Julien Roussel

A new cyclostationarity-based blind approach for motor unit's firing rate automated detection in electromyographic signals

This work focuses on electromyographic (EMG) signal processing. We propose a new blind approach that aims at detecting the firing rates of the activated motor units. The proposed method is based on the fact that, EMGs can be modelled as second-order cyclostationary signals. After application of a Blind Source Separation (BSS) algorithm, we compute a cyclostationarity measure which is the Cyclic Spectral Density (CSD), and we show how one can use it to group the estimated components into independent subspaces and in an automated manner. The proposed classification procedure is based on the concept of subspace BSS techniques, like the Multidimensional Independent Component Analysis (MICA), the difference being that our method allows automatic classification of the estimated source signals. After discarding the subspace corresponding to the noise and computation of a modified CSD measure, the proposed procedure yields to the detection of specific cyclic frequencies corresponding to the discharge frequencies of the Motor Units Action Potential Trains (MUAPTs). Early results obtained from experiments on synthetic EMGs are presented in the paper and research perspectives conclude this work.

A New Approach to Tune the Vold-Kalman Estimator for Order Tracking

In the purpose to diagnose rotating machines using vibration signal, engineers use order tracking method to process non-stationary signals . We deal here with order tracking when the vibration signal is represented in a state space model. Such a methodology leads to the Kalman estimator that requires knowledge about the noise statistics affecting the state and the measurement equation. These noise statistics are usually unknown and need to be estimated from operating data for the use of the Kalman estimation algorithm. Several methods to tune these parameters have been developed for time-invariant model. In this paper, we introduce a technique to estimate the noise covariances for a linear time-variant system using the innovation process. The efficiency of this new approach is evaluated using a synthetic non-stationary vibration signal. The advantage of this approach is that it converges quickly and provides a small estimation error compared to those used for the linear time-invariant model.

Estimation of Cyclic Cumulants of Machinery Vibration Signals in Non-stationary Operation

Cyclic statistics have been proved to be a powerful tool for the study of rotating machinery vibration signals. Indeed, such signals usually exhibit cyclostationary features related to the shaft speed and to the geometry of the components. Cyclostationarity can be studied at order one (periodic deterministic components) or order 2 and more. Cyclic statistics at order N comprise a pure Nth order cyclostationary part and a contribution from orders 1 to N − 1. It may be interesting to study pure cyclostationarity at order N, i.e. to remove the influence of smaller orders. This can be done by computing cyclic cumulants instead of cyclic moments. In order to compute 2nd order cumulants of the vibration signal, one must remove from the signal the 1st order cyclostationary components, that is to say the deterministic periodic components. Some classical approaches have been proposed, based on synchronized averaging or Fourier transform. But some limitations appear when the vibration signal comprises components tied to different rotation frequencies (for instance in the case of gears) or under variable speed. The method that we propose in order to extract these periodic components is based on a biquad filter bank. Biquad filters have been extensively used in audio processing and allow building band-pass or notch filter banks at low computational cost. We show how such filters can be used to remove the 1st order cyclic components from the signal. An extension to variable speed operation is proposed by having the filters central frequency follow the variations of the rotation frequency. The technique is applied to simulated signals as well as real life signals.

Decomposition of Multi-Channel Intramuscular EMG Signals by Cyclostationary-Based Blind Source Separation

We propose a novel decomposition method for electromyographic signals based on blind source separation. Using the cyclostationary properties of motor unit action potential trains (MUAPt), it is shown that the MUAPt can be decomposed by joint diagonalization of the cyclic spatial correlation matrix of the observations. After modeling the source signals, we provide the proof of orthogonality of the sources and of their delayed versions in a cyclostationary context. We tested the proposed method on simulated signals and showed that it can decompose up to six sources with a probability of correct detection and classification >95%, using only eight recording sites. Moreover, we tested the method on experimental multi-channel signals recorded with thin-film intramuscular electrodes, with a total of 32 recording sites. The rate of agreement of the decomposed MUAPt with those obtained by an expert using a validated tool for decomposition was >93%.

Estimation of Foetal Contribution to ECG Recordings Using Oblique Projection Technique Exploiting Cyclostationary Properties of the Heartbeat Signals

In this paper, the authors propose to estimate the contribution of the electrical activity of the foetal heart to each component of cutaneous electrocardiogram (ECG) recordings using an oblique projection technique. To date, related research work reported in the literature using Blind Source Separation (BSS) or Independent Component Analysis (ICA) methods rely on a widely used projection techniques family, which is the orthogonal projection. More recently, reported work in other areas of biomedical research is based on the use of oblique projection techniques in order to remove the artifacts from the available recordings and to estimate the contribution of the Signal of Interest (SoI) to the data set. The novel approach presented in this paper is tailored to the problem of foetal ECG (FECG) contribution estimation. The optimal steering vector’s computation procedure for the oblique projection is based on the use of the cyclostationary properties of the SoI and on the concept of Multidimensional Independent Component Analysis (MICA). The latter is used to gather into independent subspaces the components estimated using a BSS/ICA method and corresponding to the foetus’ heartbeats, the mother’s heartbeats and various artifacts and noise sources. Early comparative results with the orthogonal approach obtained after application of both methods to synthetic non-invasive FECG recordings illustrate the reliability as well as the effectiveness of the proposed method. A discussion and perspectives for future research conclude this work.