Automated Biomedical Signal Quality Assessment of Electromyograms: Current Challenges and Future Prospects

Abstract

This work is an exposition of research supporting efforts to automate quality assessment in surface electromyography (sEMG). Electromyography measures electrical activity from skeletal muscles, which are the muscles associated with voluntary movements. Skeletal muscles can consist of tens to hundreds of thousands of contractile fibers which are grouped into functional units called Motor Units (MUs). Each MU is driven to contract its fibers by electrochemical impulses sent from a motor neuron, and a muscle may contain anywhere from tens to hundreds of MUs. When an MU is activated, all fibers associated with it are simultaneously activated. When fibers are activated, an electrochemical impulse propagates along each of them to cause contraction. This impulse is known as a Single Fiber Action Potential (SFAP). A Motor Unit Action Potential (MUAP) is the summation of all SFAPs corresponding to an activated MU. To generate a continuous contraction, MUs are repeatedly activated, producing a train of MUAPs, and this pulse train is called the Motor Unit Action Potential Train (MUAPT). The combined electrical activity of all of the MUAPTs associated with a contraction, as recorded on the surface of the skin using non-invasive electrodes, is a surface electromyography (sEMG) signal. In general, it is not possible to directly observe the SFAPs, MUAPs, and MUAPTs non-invasively (though there have been algorithmic efforts to decompose sEMG signals into its constituent MUAPTs). SFAPs and MUAPTs can be observed by inserting needle electrodes into specific muscle regions for high spatial selectivity; however, such techniques are invasive, making surface measurements a desirable alternative. sEMG signals have been used in a wide variety of applications, including fatigue assessment, myoelectric control, diagnosis of neuromuscular disorders, and tracking performance in sports.