BUSCADOR RECOLECTA

It has been recurrently observed that, for compound muscle action potentials (M wave) recorded over the innervation zone of the vastus lateralis, the descending portion of the first phase generally shows an 'inflection' or 'shoulder'. We sought to clarify the electrical origin of this shoulder-like feature and examine its implications. M waves evoked by maximal single shocks to the femoral nerve were recorded in monopolar and bipolar configurations from 126 individuals using classical (10-mm recording diameter, 20-mm inter-electrode distance) electrodes and from eight individuals using small electrodes arranged in a linear array. The changes of the M-wave waveform at different positions along the muscle fibers' direction were examined. The shoulder was identified more frequently in monopolar (97%) than in bipolar (46%) M waves. The shoulder of M waves recorded at different distances from the innervation zone had the same latency. Furthermore, the shoulder of the M wave recorded over the innervation zone coincided in latency with the positive peak of that recorded beyond the muscle. The positive phase of the M wave detected 20 mm away from the innervation zone was essentially composed of non-propagating components. The shoulder-like feature in monopolar and bipolar M waves results from the termination of action potentials at the superficial aponeurosis of the vastus lateralis. We conclude that, only the amplitude of the first phase, and not
the second, of M waves recorded monopolarly and/or bipolarly in close proximity to the innervation zone can be used reliably to monitor possible changes in muscle membrane excitability., This work has been supported by the Spanish Ministerio de Economía y Competitividad (MINECO), under the TEC2014-58947-R project.

Scanning-EMG is an electrophysiological technique in which the electrical activity of the motor unit is recorded at multiple points along a corridor crossing the motor unit territory. Correct analysis of the scanning-EMG signal requires prior elimination of interference from nearby motor units. Although the traditional processing based on the median filtering is effective in removing such interference, it distorts the physiological waveform of the scanning-EMG signal. In this study, we describe a new scanning-EMG signal processing algorithm that preserves the physiological signal waveform while effectively removing interference from other motor units. To obtain a cleaned-up version of the scanning signal, the masked least-squares smoothing (MLSS) algorithm recalculates and replaces each sample value of the signal using a least-squares smoothing in the spatial dimension, taking into account the information of only those samples that are not contaminated with activity of other motor units. The performance of the new algorithm with simulated scanning-EMG signals is studied and compared with the performance of the median algorithm and tested with real scanning signals. Results show that the MLSS algorithm distorts the waveform of the scanning-EMG signal much less than the median algorithm (approximately 3.5 dB gain), being at the same time very effective at removing interference components., This work was supported by the Spanish Ministerio de Economía y Competitividad (MINECO), under the TEC2014-58947-R project.

(1) Background: The aim of this study was to investigate the validity and reliability of surface electromyography (EMG) for automatic detection of the aerobic and anaerobic thresholds during an incremental continuous cycling test using 1 min exercise periods in elite cyclists. (2) Methods: Sixteen well-trained cyclists completed an incremental exercise test (25 W/1 min) to exhaustion. Surface bipolar EMG signals were recorded from the vastus lateralis, vastus medialis, biceps femoris, and gluteus maximus, and the root mean square (RMS) were assessed. The multi-segment linear regression method was used to calculate the first and second EMG thresholds (EMG(T1) and EMG(T2)). During the test, gas exchange data were collected to determine the first and second ventilatory thresholds (VT1 and VT2). (3) Results: Two breakpoints (thresholds) were identified in the RMS EMG vs. time curve for all muscles in 75% of participants. The two breakpoints, EMG(T1) and EMG(T2) , were detected at around 70%-80% and 90%-95% of VO2MAX, respectively. No significant differences were found between the means of VT(1 )and EMGT(1) for the vastii and biceps femoris muscles (p > 0.05). There were no significant differences between means of EMG(T2) and VT2 (p > 0.05). (4) Conclusions: It is concluded that the multi-segment linear regression algorithm is a valid non-invasive method for analyzing the aerobic-anaerobic transition during incremental tests with 1 min stage durations., This research was funded by the Spanish Ministerio de Economía y Competitividad (MINECO), under the TEC2014-58947-R project.

We investigated whether correlation measures derived from pairs of motor unit (MU) spike trains are reliable indicators of the degree of common synaptic input to motor neurons. Several 50-s isometric contractions of the biceps brachii muscle were performed at different target forces ranging from 10 to 30% of the maximal voluntary contraction relying on force feedback. Forty-eight pairs of MUs were examined at various force levels. Motor unit synchrony was assessed by cross-correlation analysis using three indexes: the output correlation as the peak of the cross-histogram (ρ) and the number of synchronous spikes per second (CIS) and per trigger (E). Individual analysis of MU pairs revealed that ρ, CIS, and E were most often positively associated with discharge rate (87, 85, and 76% of the MU pairs, respectively) and negatively with interspike interval variability (69, 65, and 62% of the MU pairs, respectively). Moreover, the behavior of synchronization indexes with discharge rate (and interspike interval variability) varied greatly among the MU pairs. These results were consistent with theoretical predictions, which showed that the output correlation between pairs of spike trains depends on the statistics of the input current and motor neuron intrinsic properties that differ for different motor neuron pairs. In conclusion, the synchronization between MU firing trains is necessarily caused by the (functional) common input to motor neurons, but it is not possible to infer the degree of shared common input to a pair of motor neurons on the basis of correlation measures of their output spike trains., F. Negro has received funding from the European Union’s Horizon 2020 research and innovation program under Marie Skłodowska-Curie Grant 702491 (NeuralCon). This work has been supported by the Spanish Ministerio de Economía y Competitividad (MINECO), under the TEC2014-58947-R project.

Inter-discharge interval distribution modeling of the motor unit firing pattern plays an important role in electromyographic decomposition and the statistical analysis of firing patterns. When modeling firing patterns obtained from automatic procedures, false positives and false negatives can be taken into account to enhance performance in estimating firing pattern statistics. Available models of this type, however, are only approximate and use Gaussian distributions, which are not strictly suitable for modeling renewal point processes. In this paper, the theory of point processes is used to derive an exact solution to the distribution when a gamma distribution is used to model the physiological firing pattern. Besides being exact, the solution provides a way to model the skewness of the inter-discharge distribution, and this may make it possible to obtain a better fit with available experimental data. In order to demonstrate potential applications of the model, we use it to obtain a maximum likelihood estimator of firing pattern statistics. Our tests found this estimator to be reliable over a wide range of firing conditions, whether dealing with real or simulated firing patterns, the proposed solution had better agreement than other models., This work has been supported by the Spanish Ministerio de Economía y Competitividad (MINECO), under the TEC2014-58947-R project.

Objective: To evaluate the performance of a recently proposed motor unit action potential (MUAP) averaging method based on a sliding window, and compare it with relevant published methods in normal and
pathological muscles. Methods: Three versions of the method (with different window lengths) were compared to three relevant published methods in terms of signal analysis-based merit figures and MUAP waveform parameters used in the clinical practice. 218 MUAP trains recorded from normal, myopathic, subacute neurogenic and chronic neurogenic muscles were analysed. Percentage scores of the cases in which the methods obtained the best performance or a performance not significantly worse than the best were computed. Results: For signal processing figures of merit, the three versions of the new method performed better
(with scores of 100, 86.6 and 66.7%) than the other three methods (66.7, 25 and 0%, respectively). In terms of MUAP waveform parameters, the new method also performed better (100, 95.8 and 91.7%) than the
other methods (83.3, 37.5 and 25%). Conclusions: For the types of normal and pathological muscle studied, the sliding window approach extracted more accurate and reliable MUAP curves than other existing methods. Significance: The new method can be of service in quantitative EMG., This work has been supported by the Spanish Ministerio de Economía y Competitividad, under the TEC2014-58947-R project.

The motor unit profile, a representation of the trajectories of positive and negative turns of a scanning-EMG signal, is a new way to characterize the motor unit potential. Such characterization allows quantification of the scanning-EMG signal's complexity, which is closely related to the anatomy and physiology of the motor unit. To extract the motor unit profile, an algorithm that detects the turns of the scanning-EMG signal and links them using point-tracking techniques has been developed. The performance of this algorithm is sensitive to three parameters: the turn detection threshold, the maximum tracking interval threshold, and the trajectory purge threshold. Real scanning-EMG signals have been used to analyze the algorithm's behavior and the influence of the algorithm's parameters and to determine which parameter values provide the best performance., This work has been supported by the Spanish Ministerio de Economía y Competitividad (MINECO), under the TEC2014-58947-R project.

Objective: To evaluate the performance of a recently proposed motor unit action potential (MUAP) averaging method based on a sliding window, and compare it with relevant published methods in normal and
pathological muscles.
Methods: Three versions of the method (with different window lengths) were compared to three relevant
published methods in terms of signal analysis-based merit figures and MUAP waveform parameters used
in the clinical practice. 218 MUAP trains recorded from normal, myopathic, subacute neurogenic and
chronic neurogenic muscles were analysed. Percentage scores of the cases in which the methods obtained
the best performance or a performance not significantly worse than the best were computed.
Results: For signal processing figures of merit, the three versions of the new method performed better
(with scores of 100, 86.6 and 66.7%) than the other three methods (66.7, 25 and 0%, respectively). In terms
of MUAP waveform parameters, the new method also performed better (100, 95.8 and 91.7%) than the
other methods (83.3, 37.5 and 25%).
Conclusions: For the types of normal and pathological muscle studied, the sliding window approach
extracted more accurate and reliable MUAP curves than other existing methods.
Significance: The new method can be of service in quantitative EMG.