Sélection de variables stabilométriques pour l'analyse et la détection de la dégradation de l'équilibre postural

L'étude de la posture statique présente un grand intérêt pour l'analyse du déficit du contrôle de l'équilibre. Une méthode d'analyse de l'équilibre consiste à utiliser une plate-forme de forces qui permet d'extraire le déplacement du centre de pression (CdP). Les paramètres extraits du CdP s'avèrent comme des variables clés pour surveiller la dégradation de l'équilibre. Cependant, la non pertinence et\ou la redondance de certains d'entre eux rendent difficile une détection efficace d'une dégradation. L'objectif de cet article est l'implémentation d'une méthode de détection à noyau (SVDD) et d'une procédure de sélection des paramètres pertinents capables de détecter une dégradation de l'équilibre. Le critère de sélection choisi est la maximisation de la surface sous la courbe COR

Shannon and Renyi Entropies to Classify Effects of Mild Traumatic Brain Injury on Postural Sway

Background: Mild Traumatic Brain Injury (mTBI) has been identified as a major public and military health concern both in the United States and worldwide. Characterizing the effects of mTBI on postural sway could be an important tool for assessing recovery from the injury. Methodology/Principal Findings: We assess postural sway by motion of the center of pressure (COP). Methods for data reduction include calculation of area of COP and fractal analysis of COP motion time courses. We found that fractal scaling appears applicable to sway power above about 0.5 Hz, thus fractal characterization is only quantifying the secondary effects (a small fraction of total power) in the sway time series, and is not effective in quantifying long-term effects of mTBI on postural sway. We also found that the area of COP sensitively depends on the length of data series over which the COP is obtained. These weaknesses motivated us to use instead Shannon and Renyi entropies to assess postural instability following mTBI. These entropy measures have a number of appealing properties, including capacity for determination of the optimal length of the time series for analysis and a new interpretation of the area of COP. Conclusions: Entropy analysis can readily detect postural instability in athletes at least 10 days post-concussion so that it appears promising as a sensitive measure of effects of mTBI on postural sway

The PARAChute project: remote monitoring of posture and gait for fall prevention

Falls in the elderly are a major public health problem due to both their frequency and their medical and social consequences. In France alone, more than two million people aged over 65 years old fall each year, leading to more than 9 000 deaths, in particular in those over 75 years old (more than 8 000 deaths). This paper describes the PARAChute project, which aims to develop a methodology that will enable the detection of an increased risk of falling in community-dwelling elderly. The methods used for a remote noninvasive assessment for static and dynamic balance assessments and gait analysis are described. The final result of the project has been the development of an algorithm for movement detection during gait and a balance signature extracted from a force plate. A multicentre longitudinal evaluation of balance has commenced in order to validate the methodologies and technologies developed in the project

Effect of connectivity measures on the identification of brain functional core network at rest

International audienceMagneto/Electro-encephalography (M/EEG) source connectivity is an emergent tool to identify brain networks with high time/space resolution. Here, we aim to identify the brain core network (s-core decomposition) using dense-EEG. We also evaluate the effect of the functional connectivity methods used and more precisely the effect of the correction for the so-called source leakage problem. Two connectivity measures were evaluated the phase locking value (PLV) and phase lag index (PLI) that supposed to deal with the leakage problem by removing the zero-lag connections. Both methods were evaluated on resting state dense-EEG signals recorded from 19 healthy participants. Core networks obtained by each method was compared to those computed using fMRI from 487 healthy participants at rest (from the Human Connectome Project - HCP). The correlation between networks obtained by EEG and fMRI was used as performance criterion. Results show that PLV networks are closer to fMRI networks with significantly higher correlation values with fMRI networks, than PLI networks. Results suggest caution when selecting the functional connectivity methods and mainly methods that remove the zero-lag connections as it can severely affect the network characteristics. The choice of functional connectivity measure is indeed crucial not only in cognitive neuroscience but also in clinical neuroscience. © 2019 IEEE

Exploring the Correlation Between M/EEG Source–Space and fMRI Networks at Rest

Magneto/electro-encephalography (M/EEG) source connectivity is an emerging approach to estimate brain networks with high temporal and spatial resolutions. Here, we aim to evaluate the effect of functional connectivity (FC) methods on the correlation between M/EEG source–space and fMRI networks at rest. Two main FC families are tested: (i) FC methods that do not remove zero-lag connectivity including Phase Locking Value (PLV) and Amplitude Envelope Correlation (AEC) and (ii) FC methods that remove zero-lag connections such as Phase Lag Index (PLI) and two orthogonalisation approaches combined with PLV (PLVCol, PLVPas) and AEC (AECCol, AECPas). Methods are evaluated on resting state M/EEG signals recorded from healthy participants at rest (N = 74). Networks obtained by each FC method are compared with fMRI networks (obtained from the Human Connectome Project). Results show low correlations for all FC methods, however PLV and AEC networks are significantly correlated with fMRI networks (ρ = 0.12, p = 1.93 × 10–8 and ρ = 0.06, p = 0.007, respectively), while other methods are not. These observations are consistent for all M/EEG frequency bands and for different FC matrices threshold. Our main message is to be careful in selecting FC methods when comparing or combining M/EEG with fMRI. We consider that more comparative studies based on simulation and real data and at different levels (node, module or sub networks) are still needed in order to improve our understanding on the relationships between M/EEG source–space networks and fMRI networks at rest

Determination of the bioavailability of gentamicin to the lungs following inhalation from two jet nebulizers

NoAims To determine the bioavailability of gentamicin to the lung following inhalation from two jet nebulizers. Methods Serial urine samples were obtained from 10 volunteers after a 80 mg dose given orally, nebulized from a Pari LC + (PARI) and MicroNeb III (MN) devices, or after a 40 mg intravenous dose. In vitro aerodynamic characteristics of the nebulized doses were also determined. Results The mean (SD) absolute gentamicin lung bioavailalibility following delivery by PARI and MN devices was 1.4 (0.4) and 1.7 (0.5) %. The mass median aerodynamic diameter (MMAD) of the drug particles from the PARI and MN systems was 8.6 (0.6) and 6.7 (0.5) µm and the corresponding fine particle doses (FPD) were 10.2 (2.8) and 11.7 (1.5) mg. Conclusions The MMAD and FPD data reflect the poor lung deposition of gentamicin identified by urinary excretion

Information-Importance Based Communication for Large-Scale WSN Data Processing

International audienceGathering information in an energy-efficient and scalable manner from a wireless sensor network is always a basic need. In this work, we use the multi-agent approach in order to build an Information-Importance Based Communication for large scale wireless sensor network data processing. The principal goal of our proposition is to tackle the problem of network density and scalability in an energy efficient manner. Simulation results are provided to illustrate the efficiency of our proposition