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Decomposition of multichannel surface electromyograms

Final Activity Report Summary - DE MUSE (Decomposition of multichannel surface electromyograms)

Surface electromyography (sEMG) consists of non-invasive measurement of electrical activity of muscle fibres at the skin surface above the observed muscle and has been under intense investigation for several decades. One of the most important, but also most challenging issues is sEMG decomposition into its constituent components, i.e. electrical potentials of small functional groups of muscle fibres, called motor units (MU). Human skeletal muscles comprise several tens of MUs. Identification of their electrical activities in vivo represents the basis for the advanced research concerning the assessment of various neuromuscular pathologies and their treatments, work-related disorders, myoelectrical manifestations of fatigue, and for assessments of effectiveness in motor rehabilitation. Activity of individual MUs is classically assessed by intramuscular EMG, which has high spatial selectivity and thus comprises the contributions of a relatively small number of MUs whose fibres are close to the recording point. Because this method is invasive, it has limitations in cases when needle insertion is not possible or not desirable, such as in clinical examinations of children, professional athletes, patients with transplanted limbs or haemophilia, and, in general, in dynamic conditions, during work, sport or space activities.

Decomposition of sEMG surpasses aforementioned problems, but represents considerably more challenging problem than decomposition of intramuscular EMG, mainly due to the low selectivity of surface acquisition system and filtering effect of subcutaneous tissue. Most of the existing sEMG decomposition methods fail to identify complete MU discharge patterns. The gradient Convolutive Kernel Compensation (gCKC) approach, developed in the DEMUSE project, overcomes this limitation and provides a good approximation of complete MU discharge patterns. The method is nonparametric, fully automatic and relies minimally on anatomic properties of the investigated muscle. Reconstructed MU discharge patterns are automatically sorted with respect to the estimated degree of decomposition reliability.

The gCKC method has been tested in a variety of isometric conditions, including simulated and experimental signals acquired at constant and variable force levels from more than 10 different muscles, including biceps brachii, upper trapezius, gastrocnemius, soleus, tibialis anterior and external anal sphincter. Altogether, more than 150 subjects participated to aforementioned experiments and more than 10.000 motor units were identified in vivo. In all these tests, the gCKC decomposition identified complete discharge patterns of up to 25 concurrently active MUs, more than any other existing surface EMG decomposition method.

Along with the gCKC technique, interactive decomposition tool was implemented. This tool runs on a standard personal computer and enables the user to load, visualise and decompose multichannel sEMG signals, inspect and manually edit automatically identified MU discharge patterns and assess various MU parameters (e.g. discharge rate, variability of inter-discharge interval, muscle fibre conduction velocity). The tool is particularly suitable for non-invasive research in occupational, sport and rehabilitation medicine, ergonomics and space medicine. Possible collateral applications include objective assessment of effectiveness of rehabilitation and training of athletes, prevention of work-related neuromuscular disorders and diseases, and monitoring of the musculoskeletal deterioration in the microgravity environment.

Although supported by strong scientific results, the developed decomposition of sEMG does not aim at substituting for the classic intramuscular recordings, which remain the standard for MU studies. Rather, the non-invasive approach may be useful in cases when the use of needles is not possible or not desirable or may be applied together with intramuscular EMG to increase the number of identified MUs.