Philippe Ravier

Experimental muscle pain changes the spatial distribution of upper trapezius muscle activity during sustained contraction.

OBJECTIVE To investigate the effect of local excitation of nociceptive muscle afferents on the spatial distribution of muscle activity. METHODS Surface electromyographic (EMG) signals were recorded from the upper trapezius muscle of 10 healthy volunteers with a 5 x 13 electrode grid during 90-s isometric contractions before, during, 15 and 30 min after intramuscular injection of hypertonic (painful) or isotonic (non-painful) saline. From the multi-channel EMG recordings, two-dimensional maps of root mean square and mean power frequency were obtained. The centre of gravity of the root mean square map was used to quantify global changes in the spatial distribution of muscle activity. RESULTS During sustained contractions, average root mean square increased, average mean frequency decreased and the centre of gravity moved cranially. During experimental muscle pain, compared to before injection, the average root mean square decreased and there was a caudal shift of the centre of gravity. Fifteen minutes after the painful injection the centre of gravity returned to its original position. CONCLUSIONS Short-term dynamic reorganization of the spatial distribution of muscle activity occurred in response to nociceptive afferent input. SIGNIFICANCE The study furnishes an extension of the pain adaptation model indicating heterogeneous inhibition of muscle activity.

Wavelet packets and de-noising based on higher-order-statistics for transient detection

Abstract In this paper, we present a detector of transient acoustic signals that combines two powerful detection tools: a local wavelet analysis and higher-order statistical properties of the signals. The use of both techniques makes detection possible in low signal-to-noise ratio conditions, when other means of detection are no longer sufficient. The proposed algorithm uses the adapted wavelet packet transform. It leads to a partition of the signal which is ‘optimal’ according to a criterion that tests the Gaussian nature of the frequency bands. To get a time dependent detection curve, we perform a de-noising procedure on the wavelet coefficients: The Gaussian coefficients are set to zero. We then apply a classical method of detection on the time reconstructed de-noised signal. We study the performance of the detector in terms of experimental ROC curves. We show that the detector performs better than decompositions using other classical splitting criteria. In the last part, we present an application of the algorithm on real flow recordings of nuclear plant pipings. The detector indicates the presence of a missing body in the piping at some instants not seen with a classical energy detector.

Low bias histogram-based estimation of mutual information for feature selection

This paper presents a low bias histogram-based estimation of mutual information and its application to feature selection problems. By canceling the first order bias, the estimation avoids the bias accumulation problem that affects classical methods. As a consequence, on a synthetic feature selection problem, only the proposed method results in the exact number of features to be chosen in the Gaussian case when compared to four other approaches. In a speech recognition application, the proposed method and the Sturges method are the only ones that lead to a correct number of selected features in the noise free case. In the reduced data case, only the proposed method points out the optimal number of features to select. Finally, in the noisy case, only the proposed method leads to results of high quality; other methods show severely underestimated numbers of selected features.

GPS/Galileo Multipath Mitigation Using the First Side Peak of Double Delta Correlator

In this paper, we propose an efficient scheme for the Line of Sight (LOS) delay estimation in satellite telecommunications. It aims to reduce errors caused by the Multipath (MP) fading channels in Global Navigation Satellite System (GNSS) such as Global Positioning System (GPS) and future Galileo. The scheme is based on the use of first side peaks of Double Delta Correlator (DDC) in combination with Savitzky-Golay Filter (SGF). The proposed scheme is called “Enhanced DDC (EDDC)”. The DDC technique, like High Resolution Correlator (HRC) and Strobe Correlator (SC), uses the central peak of the Correlation Function (CF) for the LOS delay estimation. As a result, it enables to deal with the medium-delay MP. Yet, it presents a greater sensitivity to the noise and to the long-delay MP that we propose to mitigate, respectively, with the use of SGF and the measure of the first side peak (instead of the central). The obtained results show that our proposed scheme gives better performances over DDC scheme. In fact, it shows a significant error reduction caused by the presence of long-delay MP signals (up to 90%) compared to that of the DDC scheme.

Redefining Performance Evaluation Tools for Real-Time QRS Complex Classification Systems

In a heartbeat classification procedure, the detection of QRS complex waveforms is necessary. In many studies, this heartbeat extraction function is not considered: the inputs of the classifier are assumed to be correctly identified. This communication aims to redefine classical performance evaluation tools in entire QRS complex classification systems and to evaluate the effects induced by QRS detection errors on the performance of heartbeat classification processing (normal versus abnormal). Performance statistics are given and discussed considering the MIT/BIH database records that are replayed on a real-time classification system composed of the classical detector proposed by Hamilton and Tompkins, followed by a neural-network classifier. This study shows that a classification accuracy of 96.72% falls to 94.90% when a drop of 1.78% error rate is introduced in the detector quality. This corresponds to an increase of about 50% bad classifications.

Detection of PVC in ECG signals using fractional linear prediction

Abstract In this paper, we propose a modeling technique for the QRS complex based on the fractional linear prediction (FLP). As a result of FLP modeling, each QRS complex is represented by a vector of three coefficients. The FLP modeling evaluation is achieved in two steps. In the first step, the ability of the FLP coefficients to efficiently model QRS complex waves is assessed by comparison with the Linear Prediction (LP) coefficients through the signal-to-error (SER) values evaluated between the original waves and predicted ones. In the second step, the performance of several classifiers is used to evaluate the effectiveness and robustness of FLP modeling over LP modeling. Classifiers are fed by the three estimated coefficients in order to discriminate premature ventricular contraction (PVC) arrhythmia from normal beats. The study has successfully demonstrated that FLP modeling can be an alternative to the LP modeling in the field of QRS complex modeling.

The vastus lateralis neuromuscular activity during all-out cycling exercise.

OBJECTIVE The objective of this work was to study modifications in motor control through surface electromyographic (sEMG) activity during a very short all-out cycling exercise. METHODS Twelve male cyclists (age 23+/-4 years) participated in this study. After a warm-up period, each subject performed three all-out cycling exercises of 6s separated by 2 min of complete rest. This protocol was repeated three times with a minimum of 2 days between each session. The braking torque imposed on cycling motion was 19 Nm. The sEMG of the vastus lateralis was recorded during the first seven contractions of the sprint. Time-frequency analysis of sEMG was performed using continuous wavelet transform. The mean power frequency (MPF, qualitative modifications in the recruitment of motor units) and signal energy (a quantitative indicator of modifications in the motor units recruitment) were computed for the frequency range 10-500 Hz. RESULTS sEMG energy increased (P0.05) between contraction number 1 and 2, decreased (P < or =0.05) between contraction number 2 and 3 then stabilized between contraction number 3 and 7 during the all-out test. MPF increased (P < or =0.05) during the all-out test. This increase was more marked during the first two contractions. CONCLUSIONS The decrease in energy and the increase in the sEMG MPF suggest a large spatial recruitment of motor units (MUs) at the beginning of the sprint followed by a preferential recruitment of faster MUs at the end of the sprint, respectively.

Improving the Power Line Communication Signal-to-Noise Ratio During a Resistive Load Commutation

In any cable or power line communications, impulse noise is known to be the most difficult noise to filter. In particular, non periodic asynchronous impulse noise is impossible to predict. Under such noise conditions, the OFDM1 symbol generally used in PLC2 is corrupted. To overcome this problem, the signal-to-noise ratio is generally improved by detecting and/or filtering the noise. This leads however to heavy detection and computing time in comparison with the disturbance duration. In this paper, we study the parameters of the noise generated by a load commutation and propose a new approach that consists in controlling the commutation instant of the load. This approach reduces by up to 15 dB the asynchronous impulsive noise emitted by a resistive load in a 40.96-µs typical Homeplug AV3 OFDM symbol duration. Synchronisation is integrated in electronic commutation system. Results show that the load value does not influence the magnitude of the impulse noise.

A New Concept of Virtual Patient for Real-Time ECG Analyzers

Designing real-time systems such as electrocardiogram (ECG) analyzers and evaluating their performance precisely is not an easy task. One of the difficulties is due to the general loss of performance that is frequently observed when implementing data processing algorithms on such systems and running the devices in new environments. Performance values are traditionally estimated theoretically or numerically (personal computer simulations) or are evaluated on real data but in a simulated environment. While the best performance measurements are accredited due to experiments driven in a real environment, the data at stake in this case are not entirely controlled. We propose in this paper the new concept of a virtual patient for real-time ECG analyzers. This concept enables a real environment to be created virtually by generating signals from a database composed of real acquired data. The analog signals generated will be seen by the analyzer as if they were coming from instantaneous electrical heart activities. This procedure provides a more correct assessment of the ECG analyzer performance than was previously possible. The test bench is useful for Holter manufacturers since it can deliver genuine performance values of the device.

A new histogram-based estimation technique of entropy and mutual information using mean squared error minimization

Mutual Information (MI) has extensively been used as a measure of similarity or dependence between random variables (or parameters) in different signal and image processing applications. However, MI estimation techniques are known to exhibit a large bias, a high Mean Squared Error (MSE), and can computationally be very costly. In order to overcome these drawbacks, we propose here a novel fast and low MSE histogram-based estimation technique for the computation of entropy and the mutual information. By minimizing the MSE, the estimation avoids the error accumulation problem of traditional methods. We derive an expression for the optimal number of bins to estimate the MI for both continuous and discrete random variables. Experimental results from a speech recognition problem and a computer aided diagnosis problem show the power of the proposed approach in estimating the optimal number of selected features with enhanced classification results compared to existing approaches.