Multiresolution analysis of a class of nonstationary processes

Caption title.Includes bibliographical references (p. 24-26).Supported by the ARO. DAAL03-92-G-0115 Supported by the NSF. MIP-9015281H. Krim and J.-C. Pesquet

Stability of heartbeat interval distributions in chronic high altitude hypoxia

Recent studies of nonlinear dynamics of the long-term variability of heart rate have identified nontrivial long-range correlations and scale-invariant power-law characteristics (1/f noise) that were remarkably consistent between individuals and were unrelated to external or environmental stimuli (Meyer et al., 1998a). The present analysis of complex nonstationary heartbeat patterns is based on the sequential application of the wavelet transform for elimination of local polynomial nonstationary behavior and an analytic signal approach by use of the Hilbert transform (Cumulative Variation Amplitude Analysis). The effects of chronic high altitude hypoxia on the distributions and scaling functions of cardiac intervals over 24 hr epochs and 4 hr day/nighttime subepochs were determined from serial heartbeat interval time series of digitized 24 hr ambulatory ECGs recorded in 9 healthy subjects (mean age 34 yrs) at sea level and during a sojourn at high altitude (5,050 m) for 34 days (Ev-K2-CNR Pyramid Laboratory, Sagarmatha National Park, Nepal). The results suggest that there exists a hidden, potentially universal, common structure in the heterogeneous time series. A common scaling function with a stable Gamma distribution defines the probability density of the amplitudes of the fluctuations in the heartbeat interval time series of individual subjects. The appropriately rescaled distributions of normal subjects at sea level demonstrated stable Gamma scaling consistent with a single scaled plot (data collapse). Longitudinal assessment of the rescaled distributions of the 24 hr recordings of individual subjects showed that the stability of the distributions was unaffected by the subject's exposure to a hypobaric (hypoxic) environment. The rescaled distributions of 4 hr subepochs showed similar scaling behavior with a stable Gamma distribution indicating that the common structure was unequivocally applicable to both day and night phases and, furthermore, did not undergo systematic changes in response to high altitude. In contrast, a single function stable over a wide range of time scales was not observed in patients with congestive heart failure or patients after cardiac transplantation. The functional form of the scaling in normal subjects would seem to be attributable to the underlying nonlinear dynamics of cardiac control. The results suggest that the observed Gamma scaling of the distributions in healthy subjects constitutes an intrinsic dynamical property of normal heart function that would not undergo early readjustment or late acclimatization to extrinsic environmental physiological stress, e.g., chronic hypoxi

Measuring Group Velocity in Seismic Noise Correlation Studies Based on Phase Coherence and Resampling Strategies

Seismic noise cross correlation studies are of increasing importance in the seismological research community due to the ubiquity of noise sources and advances on how to use the seismic noise wave field for structural imaging and monitoring purposes. Stacks of noise cross correlations are now routinely used to extract empirical Green's functions between station pairs. In regional and global scale studies, mostly surface waves are extracted due to their dominance in seismic noise wave fields. Group arrival times measured from the time-frequency representation of frequency dispersive surface waves are further used in tomographic inversions to image seismic structure. Often, the group arrivals are not clearly identified or ambiguous depending on the signal and noise characteristics. Here, we present a procedure to robustly measure group velocities using the time-frequency domain phase-weighted stack (PWS) combined with data resampling and decision strategies. The time-frequency PWS improves signal extraction through incoherent signal attenuation during the stack of the noise cross correlations. Resampling strategies help to identify signals robust against data variations and to assess their errors. We have gathered these ingredients in an algorithm where the decision strategies and tuning parameters are reduced for semiautomated processing schemes. Our numerical and field data examples show a robust assignment of surface-wave group arrivals. The method is computational efficient thanks to an implementation based on pseudoanalytic frames of wavelets and enables processing large amounts of data.This work was supported in part by the Project MISTERIOS under Grant CGL2013-48601-C2-1-R, in part by the MIMOSA under Grant ANR-14-CE01-0012, in part by the COST Action ES1401 TIDES, in part by AGAUR, and in part by the FP7 Marie Curie Project through SV's Beatriu de Pinos Fellowship under Contract 600385. This is IPGP contribution 3814.Peer reviewe

Parametric and Nonparametric EEG Analysis for the Evaluation of EEG Activity in Young Children with Controlled Epilepsy

There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform) and a parametric, signal modeling technique (ARMA) are compared and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest) task and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where classification scores of up to 100% were obtained with a linear discriminant classifier

KAStrion project: a new concept for the condition monitoring of wind turbines

International audienceKAStrion was a project entitled “Current and vibration analysis for preventive and predictive condition-based maintenance in wind farms”. It was fund by the KIC InnoEnergy from 2012 to 2014. The aim of this paper is to sum up and highlight the main results of the project. KAStrion goals were to maximize the production time of wind turbine farms by delivering a complete solution build upon a stand-alone analysis system which delivers a continuous on-site pre-diagnostic of the machine based on a multi-modal spectral monitoring technology. This embedded system located in the nacelle is connected to a tailored diagnostic center which delivers a periodic reporting on technical state of each machine of the farm. The strong innovation of KAStrion was to develop firstly a data-driven signal processing, referred to as AStrion, to automatically analyze, detect, classify all the spectral structures (harmonics and sidebands) of vibration signals, and secondly an original approach, referred to as SMESA, to process polyphase electrical signals. Contrary to existing systems, the coupling with the system kinematics is done after the analysis. KAStrion system has been tested on a specific test bench designed as a wind turbine at a smaller scale with load units on the main bearing, the planetary gear box and the output bearing in order to generate defects within an endurance test program. When compared with standard condition monitoring features, KAStrion shows its ability to characterize the start and the stage of the fault without the need of a historical data base. KAStrion system is also continuously tested on 2 two wind turbines in Arfons windfarm in Franc