Sources of Bias in Synchronization Measures and how to Minimize Their Effects on the Estimation of Synchronicity: Application to the Uterine Electromyogram

Non

Automatic detection of mode mixing in empirical modal decomposition by non-stationarity detection : application to IMF of interest selection or denoising

La décomposition modale empirique est une méthode itérative permettant de décomposer un signal en différents modes ou IMF (Intrinsic Mode Function). Un algorithme de sélection des composantes modales d'intérêts a été récemment proposé. Cette méthode se base sur une étude statistique du bruit contenu dans chacune des IMF et sur un modèle mathématique de la répartition du bruit dans chacune des IMF, propre au signal analysé, par estimation du bruit sur le premier mode (qui est censé contenir uniquement du bruit). Cependant un phénomène de mixage de modes peut apparaître et aboutir à une surestimation du niveau de bruit dans le signal original. Certaines IMF seront donc considérées à tort comme du bruit. Nous proposons une méthode générale de détection du mixage de modes basée sur la détection de non stationnarité sur la première IMF. Une fois le mixage de modes identifié, nous proposons ensuite de corriger l'estimation du niveau de bruit contenu sur la première IMF par une extraction sur cette IMF de la partie signal et de la partie correspondant uniquement au bruit. Les résultats obtenus sur des signaux purement synthétiques ou issus de la mécanique et du génie biomédical montrent l'intérêt de l'approche proposée

Analysis of the propagation of uterine electrical activity applied to predict preterm labor

COMPIEGNE-BU (601592101) / SudocSudocFranceF

Windowed multivariate autoregressive model improving classification of labor vs. pregnancy contractions.

International audienceAnalyzing the propagation of uterine electrical activity is poised to become a powerful tool in labor detection and for the prediction of preterm labor. Several methods have been proposed to investigate the relationship between signals recorded externally from several sites on the pregnant uterus. A promising recent method is the multivariate autoregressive (MVAR) model. In this paper we proposed a windowed (time varying) version of the multivariate autoregressive model, called W-MVAR, to investigate the connectivity between signals while still respecting their non-stationary characteristics. The proposed method was tested on synthetic signals as well as applied to real signals. The comparison between the two methods on synthetic signals showed the superiority of W-MVAR to detect connectivity even if it is non-stationary. The application of W-MVAR on multichannel real uterine signals show that the proposed method is a good tool to distinguish non-labor and labor signals. These results are very promising and can very possibly have important clinical applications in labor detection and preterm labor prediction

Bivariate piecewise stationary segmentation; improved pre-treatment for synchronization measures used on non-stationary biological signals

International audienc

Performance analysis of four nonlinearity analysis methods using a model with variable complexity and application to uterine EMG signals.

International audienceSeveral measures have been proposed to detect nonlinear characteristics in time series. Results on time series, multiple surrogates and their z-score are used to statistically test for the presence or absence of non-linearity. The z-score itself has sometimes been used as a measure of nonlinearity. The sensitivity of nonlinear methods to the nonlinearity level and their robustness to noise have rarely been evaluated in the past. While surrogates are important tools to rigorously detect nonlinearity, their usefulness for evaluating the level of nonlinearity is not clear. In this paper we investigate the performance of four methods arising from three families that are widely used in non-linearity detection: statistics (time reversibility), predictability (sample entropy, delay vector variance) and chaos theory (Lyapunov exponents). We used sensitivity to increasing complexity and the mean square error (MSE) of Monte Carlo instances for quantitative comparison of their performances. These methods were applied to a Henon nonlinear synthetic model in which we can vary the complexity degree (CD). This was done first by applying the methods directly to the signal and then using the z-score (surrogates) with and without added noise. The methods were then applied to real uterine EMG signals and used to distinguish between pregnancy and labor contraction bursts. The discrimination performances were compared to linear frequency based methods classically used for the same purpose such as mean power frequency (MPF), peak frequency (PF) and median frequency (MF). The results show noticeable difference between different methods, with a clear superiority of some of the nonlinear methods (time reversibility, Lyapunov exponents) over the linear methods. Applying the methods directly to the signals gave better results than using the z-score, except for sample entropy

Synchronization between EMG at different uterine locations investigated using time-frequency ridge reconstruction: comparison of pregnancy and labor contractions

To access publisher full text version of this article. Please click on the hyperlink in Additional Links fieldThe extraction of the frequency components of a signal can be useful for the characterization of the underlying system. One method for isolating a frequency component of a signal is by the extraction and reconstruction of the local maxima or ridge of its time-frequency representation. We compare here the performances of two well-known ridge reconstruction methods, namely the Carmona and Marseille methods, on synthetic signals as well as real electrohysterogram (EHG). We show that Carmona's method presents lower reconstruction errors. We then used the separately reconstructed frequency components of the EHG independently for labor prediction using a synchronization measure. We show that the proposed synchronization parameters present similar prediction rate to classical parameters obtained directly from the time-frequency representation but also seem to provide information complementary to the classical parameters and may thus improve the accuracy in labor prediction when they are used jointly

Interactions between Uterine EMG at Different Sites Investigated Using Wavelet Analysis: Comparison of Pregnancy and Labor Contractions

International audienc

Comparison between approximate entropy, correntropy and time reversibility: Application to uterine electromyogram signals

International audienc