On the detection of elderly equilibrium degradation using multivariate-EMD

International audienceThe aim of this paper is to provide a new methodology for the detection of an increased risk of falling in community-dwelling elderly. A new extended method of the empirical mode decomposition (EMD) called multivariate-EMD is employed in the proposed solution. This method will be mainly used to analyze the stabilogram center of pressure (COP) time series. In this paper, we describe also the remote non-invasive assessment method, which is suitable for static and dynamic balance. Balance was assessed using a miniature force plate, while gait was assessed using wireless sensors placed in a corridor of the home. The experimental results show the effectiveness of this indicator to identify the differences in standing posture between different groups of population

KLB , encoding β‐Klotho, is mutated in patients with congenital hypogonadotropic hypogonadism

Congenital hypogonadotropic hypogonadism (CHH) is a rare genetic form of isolated gonadotropin‐releasing hormone (GnRH) deficiency caused by mutations in > 30 genes. Fibroblast growth factor receptor 1 (FGFR1) is the most frequently mutated gene in CHH and is implicated in GnRH neuron development and maintenance. We note that a CHH FGFR1 mutation (p.L342S) decreases signaling of the metabolic regulator FGF21 by impairing the association of FGFR1 with β‐Klotho (KLB), the obligate co‐receptor for FGF21. We thus hypothesized that the metabolic FGF21/KLB/FGFR1 pathway is involved in CHH. Genetic screening of 334 CHH patients identified seven heterozygous loss‐of‐function KLB mutations in 13 patients (4%). Most patients with KLB mutations (9/13) exhibited metabolic defects. In mice, lack of Klb led to delayed puberty, altered estrous cyclicity, and subfertility due to a hypothalamic defect associated with inability of GnRH neurons to release GnRH in response to FGF21. Peripheral FGF21 administration could indeed reach GnRH neurons through circumventricular organs in the hypothalamus. We conclude that FGF21/KLB/FGFR1 signaling plays an essential role in GnRH biology, potentially linking metabolism with reproduction

Mutations in FGF17, IL17RD, DUSP6, SPRY4, and FLRT3 Are Identified in Individuals with Congenital Hypogonadotropic Hypogonadism

Congenital hypogonadotropic hypogonadism (CHH) and its anosmia-associated form (Kallmann syndrome [KS]) are genetically heterogeneous. Among the >15 genes implicated in these conditions, mutations in FGF8 and FGFR1 account for ∼12% of cases; notably, KAL1 and HS6ST1 are also involved in FGFR1 signaling and can be mutated in CHH. We therefore hypothesized that mutations in genes encoding a broader range of modulators of the FGFR1 pathway might contribute to the genetics of CHH as causal or modifier mutations. Thus, we aimed to (1) investigate whether CHH individuals harbor mutations in members of the so-called "FGF8 synexpression" group and (2) validate the ability of a bioinformatics algorithm on the basis of protein-protein interactome data (interactome-based affiliation scoring [IBAS]) to identify high-quality candidate genes. On the basis of sequence homology, expression, and structural and functional data, seven genes were selected and sequenced in 386 unrelated CHH individuals and 155 controls. Except for FGF18 and SPRY2, all other genes were found to be mutated in CHH individuals: FGF17 (n = 3 individuals), IL17RD (n = 8), DUSP6 (n = 5), SPRY4 (n = 14), and FLRT3 (n = 3). Independently, IBAS predicted FGF17 and IL17RD as the two top candidates in the entire proteome on the basis of a statistical test of their protein-protein interaction patterns to proteins known to be altered in CHH. Most of the FGF17 and IL17RD mutations altered protein function in vitro. IL17RD mutations were found only in KS individuals and were strongly linked to hearing loss (6/8 individuals). Mutations in genes encoding components of the FGF pathway are associated with complex modes of CHH inheritance and act primarily as contributors to an oligogenic genetic architecture underlying CHH

Traitement non linéaire des données pour l'analyse de l'équilibre chez les personnes âgées

Dans les pays développés, les dernières statistiques indiquent clairement l augmentation de la proportion des personnes âgées. Donc, le maintien de l'autonomie à domicile constitue aujourd'hui un enjeu primordial pour cette catégorie. En particulier, la majorité des personnes âgées, vivant seul à domicile, fait face à des situations à risques telles que des chutes. Dans cette thèse, nous proposons des méthodes non linéaires dans le but d'extraire des paramètres pertinents pour la prévention et la détection précoce du risque de chute. L originalité de ces méthodes réside dans l analyse non linéaire des composantes du signal obtenues à partir d une décomposition modale empirique. La décomposition modale empirique a permis de mettre en évidence l existence d une composante chaotique dans le signal à partir de laquelle on peut extraire un indicateur performant de la qualité de la posture humaine. Nous nous sommes particulièrement intéressés à la reformulation mathématique de l EMD dans le but est de la généraliser au cas multidimensionnel. Cette nouvelle extension a été appliquée par la suite sur les signaux stabilométriques dans le but d'extraire des paramètres pertinents de prévention et de détection de chute. Les perspectives de ce travail sont nombreuses, parmi lesquelles la fusion des données de la posture humaine avec d autres données issues des capteurs intégrés dans un habitat intelligentIn developed countries, the latest statistics clearly indicate that elderly population is growing, and people are living longer. Thus, maintaining a functional independence of the elderly becomes a priority for our society. The majority of elderly people living alone are more and more exposed to risky situations; one of them is the risk of falling. Falls are the leading cause of serious and health threats in the elder population. In this thesis, we started by reviewing the problem of fall detection in the elderly. Next, we proposed non-linear methods in order to extract relevant parameters for the prevention and early detection of the risk of falling. The originality of these methods is the nonlinear analysis of the components obtained from an empirical modal decomposition. The EMD method has highlighted the existence of a chaotic component in the signal from which we can extract an effective indicator of the quality of human posture. We are particularly interested in the mathematical reformulation of the EMD method in order to generalize it to the multidimensional case. This new extension was applied later on stabilogram signals in the aim of extracting relevant parameters for the prevention and detection of falling. One of the perspectives of this work is to consider data fusion of human posture with other data from sensors embedded in a smart homeTROYES-SCD-UTT (103872102) / SudocSudocFranceF

Nouvelles données sur la signalisation du FGFR2 (implications thérapeutiques dans l'ostéogenèse)

PARIS7-Bibliothèque centrale (751132105) / SudocSudocFranceF

On the surface EMG signal reconstruction using blind source separation

International audienceIn biomedical signal processing, many sources are often mixed as a form of measured signal. The goal is usually to extract and analyze one or several of them separately. In the multichannel measurements, several Blind Source Separation (BSS) techniques are available for decomposing the signal into its components. In this paper, a novel method is presented for the reconstruction of individual muscle source signals from simulated surface Elec-tromyography (s-EMG) array recordings. This method is based on BSS in a Bayesian model selection framework. Specifically, it is relies on an efficient wavelet spectral matching separating algorithm. Our concept is evaluated on theoretical decomposition and is confirmed by simulated signals

Empirical Mode Decomposition for vectorial bi-dimensional signals

International audienceIn this work, an Empirical Mode Decomposition (EMD) of bivariate signals is proposed. It permits to decompose signals into elementary rotating functions, oscillating functions and tendencies. The technique is based on recursive extraction of non planar and rapidly rotating functions, and then a recursive extraction of rapidly oscillating functions as in case of univariate EMD. Our concept is based on a theoretical decomposition and is confirmed on simulated signals

Blind separation of noisy convolutive sources

International audienceIn this paper, we present a new blind source separation method for noisy linear convolutive signal mixtures of independent components. The effectiveness of the proposed method is tested on synthetic data. Our technique has various advantages. First, it is based on the expectation-maximization (EM) algorithm for the separation of the components. Second, the proposed technique works in the spectral domain where, thanks to two simple approximations, the likelihood assumes a simple form which is easy to handle (low dimensional sufficient statistics) and to maximize (via the EM algorithm). Using this technique, we have obtained a good preliminary results being able to blindly separate noisy mixtures with two components and four different versions of mixing matrix