3,060 research outputs found
Time dependent intrinsic correlation analysis of temperature and dissolved oxygen time series using empirical mode decomposition
In the marine environment, many fields have fluctuations over a large range
of different spatial and temporal scales. These quantities can be nonlinear
\red{and} non-stationary, and often interact with each other. A good method to
study the multiple scale dynamics of such time series, and their correlations,
is needed. In this paper an application of an empirical mode decomposition
based time dependent intrinsic correlation, \red{of} two coastal oceanic time
series, temperature and dissolved oxygen (saturation percentage) is presented.
The two time series are recorded every 20 minutes \red{for} 7 years, from 2004
to 2011. The application of the Empirical Mode Decomposition on such time
series is illustrated, and the power spectra of the time series are estimated
using the Hilbert transform (Hilbert spectral analysis). Power-law regimes are
found with slopes of 1.33 for dissolved oxygen and 1.68 for temperature at high
frequencies (between 1.2 and 12 hours) \red{with} both close to 1.9 for lower
frequencies (time scales from 2 to 100 days). Moreover, the time evolution and
scale dependence of cross correlations between both series are considered. The
trends are perfectly anti-correlated. The modes of mean year 3 and 1 year have
also negative correlation, whereas higher frequency modes have a much smaller
correlation. The estimation of time-dependent intrinsic correlations helps to
show patterns of correlations at different scales, for different modes.Comment: 35 pages with 22 figure
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 KayÂżs 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
- …