29,067 research outputs found
Performance evaluation of the Hilbert–Huang transform for respiratory sound analysis and its application to continuous adventitious sound characterization
© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The use of the Hilbert–Huang transform in the analysis of biomedical signals has increased during the past few years, but its use for respiratory sound (RS) analysis is still limited. The technique includes two steps: empirical mode decomposition (EMD) and instantaneous frequency (IF) estimation. Although the mode mixing (MM) problem of EMD has been widely discussed, this technique continues to be used in many RS analysis algorithms.
In this study, we analyzed the MM effect in RS signals recorded from 30 asthmatic patients, and studied the performance of ensemble EMD (EEMD) and noise-assisted multivariate EMD (NA-MEMD) as means for preventing this effect. We propose quantitative parameters for measuring the size, reduction of MM, and residual noise level of each method. These parameters showed that EEMD is a good solution for MM, thus outperforming NA-MEMD. After testing different IF estimators, we propose Kay¿s method to calculate an EEMD-Kay-based Hilbert spectrum that offers high energy concentrations and high time and high frequency resolutions. We also propose an algorithm for the automatic characterization of continuous adventitious sounds (CAS). The tests performed showed that the proposed EEMD-Kay-based Hilbert spectrum makes it possible to determine CAS more precisely than other conventional time-frequency techniques.Postprint (author's final draft
Screening of Obstructive Sleep Apnea with Empirical Mode Decomposition of Pulse Oximetry
Detection of desaturations on the pulse oximetry signal is of great
importance for the diagnosis of sleep apneas. Using the counting of
desaturations, an index can be built to help in the diagnosis of severe cases
of obstructive sleep apnea-hypopnea syndrome. It is important to have automatic
detection methods that allows the screening for this syndrome, reducing the
need of the expensive polysomnography based studies. In this paper a novel
recognition method based on the empirical mode decomposition of the pulse
oximetry signal is proposed. The desaturations produce a very specific wave
pattern that is extracted in the modes of the decomposition. Using this
information, a detector based on properly selected thresholds and a set of
simple rules is built. The oxygen desaturation index constructed from these
detections produces a detector for obstructive sleep apnea-hypopnea syndrome
with high sensitivity () and specificity () and yields better
results than standard desaturation detection approaches.Comment: Accepted in Medical Engineering and Physic
Application of the Hilbert-Huang Transform to the Search for Gravitational Waves
We present the application of a novel method of time-series analysis, the
Hilbert-Huang Transform, to the search for gravitational waves. This algorithm
is adaptive and does not impose a basis set on the data, and thus the
time-frequency decomposition it provides is not limited by time-frequency
uncertainty spreading. Because of its high time-frequency resolution it has
important applications to both signal detection and instrumental
characterization. Applications to the data analysis of the ground and space
based gravitational wave detectors, LIGO and LISA, are described
Kepler Mission Stellar and Instrument Noise Properties
Kepler Mission results are rapidly contributing to fundamentally new
discoveries in both the exoplanet and asteroseismology fields. The data
returned from Kepler are unique in terms of the number of stars observed,
precision of photometry for time series observations, and the temporal extent
of high duty cycle observations. As the first mission to provide extensive time
series measurements on thousands of stars over months to years at a level
hitherto possible only for the Sun, the results from Kepler will vastly
increase our knowledge of stellar variability for quiet solar-type stars. Here
we report on the stellar noise inferred on the timescale of a few hours of most
interest for detection of exoplanets via transits. By design the data from
moderately bright Kepler stars are expected to have roughly comparable levels
of noise intrinsic to the stars and arising from a combination of fundamental
limitations such as Poisson statistics and any instrument noise. The noise
levels attained by Kepler on-orbit exceed by some 50% the target levels for
solar-type, quiet stars. We provide a decomposition of observed noise for an
ensemble of 12th magnitude stars arising from fundamental terms (Poisson and
readout noise), added noise due to the instrument and that intrinsic to the
stars. The largest factor in the modestly higher than anticipated noise follows
from intrinsic stellar noise. We show that using stellar parameters from
galactic stellar synthesis models, and projections to stellar rotation,
activity and hence noise levels reproduces the primary intrinsic stellar noise
features.Comment: Accepted by ApJ; 26 pages, 20 figure
Phasing the Mirror Segments of the Keck Telescopes: The Broadband Phasing Algorithm
To achieve its full diffraction limit in the infrared, the primary mirror of the Keck telescope (now telescopes) must be properly phased: The steps or piston errors between the individual mirror segments must be reduced to less than 100 nm. We accomplish this with a wave optics variation of the Shack–Hartmann test, in which the signal is not the centroid but rather the degree of coherence of the individual subimages. Using filters with a variety of coherence lengths, we can capture segments with initial piston errors as large as ± 30 µm and reduce these to 30 nm—a dynamic range of 3 orders of magnitude. Segment aberrations contribute substantially to the residual errors of ~75 nm
A Semi-Blind Source Separation Method for Differential Optical Absorption Spectroscopy of Atmospheric Gas Mixtures
Differential optical absorption spectroscopy (DOAS) is a powerful tool for
detecting and quantifying trace gases in atmospheric chemistry
\cite{Platt_Stutz08}. DOAS spectra consist of a linear combination of complex
multi-peak multi-scale structures. Most DOAS analysis routines in use today are
based on least squares techniques, for example, the approach developed in the
1970s uses polynomial fits to remove a slowly varying background, and known
reference spectra to retrieve the identity and concentrations of reference
gases. An open problem is to identify unknown gases in the fitting residuals
for complex atmospheric mixtures.
In this work, we develop a novel three step semi-blind source separation
method. The first step uses a multi-resolution analysis to remove the
slow-varying and fast-varying components in the DOAS spectral data matrix .
The second step decomposes the preprocessed data in the first step
into a linear combination of the reference spectra plus a remainder, or
, where columns of matrix are known reference spectra,
and the matrix contains the unknown non-negative coefficients that are
proportional to concentration. The second step is realized by a convex
minimization problem ,
where the norm is a hybrid norm (Huber estimator) that helps to
maintain the non-negativity of . The third step performs a blind independent
component analysis of the remainder matrix to extract remnant gas
components. We first illustrate the proposed method in processing a set of DOAS
experimental data by a satisfactory blind extraction of an a-priori unknown
trace gas (ozone) from the remainder matrix. Numerical results also show that
the method can identify multiple trace gases from the residuals.Comment: submitted to Journal of Scientific Computin
A Surrogate Model of Gravitational Waveforms from Numerical Relativity Simulations of Precessing Binary Black Hole Mergers
We present the first surrogate model for gravitational waveforms from the
coalescence of precessing binary black holes. We call this surrogate model
NRSur4d2s. Our methodology significantly extends recently introduced
reduced-order and surrogate modeling techniques, and is capable of directly
modeling numerical relativity waveforms without introducing phenomenological
assumptions or approximations to general relativity. Motivated by GW150914,
LIGO's first detection of gravitational waves from merging black holes, the
model is built from a set of numerical relativity (NR) simulations with
mass ratios , dimensionless spin magnitudes up to , and the
restriction that the initial spin of the smaller black hole lies along the axis
of orbital angular momentum. It produces waveforms which begin
gravitational wave cycles before merger and continue through ringdown, and
which contain the effects of precession as well as all
spin-weighted spherical-harmonic modes. We perform cross-validation studies to
compare the model to NR waveforms \emph{not} used to build the model, and find
a better agreement within the parameter range of the model than other,
state-of-the-art precessing waveform models, with typical mismatches of
. We also construct a frequency domain surrogate model (called
NRSur4d2s_FDROM) which can be evaluated in and is suitable
for performing parameter estimation studies on gravitational wave detections
similar to GW150914.Comment: 34 pages, 26 figure
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