Olivier Meste

Estimation of single motor unit conduction velocity from surface electromyogram signals detected with linear electrode arrays.

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 ms−1; the modified beamforming and ML estimators based on five channels provide a CV standard deviation of less than 0.1 ms−1 and allow the detection of statistically significant differences between the CVs of the three MUs. CV can therefore be used for MU classification.

Adaptive filter for event-related bioelectric signals using an impulse correlated reference input: comparison with signal averaging techniques

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). The LMS algorithm is used to adjust the weights in the adaptive process. It is shown that the AICF is equivalent to exponentially weighted averaging (FWA) 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.<<ETX>>

The Integral Pulse Frequency Modulation Model With Time-Varying Threshold: Application to Heart Rate Variability Analysis During Exercise Stress Testing

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.

Exercise performance is regulated during repeated sprints to limit the development of peripheral fatigue beyond a critical threshold

What is the central question of this study? We asked whether exercise performance is regulated during all‐out repeated sprints (involving peak cycling‐specific muscle activation and limited pacing strategy) in order to restrain the total degree of peripheral fatigue development. What is the main finding and its importance? Our results showed that power output is adjusted during repeated sprints to limit the development of peripheral fatigue to a critical threshold, independently of the degree of pre‐existing fatigue. These findings emphasize the important role of peripheral fatigue in adjustment of power output during exercise.

Time-frequency analysis of heart rate variability reveals cardiolocomotor coupling during dynamic cycling exercise in humans

To test the hypothesis that cycling exercise modulates heart rate variability (HRV), we applied a short-time Fourier transform on the electrocardiogram of subjects performing a maximal graded cycling test. A pedaling frequency component (PFC) in HRV was continuously observed over the time course of the exercise test and extracted from R-R interval series obtained from 15 healthy subjects with a heterogeneous physical fitness, exercising at three different pedaling frequency (n = 5): 70, 80, and 90 rpm. From 30 to 50% of the maximal power output (P(max)), in the 90 rpm group, spectral aliasing caused PFC to overlap with the respiratory sinus arrhythmia (RSA) band, significantly overestimating the PFC amplitude (A(PFC)). In the meantime, A(PFC) did not increase significantly from its minimal values in the 70 rpm ( approximately 1.26 ms) and 80 rpm ( approximately 1.20 ms) groups. Then, from 60 to 100% maximal power output (P(max)), workload increase caused a significant approximately 2.8-, approximately 3.3-, and approximately 3.4-fold increase in A(PFC) in the 70, 80, and 90 rpm groups, respectively, with no significant difference between groups. At peak exercise, A(PFC) accounted for approximately 43, approximately 39, and approximately 49% of the total HRV in the 70, 80, and 90 rpm groups, respectively. Our findings indicate that cycling continuously modulates the cardiac chronotropic response to exercise, inducing a new component in HRV, and that workload increase during intense exercise further accentuates this cardiolocomotor coupling. Moreover, because PFC and RSA overlapped at low workloads, methodological care should be taken in future studies aiming to quantify RSA as an index of parasympathetic activity.

Time-varying analysis methods and models for the respiratory and cardiac system coupling in graded exercise

The analysis of heart period series is a difficult task especially under graded exercise conditions. From all the information present in these series, we are the most interested in the coupling between respiratory and cardiac systems, known as respiratory sinus arrythmia. In this paper, we show that precise patterns concerning the respiratory frequency can be extracted from the heart period series. An evolutive model is introduced in order to achieve tracking of the main respiratory-related frequencies and their time-varying amplitudes. Since respiration acts to modulate the sinus rhythm, we relate the frequencies and amplitudes to this modulation by analyzing in detail its nonlinear transformation giving the heart period signal. This analysis is performed assuming stationary conditions but also in the realistic case where the mean heart period, the amplitude, and the frequency of the respiration are time-varying. Since this paper is devoted to the theoretical and complete presentation of the method used in a physiological study published elsewhere, the capabilities of our method will be illustrated in a realistic simulated case.

Assessment of ventilatory thresholds during graded and maximal exercise test using time varying analysis of respiratory sinus arrhythmia

Objective: To test whether ventilatory thresholds, measured during an exercise test, could be assessed using time varying analysis of respiratory sinus arrhythmia frequency (fRSA). Methods: Fourteen sedentary subjects and 12 endurance athletes performed a graded and maximal exercise test on a cycle ergometer: initial load 75 W (sedentary subjects) and 150 W (athletes), increments 37.5 W/2 min. fRSA was extracted from heart period series using an evolutive model. First (TV1) and second (TV2) ventilatory thresholds were determined from the time course curves of ventilation and ventilatory equivalents for O2 and CO2. Results:fRSA was accurately extracted from all recordings and positively correlated to respiratory frequency (r = 0.96 (0.03), p<0.01). In 21 of the 26 subjects, two successive non-linear increases were determined in fRSA, defining the first (TRSA1) and second (TRSA2) fRSA thresholds. When expressed as a function of power, TRSA1 and TRSA2 were not significantly different from and closely linked to TV1 (r = 0.99, p<0.001) and TV2 (r = 0.99, p<0.001), respectively. In the five remaining subjects, only one non-linear increase was observed close to TV2. Significant differences (p<0.04) were found between athlete and sedentary groups when TRSA1 and TRSA2 were expressed in terms of absolute and relative power and percentage of maximal aerobic power. In the sedentary group, TRSA1 and TRSA2 were 150.3 (18.7) W and 198.3 (28.8) W, respectively, whereas in the athlete group TRSA1 and TRSA2 were 247.3 (32.8) W and 316.0 (28.8) W, respectively. Conclusions: Dynamic analysis of fRSA provides a useful tool for identifying ventilatory thresholds during graded and maximal exercise test in sedentary subjects and athletes.

Time Delay Estimation: A New Insight Into the Woody's Method

This letter introduces a new insight into the Woodys method, a well-known time delay estimator. Firstly, we show that this classical technique used to analyze a variable latency signal is suboptimal. Thereby, we present an improved version of the Woodys method that formalizes and outperforms the previous one with an unknown signal using an iterative maximum likelihood estimator. Unlike recent approaches, the problem is expressed in the time domain in order to produce a general framework that allows the inclusion of some a priori information.

Detection of late potentials by means of wavelet transform

The wavelet transform, leading to a time-scale analysis, was used as a detection tool for ventricular late potentials. Preliminary results show that the detection is obvious from 16 beats and needs no high amplification or preliminary filtering. The experimental results are validated by averaging and comparing highly amplified filtered signals obtained from both a healthy person and a patient with a ventricular tachycardia diagnosis.<<ETX>>

Noninvasive Assessment of the Complexity and Stationarity of the Atrial Wavefront Patterns During Atrial Fibrillation

A novel automated approach to quantitatively evaluate the degree of spatio-temporal organization in the atrial activity (AA) during atrial fibrillation (AF) from surface recordings, obtained from body surface potential maps (BSPM), is presented. AA organization is assessed by measuring the reflection of the spatial complexity and temporal stationarity of the wavefront patterns propagating inside the atria on the surface ECG, by means of principal component analysis (PCA). Complexity and stationarity are quantified through novel parameters describing the structure of the mixing matrices derived by the PCA of the different AA segments across the BSPM recording. A significant inverse correlation between complexity and stationarity is highlighted by this analysis. The discriminatory power of the parameters in identifying different groups in the set of patients under study is also analyzed. The obtained results present analogies with earlier invasive studies in terms of number of significant components necessary to describe 95% of the variance in the AA (four for more organized AF, and eight for more disorganized AF). These findings suggest that automated analysis of AF organization exploiting spatial diversity in surface recordings is indeed possible, potentially leading to an improvement in clinical decision making and AF treatment.