Estimation du risque de mort subite par arrêt cardiaque a l'aide de méthodes d'apprentissage artificiel

Depuis le début des années 2000, le défibrillateur automatique implantable (DAI) est prescrit de manière prophylactique aux populations à risque de mort subite. Nombre de ces implantations semblent prématurées, ce qui pose problème en raison des complications post-opératoires encourues. Il apparaît donc important de mieux définir la population à risque de mort subite, afin d'optimiser la sélection des patients.Le pouvoir prédictif de mort subite des différents descripteurs du Holter a fait l'objet de nombreuses études univariées, sans permettre d'amélioration des critères de sélection. Dans ce mémoire, nous présentons l'analyse multivariée des descripteurs du Holter que nous avons menée. Nous avons extrait l'ensemble des descripteurs calculables sur la base étiquetée d'enregistrements de patients, victimes ou non d'arythmies traitées par le DAI, dont nous disposons. À l'aide de connaissances physiologiques sur l'arythmogenèse, nous avons réalisé une sélection des descripteurs les plus pertinents. Puis, par une méthode originale de conception et d'évaluation de classifieur, nous avons construit un classifieur ad hoc, basé, sur les connaissances physiologiques de l'arythmogenèse ; ce classifieur discrimine les patients à risque, des patients pour lesquels l'implantation ne paraît pas opportune.Au vu des performances atteintes, il semble possible d'améliorer la fiabilité des indications d'implantation prophylactique, à l'aide de méthodes d'apprentissage statistique. Pour valider cette conclusion, il paraît néanmoins nécessaire d'appliquer la méthode exposée dans la présente étude à une base de données de plus grande dimension, et de contenu mieux adapté à nos objectifs.Implantable cardioverter defibrillators (ICD) have been prescribed for prophylaxis since the early 2000?s, for patients at high risk of SCD. Unfortunately, most implantations to date appear unnecessary. This result raises an important issue because of the perioperative and postoperative risks. Thus, it is important to improve the selection of the candidates to ICD implantation in primary prevention. Risk stratification for SCD based on Holter recordings has been extensively performed in the past, without resulting in a significant improvement of the selection of candidates to ICD implantation. The present report describes a nonlinear multivariate analysis of Holter recording indices. We computed all the descriptors available in the Holter recordings present in our database. The latter consisted of labelled Holter recordings of patients equipped with an ICD in primary prevention, a fraction of these patients received at least one appropriate therapy from their ICD during a 6-month follow-up. Based on physiological knowledge on arrhythmogenesis, feature selection was performed, and an innovative procedure of classifier design and evaluation was proposed. The classifier is intended to discriminate patients who are really at risk of sudden death from patients for whom ICD implantation does not seem necessary. In addition, we designed an ad hoc classifier that capitalizes on prior knowledge on arrhythmogenesis. We conclude that improving prophylactic ICD-implantation candidate selection by automatic classification from Holter recording features may be possible. Nevertheless, that statement should be supported by the study of a more extensive and appropriate database.PARIS-JUSSIEU-Bib.électronique (751059901) / SudocSudocFranceF

Towards a Practical Silent Speech Interface Based on Vocal Tract Imaging

Intégralité des actes de cette conférence disponible au lien suivant: http://www.issp2011.uqam.ca/upload/files/proceedings.pdfInternational audienceThe paper describes advances in the development of an ultrasound silent speech interface for use in silent communications applications or as a speaking aid for persons who have undergone a laryngectomy. It reports some first steps towards making such a device lightweight, portable, interactive, and practical to use. Simple experimental tests of an interactive silent speech interface for everyday applications are described. Possible future improvements including extension to continuous speech and real time operation are discussed.Cet article décrit les avancements dans le développement d'une interface ultrasonore de parole silencieuse, pour des applications en communication silencieuse ou comme une aide pour les personnes laryngectomisées. Nous rapportons les premiers pas pour réaliser une telle interface portable, interactive, et pratique à utiliser. De simples tests expérimentaux de cette interface pour des applications quotidiennes sont décrits. Des améliorations futures possibles incluant l'extension à la parole continue et aux traitements en temps réels sont discutées

Predicting the Surface Tension of Liquids: Comparison of Four Modeling Approaches and Application to Cosmetic Oils

The efficiency of four modeling approaches, namely group contributions, corresponding-states principle, σ-moment-based neural networks, and graph machines, are compared for the estimation of the surface tension (ST) of 269 pure liquid compounds at 25 °C from their molecular structure. This study focuses on liquids containing only carbon, oxygen, hydrogen or silicon atoms since our purpose is to predict the surface tension of cosmetic oils. Neural network estimations are performed from σ-moment descriptors as defined in the COSMO-RS model, while methods based on group contributions, corresponding-states principle and graph machines use 2D molecular information (SMILES codes). The graph machine approach provides the best results, estimating the surface tensions of 23 cosmetic oils, such as hemisqualane, isopropyl myristate or decamethylcyclopentasiloxane (D5), with accuracy better than 1 mN.m–1. A demonstration of the graph machine model using the recent Docker technology is available for download in the Supplementary Information

A New Yeast Poly(A) Polymerase Complex Involved in RNA Quality Control

Eukaryotic cells contain several unconventional poly(A) polymerases in addition to the canonical enzymes responsible for the synthesis of poly(A) tails of nuclear messenger RNA precursors. The yeast protein Trf4p has been implicated in a quality control pathway that leads to the polyadenylation and subsequent exosome-mediated degradation of hypomethylated initiator tRNA(Met) (tRNA(i) (Met)). Here we show that Trf4p is the catalytic subunit of a new poly(A) polymerase complex that contains Air1p or Air2p as potential RNA-binding subunits, as well as the putative RNA helicase Mtr4p. Comparison of native tRNA(i) (Met) with its in vitro transcribed unmodified counterpart revealed that the unmodified RNA was preferentially polyadenylated by affinity-purified Trf4 complex from yeast, as well as by complexes reconstituted from recombinant components. These results and additional experiments with other tRNA substrates suggested that the Trf4 complex can discriminate between native tRNAs and molecules that are incorrectly folded. Moreover, the polyadenylation activity of the Trf4 complex stimulated the degradation of unmodified tRNA(i) (Met) by nuclear exosome fractions in vitro. Degradation was most efficient when coupled to the polyadenylation activity of the Trf4 complex, indicating that the poly(A) tails serve as signals for the recruitment of the exosome. This polyadenylation-mediated RNA surveillance resembles the role of polyadenylation in bacterial RNA turnover

Neural Networks: Methodology and Applications

Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts ands seemlessly edited to present a coherent and comprehensive, yet not redundant, practically-oriented introduction