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Block adaptive filters with deterministic reference inputs for event-related signals: BLMS and BRLS

Olmos, S; Sörnmo, Leif LU and Laguna, P (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)
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author
organization
publishing date
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
2007-11-08 08:12:13
date last changed
2017-06-25 04:31:33
@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},
  keyword      = {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},
  volume       = {50},
  year         = {2002},
}