding the motor unit action potential trains (MUAPTs) generated by the motor units (MUs) that are recruited during muscle contraction. Each MUAPT is made of a series of intermittent discharges that take the form of spatially dispersed individual motor unit action potentials (MUAPs). Intramuscular EMG is commonly acquired by means of indwelling needles or fine wire sensors that provide direct and targeted contact with the musculatures. Intramuscular EMG is used as a routine method for the electrophysiological examination of neuromuscular symptoms. EMG decomposition reverses the process of signal generation by separating the de-noised EMG signal into its constituent MUAPTs. EMG decomposition reverses the process of signal generation by separating the de-noised EMG signal into its constituent MUAPTs This process is accomplished by identifying MUAP waveforms
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