This work addresses the problem of estimating the conduction velocity (CV) of single motor unit (MU) action potentials from surface EMG signals detected with linear electrode arrays during voluntary muscle contractions. In ideal conditions, that is without shape or scale changes of the propagating signals and with additive white Gaussian noise, the maximum likelihood (ML) is the optimum estimator of delay. Nevertheless, other methods with computational advantages can be proposed; among them, a modified version of the beamforming algorithm is presented and compared with the ML estimator. In real cases, the resolution in delay estimation in the time domain is limited because of the sampling process. Transformation to the frequency domain allows a continuous estimation. A fast, high-resolution implementation of the presented multichannel techniques in the frequency domain is proposed. This approach is affected by a negligible decrease in performance with respect to ideal interpolation. Application of the ML estimator, based on two-channel information, to ten firings of each of three MUs provides a CV estimate affected by a standard deviation of 0.5 m s(-1); the modified beamforming and ML estimators based on five channels provide a CV standard deviation of less than 0.1 m s(-1) and allow the detection of statistically significant differences between the CVs of the three MUs. CV can therefore be used for MU classification.

Blain, Grégory, Olivier Meste, and Stéphane Bermon. Influences of breathing patterns on respiratory sinus arrhythmia in humans during exercise. Am J Physiol Heart Circ Physiol 288: H887-H895, 2005. First published September 23, 2004 doi:10.1152/ajpheart. 00767.2004.-Persistence of respiratory sinus arrhythmia (RSA) has been described in humans during intense exercise and attributed to an increase in ventilation. However, the direct influence of ventilation on RSA has never been assessed. The dynamic evolution of RSA and its links to ventilation were investigated during exercise in 14 healthy men using an original modeling approach. An evolutive model was estimated from the detrended and high-pass-filtered heart period series. The instantaneous RSA frequency (FRSA, in Hz) and amplitude (ARSA, in ms) were then extracted from all recordings. ARSA was calculated with short-time Fourier transform. First, measurements of FRSA and ARSA were performed from data obtained during a graded and maximal exercise test. Influences of different ventilation regimens [changes in tidal volume (VT) and respiratory frequency (FR)] on ARSA were then tested during submaximal [70% peak O2 consumption (V O2 peak)] rectangular exercise bouts. Under graded and maximal exercise conditions, ARSA decreased from the beginning of exercise to 61.9 Ϯ 3.8% V O2 peak and then increased up to peak exercise. During the paced breathing protocol, normoventilation (69.4 Ϯ 8.8 l/min), hyperventilation (81.8 Ϯ 8.3 l/min), and hypoventilation (56.4 Ϯ 6.2 l/min) led to significantly (P Ͻ 0.01) different ARSA values (3.8 Ϯ 0.5, 4.6 Ϯ 0.8, and 2.9 Ϯ 0.5 ms, respectively). In addition, no statistical difference was found in ARSA when ventilation was kept constant, whatever the FR-VT combinations. Those results indicate that RSA persists for all exercise intensities and increases during the highest intensities. Its persistence and increase are strongly linked to both the frequency and degree of lung inflation, suggesting a mechanical influence of breathing on RSA. cardiorespiratory coupling; heart period variability; paced breathing; time-varying model

This study introduces singular spectrum decomposition (SSD), a new adaptive method for decomposing nonlinear and nonstationary time series in narrow-banded components. The method takes its origin from singular spectrum analysis (SSA), a nonparametric spectral estimation method used for analysis and prediction of time series. Unlike SSA, SSD is a decomposition method in which the choice of fundamental parameters has been completely automated. This is achieved by focusing on the frequency content of the signal. In particular, this holds for the choice of the window length used to generate the trajectory matrix of the data and for the selection of its principal components for the reconstruction of a specific component series. Moreover, a new definition of the trajectory matrix with respect to the standard SSA allows the oscillatory content in the data to be enhanced and guarantees decrease of energy of the residual. Through the numerical examples and simulations, the SSD method is shown to be able to accurately retrieve different components concealed in the data, minimizing at the same time the generation of spurious components. Applications on time series from both the biological and the physical domain are also presented highlighting the capability of SSD to yield physically meaningful components.

In this paper, an approach for heart rate variability analysis during exercise stress testing is proposed based on the integral pulse frequency modulation (IPFM) model, where a time-varying threshold is included to account for the nonstationary mean heart rate. The proposed technique allows the estimation of the autonomic nervous system (ANS) modulating signal using the methods derived for the IPFM model with constant threshold plus a correction, which is shown to be needed to take into account the time-varying mean heart rate. On simulations, this technique allows the estimation of the ANS modulation on the heart from the beat occurrence time series with lower errors than the IPFM model with constant threshold (1.1% ± 1.3% versus 15.0% ± 14.9%). On an exercise stress testing database, the ANS modulation estimated by the proposed technique is closer to physiology than that obtained from the IPFM model with constant threshold, which tends to overestimate the ANS modulation during the recovery and underestimate it during the initial rest.

Ependymal cell cilia help move cerebrospinal fluid through the cerebral ventricles, but the regulation of their beat frequency remains unclear. Using in vitro, high-speed video microscopy and in vivo magnetic resonance imaging in mice, we found that the metabolic peptide melanin-concentrating hormone (MCH) positively controlled cilia beat frequency, specifically in the ventral third ventricle, whereas a lack of MCH receptor provoked a ventricular size increase.

We hypothesized that exercise performance is adjusted during repeated sprints in order not to surpass a critical threshold of peripheral fatigue. Twelve men randomly performed three experimental sessions on different days, i.e. one single 10 s all-out sprint and two trials of 10 × 10 s all-out sprints with 30 s of passive recovery in between. One trial was performed in the unfatigued state (CTRL) and one following electrically induced quadriceps muscle fatigue (FTNMES). Peripheral fatigue was quantified by comparing pre- with postexercise changes in potentiated quadriceps twitch force (ΔQtw-pot) evoked by supramaximal magnetic stimulation of the femoral nerve. Central fatigue was estimated by comparing pre- with postexercise voluntary activation of quadriceps motor units. The root mean square (RMS) of the vastus lateralis and vastus medialis EMG normalized to maximal M-wave amplitude (RMS.Mmax (-1)) was also calculated during sprints. Compared with CTRL condition, pre-existing quadriceps muscle fatigue in FTNMES (ΔQtw-pot = -29 ± 4%) resulted in a significant (P < 0.05) reduction in power output (-4.0 ± 0.9%) associated with a reduction in RMS.Mmax (-1). However, ΔQtw-pot postsprints decreased by 51% in both conditions, indicating that the level of peripheral fatigue was identical and independent of the degree of pre-existing fatigue. Our findings show that power output and cycling EMG are adjusted during exercise in order to limit the development of peripheral fatigue beyond a constant threshold. We hypothesize that the contribution of peripheral fatigue to exercise limitation involves a reduction in central motor drive in addition to the impairment in muscular function.

Many bioelectric signals result from the electrical response of physiological systems to an impulse that can be internal (ECG signals) or external (evoked potentials). In this paper an adaptive impulse correlated filter (AICF) for event-related signals that are time-locked to a stimulus is presented. This filter estimates the deterministic component of the signal and removes the noise uncorrelated with the stimulus, even if this noise is colored, as in the case of evoked potentials. The filter needs two inputs: the signal (primary input) and an impulse correlated with the deterministic component (reference input). We use the LMS algorithm to adjust the weights in the adaptive process. First, we show that the AICF is equivalent to exponentially weighted averaging (EWA) when using the LMS algorithm. A quantitative analysis of the signal-to-noise ratio improvement, convergence, and misadjustment error is presented. A comparison of the AICF with ensemble averaging (EA) and moving window averaging (MWA) techniques is also presented. The adaptive filter is applied to real high-resolution ECG signals and time-varying somatosensory evoked potentials.

BackgroundDiabetes is associated with prolongation of the QT interval of the electrocardiogram and enhanced dispersion of ventricular repolarization, factors that, together with atherosclerosis and myocardial ischemia, may promote the occurrence of electrical disorders. Thus, we tested the possibility that alterations in transmembrane ionic currents reduce the repolarization reserve of myocytes, leading to action potential (AP) prolongation and enhanced beat‐to‐beat variability of repolarization.Methods and ResultsDiabetes was induced in mice with streptozotocin (STZ), and effects of hyperglycemia on electrical properties of whole heart and myocytes were studied with respect to an untreated control group (Ctrl) using electrocardiographic recordings in vivo, ex vivo perfused hearts, and single‐cell patch‐clamp analysis. Additionally, a newly developed algorithm was introduced to obtain detailed information of the impact of high glucose on AP profile. Compared to Ctrl, hyperglycemia in STZ‐treated animals was coupled with prolongation of the QT interval, enhanced temporal dispersion of electrical recovery, and susceptibility to ventricular arrhythmias, defects observed, in part, in the Akita mutant mouse model of type I diabetes. AP was prolonged and beat‐to‐beat variability of repolarization was enhanced in diabetic myocytes, with respect to Ctrl cells. Density of Kv K+ and L‐type Ca2+ currents were decreased in STZ myocytes, in comparison to cells from normoglycemic mice. Pharmacological reduction of Kv currents in Ctrl cells lengthened AP duration and increased temporal dispersion of repolarization, reiterating features identified in diabetic myocytes.ConclusionsReductions in the repolarizing K+ currents may contribute to electrical disturbances of the diabetic heart.