Different recoveries of the first and second phases of the M-wave after intermittent maximal voluntary contractions

We investigated the recovery of muscle electrical properties after intermittent intense exercise by examining separately the first and second phases of the muscle compound action potential (M-wave). M-waves and mechanical twitches were obtained using femoral nerve stimulation throughout the 30-min recovery period following 48 successive intermittent 3-s MVCs. The amplitude, duration, and area of the M-wave first and second phases, and the peak twitch force were measured from the knee extensors. The amplitudes of both the first and second M-wave phases were increased immediately after exercise (P < 0.05), but, whereas the first phase remained enlarged for 5 min after exercise, the increase of the second phase only lasted for 10 s. After 30 min of recovery, the amplitude, area, and duration of both the first and second phases were decreased compared to control values (10-20%, P < 0.05). A significant temporal association was found between the changes in the amplitude and duration of the M-wave first phase (maximal cross correlations, 0.9-0.93; time lag, 0 s). A significant, negative temporal relation was found between the amplitude of the M-wave first phase and the peak twitch force during recovery (P < 0.05). The prolonged enlargement of the M-wave first phase during recovery seems primarily related to fatigue-induced changes in membrane properties, whereas the extremely short recovery of the second phase might be related to changes in muscle architectural features. It is concluded that muscle excitability is impaired even after intermittent fatiguing contractions which allow partial clearance of extracellular K(+)

Detection of Multiple Innervation Zones from Multi-Channel Surface EMG Recordings with Low Signal-to-Noise Ratio Using Graph-Cut Segmentation

Knowledge of the location of muscle Innervation Zones (IZs) is important in many applications, e.g. for minimizing the quantity of injected botulinum toxin for the treatment of spasticity or for deciding on the type of episiotomy during child delivery. Surface EMG (sEMG) can be noninvasively recorded to assess physiological and morphological characteristics of contracting muscles. However, it is not often possible to record signals of high quality. Moreover, muscles could have multiple IZs, which should all be identified. We designed a fully-automatic algorithm based on the enhanced image Graph-Cut segmentation and morphological image processing methods to identify up to five IZs in 60-ms intervals of very-low to moderate quality sEMG signal detected with multi-channel electrodes (20 bipolar channels with Inter Electrode Distance (IED) of 5 mm). An anisotropic multilayered cylinder model was used to simulate 750 sEMG signals with signal-to-noise ratio ranging from -5 to 15 dB (using Gaussian noise) and in each 60-ms signal frame, 1 to 5 IZs were included. The micro- and macro- averaged performance indices were then reported for the proposed IZ detection algorithm. In the micro-averaging procedure, the number of True Positives, False Positives and False Negatives in each frame were summed up to generate cumulative measures. In the macro-averaging, on the other hand, precision and recall were calculated for each frame and their averages are used to determine F1-score. Overall, the micro (macro)-averaged sensitivity, precision and F1-score of the algorithm for IZ channel identification were 82.7% (87.5%), 92.9% (94.0%) and 87.5% (90.6%), respectively. For the correctly identified IZ locations, the average bias error was of 0.02±0.10 IED ratio. Also, the average absolute conduction velocity estimation error was 0.41±0.40 m/s for such frames. The sensitivity analysis including increasing IED and reducing interpolation coefficient for time samples was performed. Meanwhile, the effect of adding power-line interference and using other image interpolation methods on the deterioration of the performance of the proposed algorithm was investigated. The average running time of the proposed algorithm on each 60-ms sEMG frame was 25.5±8.9 (s) on an Intel dual-core 1.83 GHz CPU with 2 GB of RAM. The proposed algorithm correctly and precisely identified multiple IZs in each signal epoch in a wide range of signal quality and is thus a promising new offline tool for electrophysiological studies.peerReviewe