Block adaptive filters with deterministic reference inputs for event-related signals: BLMS and BRLS
(2002) In IEEE Transactions on Signal Processing 50(5). p.1102-1112- Abstract
- Adaptive estimation of the linear coefficient vector in truncated expansions is considered for the purpose of modeling noisy, recurrent signals. Two different criteria are studied for block-wise processing of the signal: the mean square error (MSE) and the least squares (LS) error. The block LMS (BLMS) algorithm, being the solution of the steepest descent strategy for minimizing the MSE, is shown to be steady-state unbiased and with a lower variance than the LMS algorithm. It is demonstrated that BLMS is equivalent to an exponential averager in the subspace spanned by the truncated set of basis functions. The block recursive least squares (BRLS) solution is shown to be equivalent to the BLMS algorithm with a decreasing step size. The BRLS... (More)
- Adaptive estimation of the linear coefficient vector in truncated expansions is considered for the purpose of modeling noisy, recurrent signals. Two different criteria are studied for block-wise processing of the signal: the mean square error (MSE) and the least squares (LS) error. The block LMS (BLMS) algorithm, being the solution of the steepest descent strategy for minimizing the MSE, is shown to be steady-state unbiased and with a lower variance than the LMS algorithm. It is demonstrated that BLMS is equivalent to an exponential averager in the subspace spanned by the truncated set of basis functions. The block recursive least squares (BRLS) solution is shown to be equivalent to the BLMS algorithm with a decreasing step size. The BRLS is unbiased at any occurrence number of the signal and has the same steady-state variance as the BLMS but with a lower variance at the transient stage. The estimation methods can be interpreted in terms of linear, time-variant filtering. The performance of the methods is studied on an ECG signal, and the results show that the performance of the block algorithms is superior to that of the LMS algorithm. In addition, measurements with clinical interest are found to be more robustly estimated in noisy signals. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/340318
- author
- Olmos, S ; Sörnmo, Leif LU and Laguna, P
- organization
- publishing date
- 2002
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- orthogonal, event-related signal, adaptive filters, deterministic input, expansions
- in
- IEEE Transactions on Signal Processing
- volume
- 50
- issue
- 5
- pages
- 1102 - 1112
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- wos:000174935200010
- scopus:0036570719
- ISSN
- 1053-587X
- DOI
- 10.1109/78.995066
- language
- English
- LU publication?
- yes
- id
- 8c558dd6-cad9-44d5-b599-4b75bda4dad0 (old id 340318)
- date added to LUP
- 2016-04-01 17:05:47
- date last changed
- 2022-01-29 00:17:39
@article{8c558dd6-cad9-44d5-b599-4b75bda4dad0,
abstract = {{Adaptive estimation of the linear coefficient vector in truncated expansions is considered for the purpose of modeling noisy, recurrent signals. Two different criteria are studied for block-wise processing of the signal: the mean square error (MSE) and the least squares (LS) error. The block LMS (BLMS) algorithm, being the solution of the steepest descent strategy for minimizing the MSE, is shown to be steady-state unbiased and with a lower variance than the LMS algorithm. It is demonstrated that BLMS is equivalent to an exponential averager in the subspace spanned by the truncated set of basis functions. The block recursive least squares (BRLS) solution is shown to be equivalent to the BLMS algorithm with a decreasing step size. The BRLS is unbiased at any occurrence number of the signal and has the same steady-state variance as the BLMS but with a lower variance at the transient stage. The estimation methods can be interpreted in terms of linear, time-variant filtering. The performance of the methods is studied on an ECG signal, and the results show that the performance of the block algorithms is superior to that of the LMS algorithm. In addition, measurements with clinical interest are found to be more robustly estimated in noisy signals.}},
author = {{Olmos, S and Sörnmo, Leif and Laguna, P}},
issn = {{1053-587X}},
keywords = {{orthogonal; event-related signal; adaptive filters; deterministic input; expansions}},
language = {{eng}},
number = {{5}},
pages = {{1102--1112}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Transactions on Signal Processing}},
title = {{Block adaptive filters with deterministic reference inputs for event-related signals: BLMS and BRLS}},
url = {{http://dx.doi.org/10.1109/78.995066}},
doi = {{10.1109/78.995066}},
volume = {{50}},
year = {{2002}},
}