Muscle fiber conduction velocity estimation by using normalized peak-averaging technique

Clinical applications of surface electromyography in neuromuscular disorders

Surface electromyography (SEMG) is still rarely used in clinical settings for the detection and analysis of myoelectric signals. The electromyographic signal detected on the skin surface includes information from a greater proportion of the muscle of interest than conventional clinical EMG, acquired using needle electrodes. SEMG is therefore more representative than the localised, and thus very selective needle EMG signals currently used. However, both reliability and interpretation of surface EMG need to be questioned. This review looks at the studies concerned with the characterisation of neuromuscular pathologies using EMG parameters. After introducing principles and limitations of surface EMG, an overview of the main results obtained in clinical settings is presented and discussed. There is a particular focus on high spatial resolution surface EMG as it is currently the best compromise between the selectivity of needle EMG and the representative nature of classical SEMG. Several perspectives are proposed that underline the fact that surface EMG is an evolving discipline and should be worthy of a place in routine clinical examinations.

A Technique to Track Individual Motor Unit Action Potentials in Surface EMG by Monitoring Their Conduction Velocities and Amplitudes

The speed of propagation of an action potential along a muscle fiber, its conduction velocity (CV), can be used as an indication of the physiological or pathological state of the muscle fiber membrane. The motor unit action potential (MUAP), the waveform resulting from the spatial and temporal summation of the individual muscle fiber action potentials of that motor unit (MU), propagates with a speed referred to as the motor unit conduction velocity (MUCV). This paper introduces a new algorithm, the MU tracking algorithm, which estimates MUCVs and MUAP amplitudes for individual MUs in a localized MU population using SEMG signals. By tracking these values across time, the electrical activity of the localized MU pool can be monitored. An assessment of the performance of the algorithm has been achieved using simulated SEMG signals. It is concluded that this analysis technique enhances the suitability of SEMG for clinical applications and points toward a future of noninvasive diagnosis and assessment of neuromuscular disorders.

Validation of estimated muscle fiber conduction velocity with the normalized peak-averaging technique

To estimate muscle fiber conduction velocity (MFCV), the normalized peak-averaging technique was developed along with complementary software and reported in a previous study. Electromyograms were recorded from the vicinity of end-plate zones (EMG data 1) and a site farther from the end-plates and distal tendons (EMG data 2) of biceps brachii muscles using surface array electrodes during 50% maximal voluntary isometric contractions in ten healthy volunteers. The MFCV values obtained with normalized peak-averaging technique were compared to those obtained with the cross-correlation technique and examined by computer simulation of the MFCV distribution. The MFCV values obtained with normalized peak-averaging technique highly correlated with those obtained with cross-correlation technique in both EMG data 1 and EMG data 2. It was also confirmed that MFCV values obtained from EMG data 1 were distributed much more widely than those obtained from EMG data 2. These results show the clinical usefulness of normalized peak-averaging technique for the detailed assessment of MFCV.