Hervé Rix

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>>

Alignment methods for averaging of high-resolution cardiac signals: a comparative study of performance

A comparative study of the performance of three alignment methods (the double-level method, a new time-delay estimation method based on normalized integrals, and matched filtering) is presented. A real signal and additive random noise for several signal-to-noise ratios (SNRs) are selected to make an ensemble of computer-simulated beats. The relation between the standard deviation of temporal misalignment versus SNR is discussed. A second study with real ECG signals is also presented. Several morphologies of QRS and P waves are tested. The results are in agreement with the computer simulation study. Nevertheless, the power spectrum of the noise process can affect the results. Matched filter estimation has been tested in the presence of power line interferences (50 Hz), with poor results. An application of the three alignment methods as a function of the SNR is proposed. The new time-delay estimation method has been observed to be robust, even in the presence of nonwhite noise.<<ETX>>

Adaptive estimation of QRS complex wave features of ECG signal by the Hermite model

The most characteristic wave set in ECG signals is the QRS complex. Automatic procedures to classify the QRS are very useful in the diagnosis of cardiac dysfunctions. Early detection and classification of QRS changes are important in realtime monitoring. ECG data compression is also important for storage and data transmission. An Adaptive Hermite Model Estimation System (AHMES) is presented for on-line beat-to-beat estimation of the features that describe the QRS complex with the Hermite model. The AHMES is based on the multiple-input adaptive linear combiner, using as inputs the succession of the QRS complexes and the Hermite functions, where a procedure has been incorporated to adaptively estimate a width related parameter b. The system allows an efficient real-time parameter extraction for classification and data compression. The performance of the AHMES is compared with that of direct feature estimation, studying the improvement in signal-to-noise ratio. In addition, the effect of misalignment at the QRS mark is shown to become a neglecting low-pass effect. The results allow the conditions in which the AHMES improves the direct estimate to be established. The application is shown, for subsequent classification, of the AHMES in extracting the QRS features of an ECG signal with the bigeminy phenomena. Another application is highlighted that helps wide ectopic beats detection using the width parameter b.

Detecting Small Variations in Shape

A method for the detection of very small differences between the shapes of two signals is presented. This method uses the normalized integrals of the signals to be compared; hence it filters the noise. After a general presentation giving an especially detailed account of the definition of equality of two shapes, the possible application fields with respect to the intrinsic properties of the method is analyzed. It makes it possible to test the fitting of a signal to a model, but chiefly, to detect small shape differences between two signals without needing any modeling of the signals or the systems from which they come. Finally it is shown how the method was applied to the particular problem of detecting twocomponent chromatographic peaks visually confounded and with low resolution.

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>>

Weighted averaging using adaptive estimation of the weights

Abstract Signal averaging is often used in biomedical signal processing. Unfortunately the classical hypothesis of perfect alignment of equal shape signals embedded in stationary and uncorrelated noise is rarely exactly verified. So the optimum improvement may be not achieved, and the estimated mean signal may be quite different from an individual realization. In this work we are interested in the noise power variation. In this case, it is more appropriate to use a weighted averaging instead of a uniform one. The main difficulty is the estimation of the signal to noise ratio for each realization in order to find the optimum weights. We show here that the optimum weights can be estimated adaptively using a constrained minimization algorithm. The constraint ensures that the estimated amplitude of the signal is not altered by the averaging procedure. Three different algorithms to estimate the optimal weights are presented and compared using simulations: (1) a direct method based on Lagrange multiplier; (2) an indirect method based on the penalty method; (3) a method based on the resolution of Kuhn and Tucker equations by means of Newton method. From the simulations, the Kuhn and Tucker method has the best performances and is used in the analysis of real electrocardiograms.

Jitter statistics estimation in alignment processes

Abstract When noisy repetitive signals are observed, the noise cancellation is achieved using the synchronous averaging method but the counterpart of such an approach is the difficulty to have perfectly aligned signals. We are interested in characterizing the departures from perfect alignment; we propose a novel method to estimate both the jitter variance without estimating the delays, and the amplitude fluctuations.

Effect of recovery on the cortisol circadian rhythm of depressed patients

Disturb~ces of the circadian rhythm of cortisol have been reported in endogenous depression, with an altered 24-hr rhythm, a reduction of amplitude, and an early timing of secretion (Lorhen& et al. f969; Halbreich et al. 1985; Linkowski et al. 1985). This latter result has been used to support the chronobiol~gical hypothesis of endogenous depression (Wehr and Wirz-Justice I98 I ), which postulates that an early timing or phase advance of circadian rby~ms may be involved in the physiopathology of depressian. However, conflicting results have been reported concerning the timing of the cortisol circadian rhythm in depression (Sachar et al. 1974, Fullerton et al. 1968), and very few data have been published concerning the circadian rhythm of cortisol in remission.

Orthonormal (Fourier and Walsh) models of time-varying evoked potentials in neurological injury

The hypothesis that injury-related changes in evoked potential (EP) signals can be modeled by orthonormal basis functions is tested. Two models of time-varying EP signals are evaluated: the Fourier series model (FSM) and the Walsh function model (WFM). The Fourier and Walsh coefficients are estimated with the aid of an adaptive least-mean-squares (LMS) technique. Results from computer simulations illustrate how selection of model order and of the adaptation rate of the estimator affect the signal-to-noise ratio (SNR). The FSM results in a somewhat higher steady-state SNR than does the WFM; however, the WFM is less computationally complex than is the FSM. These two orthonormal functions are used to evaluate transient response to hypoxic hypoxia in anesthetized cats. Trends of the first five frequencies (Fourier) and sequencies (Walsh) show that the lower frequencies and sequencies may be sensitive indicators of hypoxic neurological injury.<<ETX>>