Joint unmixing-deconvolution algorithms for hyperspectral images

International audienceThis paper combines supervised linear unmixing and deconvolution problems to increase the resolution of the abundance maps for industrial imaging systems. The joint unmixing-deconvolution (JUD) algorithm is introduced based on the Tikhonov regularization criterion for offline processing. In order to meet the needs of industrial applications, the proposed JUD algorithm is then extended for online processing by using a block Tikhonov criterion. The performance of JUD is increased by adding a non-negativity constraint which is implemented in a fast way using the quadratic penalty method and fast Fourier transform. The proposed algorithm is then assessed using both simulated and real hyperspectral images

A simultaneous sparse approximation method for multidimensional harmonic retrieval

International audienceIn this paper, a new method for the estimation of the parameters of multidimensional (R-D) harmonic and damped complex signals in noise is presented. The problem is formulated as R simultaneous sparse approximations of multiple 1-D signals. To get a method able to handle large size signals while maintaining a sufficient resolution, a multigrid dictionary refinement technique is associated to the simultaneous sparse approximation. The refinement procedure is proved to converge in the single R-D mode case. Then, for the general multiple modes case, the signal tensor model is decomposed in order to handle each mode separately in an iterative scheme. The proposed method does not require an association step since the estimated modes are automatically "paired". We also derive the Cramér-Rao lower bounds of the parameters of modal R-D signals. The expressions are given in compact form in the single tone case. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed method

Assessment of non-negative matrix factorization for the preprocessing of long-term ECG

Présentation PosterInternational audienceBackground. With the advent of health information technology and wearable acquisition systems, long-term ECG are more and more used in long-term cardiac tolerability studies of new compounds. Nevertheless, the accurate analysis of such long signals requires reliable and fast signal processing algorithms. Objectives. The objective of this study is to assess the practical relevance of innovative matrix factorization methods for the preprocessing of long-term ECG. Those signals are generally noisy with complex baseline wander and require preprocessing, such as filtering, to perform a correct analysis. Our goal is to present two innovative algorithms of matrix factorization to detect R-peaks in long-term ECG.Methods. The two tested methods are called Independent Component Analysis (ICA) and Non-Negative Matrix Factorization (NMF). They are both source separation methods whose goal is to isolate each component of an ECG: R-peak, P and T waves, noise, baseline wander etc. On the one hand, ICA assumes that all subcomponents of the signal are statistically independent from each other and on the other hand, Non-Negative Matrix Factorization is a method that uses the non-negativity of the spectrogram of the ECG to separate the different time-frequency patterns. The two signal processing methods were implemented in the Matlab computing environment.Results. The proposed approaches are tested on the MIT-BIH Arrythmia and Noise Stress Test databases: ICA shows a strong drawback by returning the different sources in a random order making compulsory a reconnaissance step or the action of a specialist. Whereas NMF achieves high results in terms of sensitivity and specificity in general even in case of complex baseline wander and highly noisy signals. Conclusion. This study emphasizes promising results of a new long-term ECG preprocessing technique based on a matrix factorization method. This approach simultaneously undertakes three tasks: denoising, baseline wander removal and peak R detection

Early detection of Cheyne-Stokes breathing via ECG-derived respiration in patients with severe heart failure: a pilot study

International audienceWe present in this paper a preliminary study for detecting early pattern of Cheyne-Stokes Breathing using a single electrocardiogram signal in patients with severe heart failure. Two ECG-derived respiration signals, namely Heart-Rate and R-Wave Amplitude, are computed and jointly used to estimate different respiratory events, respiratory rate and amplitude modulation. Three patients whose respiration goes from normal to severe CSB are used to test our method. Results show good performance for the detection of breathing cycles compared with the ventilation signal and the final classification based on respiratory events, AHI, amplitude modulation reveals exact correlation with the expert

R-peak detection in holter ECG signals using non-negative matrix factorization

International audienceHolter monitoring is mainly used for medical follow- up and diagnosis of patients with suspected cardiac ar- rhythmia such as heart rhythm irregularities that can be missed during classical electrocardiogram recording (ECG). However, these long-term continuous recordings represent a large amount of data that cannot be processed by hand. In this article, we present a new method based on Non-negative Matrix Factorization (NMF) to detect R- peaks in Holter signals. The approach consists in two stages: source separation based on the different time- frequency patterns of the QRS complexes and the other waves of the signal (P and T waves) and R-peak detection using Automatic Objective Thresholding (AOT). The pro- posed approach is validated on the MIT-BIH Arrhythmia database and achieves an average sensitivity of 99.59% and a precision of 99.69%. Using the MIT-BIH Noise Stress Test database, we also show the ability of our ap- proach to discriminate R-peaks in signals contaminated with different noises

Transgenerational effects of ERalpha36 over-expression on mammary gland development and molecular phenotype: clinical perspective for breast cancer risk and therapy.

International audienceGrowing source of evidence suggests that exposure to estrogen mimicking agents is a risk factor for breast cancer onset and progression. Long chain alkylphenols are man made compounds still present in household products, industrial and agricultural processes, leading to a global environmental and human contamination. These molecules are known to exert estrogen -like activities through binding to classical estrogen receptors. Recently, we have demonstrated that a realistic mixture of 4 tert - octylphenol and 4 - nonylphenol can stimulate proliferation and modulate epigenetic status of testicular cancer germ cells through a rapid, Estrogen Receptor alpha 36 (ERα36) -dependent non genomic pathway (Ajj et al, 2013; doi: 10.1371/journal.pone.0061758). In a retrospective study of breast tumor samples, we also validated ERα36 expression as a reliable prognostic factor for cancer progression from an estrogen dependent prolifera tive tumor toward an estrogen dispensable metastatic disease (Chamard - Jovenin et al, 2015; doi: 10.1186/s12918 - 015 - 0178 - 7). Since high ERα36 expression enhances expression of migration/invasion markers in breast tumors, we addressed the question of its involvement in response to alkylphenol exposure in vitro (MCF -10A mammary epithelial cell line and MCF -7 estrogen -sensitive cancer cells) and in vivo ( C57BL mice). A male inherited transgenerational model of exposure to environmentally relevant doses of an alkylphenol mix was set up in C57BL/6J mice to determine whether and how it impacts on mammary gland morphogenesis. Human mammary epithelial MCF -10A cells were exposed to similar doses to decipher the molecular mechanisms involved by a combination of transcriptomic study, cell phenotype analyses, functional and signaling network modeling. The relevance of mouse phenotype extrapolation to human risk is discussed. Mouse mammary gland exposed transgenerationally to the alkylphenol mix displayed a neoplastic -like histology. This phenotype was correlated with the enhanced proliferation, migration ability and apoptosis resistance observed in vitro on human mammary epithelial cells and mediated by the estrogen receptor variant ERα36. Since cellular phenotypes are similar in vivo and in vitro and involve the unique ERα36 human variant , such consequences of alkylphenol exposure could be extrapolated from mouse model to human. Low dose alkylphenol transgenerational exposure could promote abnormal mammary gland development and subsequently increase the risk of breast cancer at ageing

Estimation des paramètres de sinusoïdes amorties par décomposition en sous-bandes adaptative : application à la spectroscopie RMN

This work is devoted to the study of subband decomposition in the framework of frequency estimation of noisy damped sinusoidal signals. Such an approach is preferable when dealing with signals of great complexity, i.e. containing many components, possibly close to each other, and with much different amplitudes. This is often the case in the application considered, NMR spectroscopy.The first contribution is the development of an optimal High-Order Yule-Walker approach for damped exponentials, which may improve the signal-to-noise ratio. The second important contribution is the methodological study of the subband approach which leads us to consider adaptive forms. The third contribution is a proposal of a new adaptive scheme using a residuals whiteness test as a stop-criterion. Application to real-world NMR signals shows the superiority of such an approach, in terms of numerical complexity, detection rates, and sensitivity to noise, as compared to usual methods used in this framework.Ce travail porte sur l'estimation fréquentielle de sinusoïdes amorties bruitées par décomposition en sous-bandes. Cette approche est préférable pour des signaux de forte complexité (nombreuses composantes, problèmes de résolution dynamique et fréquentielle), ce qui est souvent le cas dans l'application traitée, la spectroscopie RMN.La première contribution est le développement d'une version optimale de l'approche Yule-Walker d'ordre élevé qui peut, dans le cas amorti, améliorer le rapport signal-sur-bruit. La deuxième contribution est une étude méthodologique de l'approche en sous-bandes qui nous conduit à considérer des formes adaptatives. La troisième contribution est la proposition d'une nouvelle structure adaptative utilisant un test de blancheur comme critère d'arrêt. Une application à des signaux RMN montre l'intérêt de cette approche en termes de complexité numérique, de taux de détection et de sensibilité au bruit, par rapport à celles habituellement utilisées dans ce contexte

Contributions à l'estimation fréquentielle multidimensionnelle et à la sélection de variables en spectroscopie

This manuscript is a synthesis of my research activity at CRAN lab between 2004 and 2017 where my projects dealt with inverse problems in signal and image processing, sparse approximation for harmonic retrieval and simultaneous variable selection, and biological image analysis.The first part of the manuscript is a review of my teaching and research activities conducted respectively at Faculté des Sciences et Technologies (Lorraine University), and CRAN. In the second part, I give details of some theoretical, algorithmic and applied contributions on three subject. The first chapter is a theoretical first-order perturbation analysis of three high-resolution algorithms usually used for 1-D harmonic and modal retrieval. The objective was to derive compact expressions of the parameter variances allowing one to select the optimal tuning parameters in a simple way. The second chapter is devoted to the multidimensional modal retrieval problem where the main objective is to propose a computationally efficient method to estimate the model parameters. First, a simultaneous sparse algorithm with multigrid dictionary refinement is presented. Then, the Cramér-Rao bounds of the multidimensional model parameters are derived. The third chapter is an applicative part. A method for simultaneous variable selection for classification of infrared spectrometry measurements is described. The third part sketches some perspectives in the fields of sparse approximation, on-line hyperspectral image processing, and cancer growth modelling.Ce mémoire d'habilitation à diriger des recherches synthétise mon activité de recherche au Centre de Recherche en Automatique de Nancy (CRAN) entre 2004 et 2017. Les travaux que j'y ai développés s'articulent autour de la résolution de problèmes inverses en traitement du signal et des images, de l'approximation parcimonieuse en analyse spectrale multidimensionnelle et en sélection de variables, et l'analyse d'images biologiques.La première partie du manuscrit présente un bilan synthétique de mes activités d'enseignement et de recherche menées respectivement à Faculté des Sciences et Technologies de l'Université de Lorraine, et au CRAN. Dans la seconde partie, je présente mes contributions méthodologiques, algorithmiques et appliquées sur trois sujets. Dans le premier chapitre, je présente une partie théorique portant sur l'analyse de perturbation de trois algorithmes haute-résolution d'estimation modale 1-D de signaux exponentiellement amortis. L'objectif est d'obtenir des expressions compactes sur la variance des paramètres du modèle qui soient facilement exploitables pour, par exemple, régler les hyperparamètres des algorithmes. Le deuxième chapitre est consacré à l'estimation modale multidimensionnelle avec comme objectif principal la réduction du coût de calcul. Dans un premier temps, un algorithme d'estimation utilisant une approximation parcimonieuse avec une mise à jour adaptative du dictionnaire est présenté. Par la suite, je décris une autre contribution concernant le calcul des bornes de Cramér-Rao des paramètres du modèle multidimensionnel. Le troisième chapitre est une partie applicative en spectroscopie infrarouge où le problème consiste à sélectionner des bandes spectrales dans un but de classification. Dans la troisième partie, je dresse quelques perspectives dans les domaines de l'approximation parcimonieuse, du traitement en ligne d'images hyperspectrales, et la modélisation de la croissance tumorale

Estimation des paramètres de sinusoïdes amorties par décomposition en sous-bandes adaptative (application à la spectroscopie RMN)

Ce travail porte sur l'estimation fréquentielle de sinusoïdes amorties bruitées par décomposition en sous-bandes. Cette approche est préférable pour des signaux de forte complexité (nombreuses composantes, problèmes de résolution dynamique et fréquentielle), ce qui est souvent le cas dans l'application traitée, la spectroscopie RMN.La première contribution est le développement d'une version optimale de l'approche Yule-Walker d'ordre élevé qui peut, dans le cas amorti, améliorer le rapport signal-sur-bruit. La deuxième contribution est une étude méthodologique de l'approche en sous-bandes qui nous conduit à considérer des formes adaptatives. La troisième contribution est la proposition d'une nouvelle structure adaptative utilisant un test de blancheur comme critère d'arrêt. Une application à des signaux RMN montre l'intérêt de cette approche en termes de complexité numérique, de taux de détection et de sensibilité au bruit, par rapport à celles habituellement utilisées dans ce contexte.This work is devoted to the study of subband decomposition in the framework of frequency estimation of noisy damped sinusoidal signals. Such an approach is preferable when dealing with signals of great complexity, i.e. containing many components, possibly close to each other, and with much different amplitudes. This is often the case in the application considered, NMR spectroscopy.The first contribution is the development of an optimal High-Order Yule-Walker approach for damped exponentials, which may improve the signal-to-noise ratio. The second important contribution is the methodological study of the subband approach which leads us to consider adaptive forms. The third contribution is a proposal of a new adaptive scheme using a residuals whiteness test as a stop-criterion. Application to real-world NMR signals shows the superiority of such an approach, in terms of numerical complexity, detection rates, and sensitivity to noise, as compared to usual methods used in this framework.NANCY1-SCD Sciences & Techniques (545782101) / SudocSudocFranceF