2,209 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 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
Independent components in spectroscopic analysis of complex mixtures
We applied two methods of "blind" spectral decomposition (MILCA and SNICA) to
quantitative and qualitative analysis of UV absorption spectra of several
non-trivial mixture types. Both methods use the concept of statistical
independence and aim at the reconstruction of minimally dependent components
from a linear mixture. We examined mixtures of major ecotoxicants (aromatic and
polyaromatic hydrocarbons), amino acids and complex mixtures of vitamins in a
veterinary drug. Both MICLA and SNICA were able to recover concentrations and
individual spectra with minimal errors comparable with instrumental noise. In
most cases their performance was similar to or better than that of other
chemometric methods such as MCR-ALS, SIMPLISMA, RADICAL, JADE and FastICA.
These results suggest that the ICA methods used in this study are suitable for
real life applications. Data used in this paper along with simple matlab codes
to reproduce paper figures can be found at
http://www.klab.caltech.edu/~kraskov/MILCA/spectraComment: 22 pages, 4 tables, 6 figure
Data-driven Signal Decomposition Approaches: A Comparative Analysis
Signal decomposition (SD) approaches aim to decompose non-stationary signals
into their constituent amplitude- and frequency-modulated components. This
represents an important preprocessing step in many practical signal processing
pipelines, providing useful knowledge and insight into the data and relevant
underlying system(s) while also facilitating tasks such as noise or artefact
removal and feature extraction. The popular SD methods are mostly data-driven,
striving to obtain inherent well-behaved signal components without making many
prior assumptions on input data. Among those methods include empirical mode
decomposition (EMD) and variants, variational mode decomposition (VMD) and
variants, synchrosqueezed transform (SST) and variants and sliding singular
spectrum analysis (SSA). With the increasing popularity and utility of these
methods in wide-ranging application, it is imperative to gain a better
understanding and insight into the operation of these algorithms, evaluate
their accuracy with and without noise in input data and gauge their sensitivity
against algorithmic parameter changes. In this work, we achieve those tasks
through extensive experiments involving carefully designed synthetic and
real-life signals. Based on our experimental observations, we comment on the
pros and cons of the considered SD algorithms as well as highlighting the best
practices, in terms of parameter selection, for the their successful operation.
The SD algorithms for both single- and multi-channel (multivariate) data fall
within the scope of our work. For multivariate signals, we evaluate the
performance of the popular algorithms in terms of fulfilling the mode-alignment
property, especially in the presence of noise.Comment: Resubmission with changes in the reference lis
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