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Optimal time–frequency distributions using a novel signal adaptive method for automatic component detection

Reinhold, Isabella LU and Sandsten, Maria LU (2017) In Signal Processing 133. p.250-259
Abstract

Finding objective methods for assessing the performance of time–frequency distributions (TFD) of measured multi-component signals is not trivial. An optimal TFD should have well resolved signal components (auto-terms) and well suppressed cross-terms. This paper presents a novel signal adaptive method, which is shown to have better performance than the existing method, of automatically detecting the signal components for TFD time instants of two-component signals. The method can be used together with a performance measure to receive automatic and objective performance measures for different TFDs, which allows for an optimal TFD to be obtained. The new method is especially useful for signals including auto-terms of unequal amplitudes and... (More)

Finding objective methods for assessing the performance of time–frequency distributions (TFD) of measured multi-component signals is not trivial. An optimal TFD should have well resolved signal components (auto-terms) and well suppressed cross-terms. This paper presents a novel signal adaptive method, which is shown to have better performance than the existing method, of automatically detecting the signal components for TFD time instants of two-component signals. The method can be used together with a performance measure to receive automatic and objective performance measures for different TFDs, which allows for an optimal TFD to be obtained. The new method is especially useful for signals including auto-terms of unequal amplitudes and non-linear frequency modulation. The method is evaluated and compared to the existing method, for finding the optimal parameters of the modified B-distribution. The performance is also shown for an example set of Heart Rate Variability (HRV) signals.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Detection, Heart rate variability, Multi-component signal, Performance measure, Time–frequency
in
Signal Processing
volume
133
pages
10 pages
publisher
Elsevier
external identifiers
  • scopus:85003967079
  • wos:000392044500023
ISSN
0165-1684
DOI
10.1016/j.sigpro.2016.11.028
language
English
LU publication?
yes
id
3a2a8fff-75db-48b9-8e16-8632ac7d94e1
date added to LUP
2017-02-03 07:19:50
date last changed
2018-01-07 11:47:52
@article{3a2a8fff-75db-48b9-8e16-8632ac7d94e1,
  abstract     = {<p>Finding objective methods for assessing the performance of time–frequency distributions (TFD) of measured multi-component signals is not trivial. An optimal TFD should have well resolved signal components (auto-terms) and well suppressed cross-terms. This paper presents a novel signal adaptive method, which is shown to have better performance than the existing method, of automatically detecting the signal components for TFD time instants of two-component signals. The method can be used together with a performance measure to receive automatic and objective performance measures for different TFDs, which allows for an optimal TFD to be obtained. The new method is especially useful for signals including auto-terms of unequal amplitudes and non-linear frequency modulation. The method is evaluated and compared to the existing method, for finding the optimal parameters of the modified B-distribution. The performance is also shown for an example set of Heart Rate Variability (HRV) signals.</p>},
  author       = {Reinhold, Isabella and Sandsten, Maria},
  issn         = {0165-1684},
  keyword      = {Detection,Heart rate variability,Multi-component signal,Performance measure,Time–frequency},
  language     = {eng},
  month        = {04},
  pages        = {250--259},
  publisher    = {Elsevier},
  series       = {Signal Processing},
  title        = {Optimal time–frequency distributions using a novel signal adaptive method for automatic component detection},
  url          = {http://dx.doi.org/10.1016/j.sigpro.2016.11.028},
  volume       = {133},
  year         = {2017},
}