Fractal time series analysis of postural stability in elderly and control subjects

<p>Abstract</p> <p>Background</p> <p>The study of balance using stabilogram analysis is of particular interest in the study of falls. Although simple statistical parameters derived from the stabilogram have been shown to predict risk of falls, such measures offer little insight into the underlying control mechanisms responsible for degradation in balance. In contrast, fractal and non-linear time-series analysis of stabilograms, such as estimations of the Hurst exponent (H), may provide information related to the underlying motor control strategies governing postural stability. In order to be adapted for a home-based follow-up of balance, such methods need to be robust, regardless of the experimental protocol, while producing time-series that are as short as possible. The present study compares two methods of calculating H: Detrended Fluctuation Analysis (DFA) and Stabilogram Diffusion Analysis (SDA) for elderly and control subjects, as well as evaluating the effect of recording duration.</p> <p>Methods</p> <p>Centre of pressure signals were obtained from 90 young adult subjects and 10 elderly subjects. Data were sampled at 100 Hz for 30 s, including stepping onto and off the force plate. Estimations of H were made using sliding windows of 10, 5, and 2.5 s durations, with windows slid forward in 1-s increments. Multivariate analysis of variance was used to test for the effect of time, age and estimation method on the Hurst exponent, while the intra-class correlation coefficient (ICC) was used as a measure of reliability.</p> <p>Results</p> <p>Both SDA and DFA methods were able to identify differences in postural stability between control and elderly subjects for time series as short as 5 s, with ICC values as high as 0.75 for DFA.</p> <p>Conclusion</p> <p>Both methods would be well-suited to non-invasive longitudinal assessment of balance. In addition, reliable estimations of H were obtained from time series as short as 5 s.</p

Univariate and bivariate empirical mode decomposition for postural stability analysis

The aim of this paper was to compare empirical mode decomposition (EMD) and two new extended methods of Open image in new windowEMD named complex empirical mode decomposition (complex-EMD) and bivariate empirical mode decomposition (bivariate-EMD). All methods were used to analyze stabilogram center of pressure (COP) time series. The two new methods are suitable to be applied to complex time series to extract complex intrinsic mode functions (IMFs) before the Hilbert transform is subsequently applied on the IMFs. The trace of the analytic IMF in the complex plane has a circular form, with each IMF having its own rotation frequency. The area of the circle and the average rotation frequency of IMFs represent efficient indicators of the postural stability status of subjects. Experimental results show the effectiveness of these indicators to identify differences in standing posture between groups

Sur la caractérisation de non-stationnarités par la méthode des substituts

International audienceThe surrogate data technique generates a family of stationarized surrogate signals from the signal under investigation, enabling to derive a statistical test to reject or accept the null hypothesis of stationarity. In this paper, we examine how and to what extent this approach also allows us to characterize the type of non-stationarity of the signal under investigation. Beyond this general approach, we are interested in a class of signals modulated jointly in amplitude and frequency, all leading to the same surrogates. This approach provides the necessary framework for characterizing different forms of non-stationarity. Experimental results demonstrate the potential of the surrogate data technique to characterize different forms of non-stationarity, beyond its original role of deriving stationarity tests

Evaluation of T-wave Morphology Parameters in Drug-Induced Repolarization Abnormalities

International audienceThis study evaluates the predictive values of T-wave morphology parameters reflecting the repolarization changes by beat to beat calculation of parameters using different mathematical tools to identify additional markers sensitive to the variation induced by drug in the surface of the electrocardiogram. T-wave morphology indicators are extracted from nearly 6 hours recordings of two patients from a clinical d-Sotalol study. The results show that drug induced change in T-wave morphology as well as QT interval. In particular, parameters extracted from spherical coordinates of vectrocardiogram that were not tested before for drug effect evaluation show high sensitivity to small change induced by drugs

Statistical hypothesis testing with time-frequency surrogates to check signal stationarity

International audienceAn operational framework is developed for testing stationarity relatively to an observation scale. The proposed method makes use of a family of stationary surrogates for defining the null hypothesis of stationarity. As a further contribution to the field, we demonstrate the strict-sense stationarity of surrogate signals and we exploit this property to derive the asymptotic distributions of their spectrogram and power spectral density. A statistical hypothesis testing framework is then proposed to check signal stationarity. Finally, some results are shown on a typical model of signals that can be thought of as stationary or nonstationary, depending on the observation scale used

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

International audienceMagneto/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 (PLV, PLV) and AEC (AEC, AEC). 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 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

Features Fusion Using Belief Functions Theory for ARDS Prediction

International audienceInformation fusion techniques are at high interest with the increase in dimensionality of the data being handled. They are applied in different applications, such as in the biomedical domain. This paper proposes an information fusion model that predicts the occurrence of ARDS using vital signs. This model uses features fusion based on the belief functions theory. Different linear and nonlinear parameters are first extracted from the signals, and a parameters selection procedure is proposed to select only pertinent parameters. These parameters are then used to construct mass functions in the belief functions framework. Afterwards, the prediction is performed in real-time by combining all the constructed mass functions. Results present the effectiveness of the belief theory predicting ARDS using the MIMIC II public database. Index Terms-acute respiratory distress syndrome, belief functions theory, features fusion, linear and non-linear parameter

Un modèle d'alerte précoce du SDRA par détection d'anormalité et fusion de données

International audienceLe syndrome de détresse respiratoire aigüe est une maladie qui perturbe le système respiratoire et peut entraîner la mort. Ce papier présente un modèle pour la prédiction de ce syndrome chez des patients ventilés en utilisant leurs signaux vitaux. Le modèle proposé est une approche individuelle consistant à détecter l’anormalité dans les signaux de chaque patient à comparer avec son état initial. Une décision est ensuite conçue par une fusion des décisions partielles des différents signaux analysés. Les résultats obtenus illustrent la capacité prédictive élevée de la méthode avec une sensibilité de 78% et une spécificité de 85%

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

Multi-class Surveillance for Acute Respiratory Distress Syndrome using Belief Functions

International audienceThe high incidence of pathologies implies the necessity of developing and implementing health surveillance technologies. This paper proposes a multi-class surveillance approach for a particular pathology, which is the acute respiratory distress syndrome. The multi-class model uses parameters extraction and belief functions theory applied on four vital signs. Vital signs are heart rate, respiratory rate, blood oxygen saturation and blood pressure. Thus, different linear and nonlinear parameters are extracted from these vital signs. A modeling of each class according to each parameter is performed in the framework of the belief functions theory. Then, these models are affected by a measure of confidence according to each parameter and combined together to lead finally to one model that distinguishes between the multi classes. This multi-class surveillance approach has shown interesting performances for the prediction of ARDS