The RITS Conference: A Major Event of Biomedical Engineering in France

International audienceResearch in Imaging and HealTh TechnologieS (RITS) is a French scientific meeting dedicated to Biomedical Engineering. This biennial event has given rise to four special issues of IRBM since 2009. The present issue collects some research works presented by young researchers (first author being less than 32 years old). All of them submitted a full paper (instead of a long abstract) to the meeting in order to participate in the SFGBM competition. All the published papers followed the standard reviewing process

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

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Preterm labour detection by use of a biophysical marker: the uterine electrical activity

<p>Abstract</p> <p>Background</p> <p>The electrical activity of the uterine muscle is representative of uterine contractility. Its characterization may be used to detect a potential risk of preterm delivery in women, even at an early gestational stage.</p> <p>Methods</p> <p>We have investigated the effect of the recording electrode position on the spectral content of the signal by using a mathematical model of the women's abdomen. We have then compared the simulated results to actual recordings. On signals with noise reduced with a dedicated algorithm, we have characterized the main frequency components of the signal spectrum in order to compute parameters indicative of different situations: preterm contractions resulting nonetheless in term delivery (i.e. normal contractions) and preterm contractions leading to preterm delivery (i.e. high-risk contractions). A diagnosis system permitted us to discriminate between these different categories of contractions. As the position of the placenta seems to affect the frequency content of electrical activity, we have also investigated in monkeys, with internal electrodes attached on the uterus, the effect of the placenta on the spectral content of the electrical signals.</p> <p>Results</p> <p>In women, the best electrode position was the median vertical axis of the abdomen. The discrimination between high risk and normal contractions showed that it was possible to detect a risk of preterm labour as early as at the 27th week of pregnancy (Misclassification Rate range: 11–19.5%). Placental influence on electrical signals was evidenced in animal recordings, with higher energy content in high frequency bands, for signals recorded away from the placenta when compared to signals recorded above the placental insertion. However, we noticed, from pregnancy to labour, a similar evolution of the frequency content of the signal towards high frequencies, whatever the relative position of electrodes and placenta.</p> <p>Conclusion</p> <p>On human recordings, this study has proved that it is possible to detect, by non-invasive abdominal recordings, a risk of preterm birth as early as the 27th week of pregnancy. On animal signals, we have evidenced that the placenta exerts a local influence on the characteristics of the electrical activity of the uterus. However, these differences have a small influence on premature delivery risk diagnosis when using proper diagnosis tools.</p

Nonlinear estimation of coupling and directionality between signals: Application to uterine EMG propagation.

International audienceUnderstanding the direction and quantity of information flowing in a complex system is a fundamental task in signal processing. Several measures have been proposed to detect the quantity of synchronization and the directionality between time series and in physiological data. In this paper we use two methods that are widely used in synchronization and directionality analysis: Nonlinear correlation coefficient (h(2)) and the general synchronization (H). The performances of both methods were tested on four dimensional coupled synthetic nonlinear Rössler models. They were then applied to a single real labor contraction uterine EMG burst with the aim of using them to detect synchronization and to plot the map of direction of information flow between the whole signal channels. The results on synthetic signal show a slight superiority of H over h(2). The results obtained on a single contraction are encouraging for the future use of these tools for resolving the open question of the directionality of uterine contractions and may provide a way of finding their source loci

Optimizing Uterine Synchronization Analysis in Pregnancy and Labor through Window Selection and Node Optimization

Preterm labor (PL) has globally become the leading cause of death in children under the age of 5 years. To address this problem, this paper will provide a new approach by analyzing the EHG signals, which are recorded on the abdomen of the mother during labor and pregnancy. The EHG signal reflects the electrical activity that induces the mechanical contraction of the myometrium. Because EHGs are known to be non-stationary signals, and because we anticipate connectivity to alter during contraction, we applied the windowing approach on real signals to help us identify the best windows and the best nodes with the most significant data to be used for classification. The suggested pipeline includes i) divide the 16 EHG signals that are recorded from the abdomen of pregnant women in N windows; ii) apply the connectivity matrices on each window; iii) apply the Graph theory-based measures on the connectivity matrices on each window; iv) apply the consensus Matrix on each window in order to retrieve the best windows and the best nodes. Following that, several neural network and machine learning methods are applied to the best windows and best nodes to categorize pregnancy and labor contractions, based on the different input parameters (connectivity method alone, connectivity method plus graph parameters, best nodes, all nodes, best windows, all windows). Results showed that the best nodes are nodes 8, 9, 10, 11, and 12; while the best windows are 2, 4, and 5. The classification results obtained by using only these best nodes are better than when using the whole nodes. The results are always better when using the full burst, whatever the chosen nodes. Thus, the windowing approach proved to be an innovative technique that can improve the differentiation between labor and pregnancy EHG signals.Comment: 10 pages, 6 figure

Utilisation de la transformée en ondelettes non décimée pour le débruitage du signal électrohystérographique utérin

Le signal électrohystéographique (EHG) représente l'activité utérine de la femme pendant la grossesse. La théorie des ondelettes est bien adaptée au débruitage de ce signal non stationnaire, corrompu par des bruits électroniques, électromagnétiques et physiologiques (par exemple l'électrocardiogramme maternel). A cause de la décimation, la traditionnelle décomposition orthogonale ne conserve que les coefficients nécessaires et suffisants pour une reconstruction parfaite. Cependant, la redondance d'information issue d'une transformée non décimée apparaît aujourd'hui précieuse pour le débruitage. Nous proposons une technique de débruitage, appliquée à l'EHG, utilisant l'algorithme à trous, avec des filtres miroirs en quadrature. Dans cette méthode, nous prenons en compte la redondance d'information dans l'estimation du seuil de débruitage

Détection et classification d'événements en représentation multidimensionnelle. Application sur l'EMG utérin

L'EMG utérin peut être représenté par un processus AR où plusieurs événements sont superposés. Dans ce cas, la détection peut être faite en utilisant des algorithmes de détection classiques. Cependant, l'algorithme de classification doit prendre en compte les formes spécifiques de ces événements superposés. Dans ce travail, la classification est basée sur la décomposition multi-échelle et sur le test multihypothèse. Après détection utlisant le modèle AR adaptatif, le signal est décomposé en plusieurs échelles et classifié suivant une matrice de variance-covariance calculée à partir de la décomposition multiéchelle. Cet approche aboutit à deux retards différents pour la détection et la classification. Ils dépendent du seuil de détection et du temps d'estimation de la matrice de variance covariance, respectivement