Optimal time–frequency distributions using a novel signal adaptive method for automatic component detection
(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.
(Less)
- author
- Reinhold, Isabella LU and Sandsten, Maria LU
- organization
- publishing date
- 2017-04-01
- 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
- 2024-05-31 22:39:45
@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}},
keywords = {{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}},
doi = {{10.1016/j.sigpro.2016.11.028}},
volume = {{133}},
year = {{2017}},
}