Several studies report gender differences in response to fatigue. Most results suggest that females have higher muscle endurance than males. Possible explanations lie on differences in muscle mass, substrate utilization, muscle morphology and neuromuscular activation. One relevant aspect not always considered is the hormonal fluctuations during the female menstrual cycle. The present work observed eighteen healthy and untrained adults (eight males, 26.9 +/- 4.0 yr and ten females, 24.0 +/- 2.8 yr) performing fatiguing isometric contractions to evaluate both the influence of menstrual cycle and gender differences in fatigability. Surface electromyographic signals were recorded from the biceps brachii using a linear electrode array of eight electrodes during 90 seconds at 40% of maximal voluntary contraction. Root mean square (RMS), mean frequency (MNF) the conduction velocity (CV) values were estimated using windows of 0.5 seconds. Female subjects showed overall lower fatigability, demonstrated by the lower mean CV decrease (1.494) compared to males (1.787). However, in periods of high decreases in hormones concentrations in females (the end of both the follicular and luteal phases), higher CV decreases were observed (1.921 and 2.183). These results indicate the need of considering the effects of hormonal fluctuations in females when observing gender effects on muscle fatigue.

The goal of this work is to study the behavior of electromyographic variables during the menstrual cycle. Ten female volunteers (24.0 ± 2.8 years of age) performed fatiguing isometric contractions, and electromyographic signals were measured on the biceps brachii in four phases of the menstrual cycle. Adaptations of classical algorithms were used for the estimation of the root mean square (RMS) value, absolute rectified value (ARV), mean frequency (MNF), median frequency (MDF), and conduction velocity (CV). The CV estimator had a higher (p = 0.002) rate of decrease at the end of the follicular phase and at the end of the luteal phase. The MDF (p = 0.002) and MNF (p = 0.004) estimators had a higher rate of decrease at the beginning of the follicular phase and at the end of the luteal phase. No significant differences between phases of the menstrual cycle were detected with the ARV and RMS estimators (p > 0.05). These results suggest that the behavior of the muscles in women presents different characteristics during different phases of the menstrual cycle. In particular, women were more susceptible to fatigue at the end of the luteal phase.

In surface electromyography (surface EMG, or S-EMG), conduction velocity (CV) refers to the velocity at which the motor unit action potentials (MUAPs) propagate along the muscle fibers, during contractions. The CV is related to the type and diameter of the muscle fibers, ion concentration, pH, and firing rate of the motor units (MUs). The CV can be used in the evaluation of contractile properties of MUs, and of muscle fatigue. The most popular methods for CV estimation are those based on maximum likelihood estimation (MLE). This work proposes an algorithm for estimating CV from S-EMG signals, using digital image processing techniques. The proposed approach is demonstrated and evaluated, using both simulated and experimentally-acquired multichannel S-EMG signals. We show that the proposed algorithm is as precise and accurate as the MLE method in typical conditions of noise and CV. The proposed method is not susceptible to errors associated with MUAP propagation direction or inadequate initialization parameters, which are common with the MLE algorithm. Image processing -based approaches may be useful in S-EMG analysis to extract different physiological parameters from multichannel S-EMG signals. Other new methods based on image processing could also be developed to help solving other tasks in EMG analysis, such as estimation of the CV for individual MUs, localization and tracking of innervation zones, and study of MU recruitment strategies.

The analysis of heart rate variability (HRV) aids in the diagnosis of various diseases related to the malfunction of the autonomic nervous system. Traditional approaches for analysis of HRV require the signal to be reasonably stationary during the period of observation. This is not possible when analyzing long duration signals. Detrended fluctuation analysis (DFA) is robust to this issue, as it removes external interferences ("trends") and considers only intrinsic characteristics which are present throughout the signal. DFA is typically performed by segmenting the signal into shorter windows. This has two undesirable effects: (i) if the signal length is not a multiple of the window length, then at least one window will have fewer samples than the others; and (ii) discontinuities are observed on the detrended signal at the edges of each window. Both issues may be addressed using a sliding window. We propose and evaluate this idea, comparing its results with those obtained using the traditional approach. Experiments using different kinds of random and real HRV signals are presented. Statistically significant differences were observed with the proposed approach, especially with respect to α2 values. The proposed method also presented a great reduction in α1 error for white noise, which is a good model for congestive heart failure, with respect to α1 correlations.

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