Biomedical subjects

O Meste

Publications and source records attributed to O Meste.

7 recordsLinked to original sources

PubMed2005

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 (f(RSA)). 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. f(RSA) was extracted from heart period series using an evolutive model. First (T(V1)) and second (T(V2)) ventilatory thresholds were determined from the time course curves of ventilation and ventilatory equivalents for O(2) and CO(2). RESULTS: f(RSA) 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 f(RSA), defining the first (T(RSA1)) and second (T(RSA2)) f(RSA) thresholds. When expressed as a function of power, T(RSA1) and T(RSA2) were not significantly different from and closely linked to T(V1) (r = 0.99, p<0.001) and T(V2) (r = 0.99, p<0.001), respectively. In the five remaining subjects, only one non-linear increase was observed close to T(V2). Significant differences (p<0.04) were found between athlete and sedentary groups when T(RSA1) and T(RSA2) were expressed in terms of absolute and relative power and percentage of maximal aerobic power. In the sedentary group, T(RSA1) and T(RSA2) were 150.3 (18.7) W and 198.3 (28.8) W, respectively, whereas in the athlete group T(RSA1) and T(RSA2) were 247.3 (32.8) W and 316.0 (28.8) W, respectively. CONCLUSIONS: Dynamic analysis of f(RSA) provides a useful tool for identifying ventilatory thresholds during graded and maximal exercise test in sedentary subjects and athletes.

Adult↗

PubMed2004

Averaging signals with random time shift and time scale fluctuations.

OBJECTIVES: Our main objective was to propose an alternative to the technique of signal averaging (SA) to avoid shape distortions due to jittering and scale fluctuations, leading to a mean shape signal rather than to an average one. METHODS: In case of both time shift and time scale fluctuations of the individual signals, the first point was to show what model makes it possible to interpret their expected action as a linear shift invariant filter followed by a scale invariant one. So, even in the case of equal shape signals, the average is clearly not the same shape. The second point was to propose another averaging process, using the normalized integrals and called Shape Averaging (ShA) which provides, in this case, a mean signal preserving the common shape. RESULTS: The performances of ShA were firstly shown by simulation. Shifted and scaled versions of a given signal, without and with additive noise, have been generated at random. The mean shape signal obtained by ShA was compared to the shifted and scaled signal using the exact average values of the shifts and scale factors. A very good reconstruction of the mean shape signal is obtained for SNR = 20 dB and quite good for 8 dB, especially compared to SA. The method was then applied to a series of M-waves coming from surface EMG signals. In this case, the comparison of ShA with SA makes it possible to appreciate the validity of equal shape signal hypothesis.

Algorithms↗

PubMed2001

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 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.

Action Potentials↗

PubMed2000

Risk evaluation of ventricular tachycardia using wavelet transform irregularity of the high-resolution electrocardiogram.

A new method for analysis of high-resolution ECG signals using a wavelet transform based on a modified Morlet function is presented. A polynomial filter is used to reduce low-frequency, high-amplitude noise components in the analysed signals. The method is tested on test ECG signals with simulated late potentials and finally verified on two post-infarction patient (PP) groups: 62 PPs with ventricular tachycardia and 44 PPs without arrhythmia. A new quantitative parameter, the irregularity factor, is proposed for discrimination between the study groups. The results show a significant difference in the parameter values for tachycardia patients compared with those for patients without arrhythmia. The sensitivity of the proposed method is 85%, and the specificity is 93%.

Adult↗

PubMed2000

Estimation of scale factors in presence of multiple signals: application to sEMG analysis.

When several realizations of an unknown recurrent signal are observed apart from a time expansion or compression, the classical way of estimating these time scaling factors is to take one signal as reference for the estimation. This approach does not take into account the common information between all possible couples of realizations. To achieve this task we use a Maximum-Likelihood based method, in a sub-optimal manner. Using some realistic assumptions and simplifications, we propose a tractable solution. The improvement of classical results is shown through a simulation whose conclusion is that the larger the number of realizations, the more correct the estimation. Finally, we apply the method to electrically evoked sEMG.

Computer Simulation↗

PubMed1994

Ventricular late potentials characterization in time-frequency domain by means of a wavelet transform.

The main transforms of Cohen's class allow signal representation simultaneously in time and frequency domains. Wavelet transforms make it possible to link the temporal window width to the analyzing frequency and leads to a "modified wavelet transform" which improves resolution both in time and frequency. A simulation study illustrates the artifacts of every time-frequency representation (TFR) on pure sinusoids and gives performance evaluation of the different methods when searching a sinusoid embedded in a QRS complex. Analyses of real signals from healthy and pathological subjects confirm the simulation results and complete the characterization of ventricular late potentials (VLP) yet detected by signal averaging.

Action Potentials↗

PubMed1992

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

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.

Algorithms↗