Block adaptive filters with deterministic reference inputs for event-related signals: BLMS and BRLS
Författare
Summary, in English
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.
Publiceringsår
2002
Språk
Engelska
Sidor
1102-1112
Publikation/Tidskrift/Serie
IEEE Transactions on Signal Processing
Volym
50
Issue
5
Dokumenttyp
Artikel i tidskrift
Förlag
IEEE - Institute of Electrical and Electronics Engineers Inc.
Ämne
- Electrical Engineering, Electronic Engineering, Information Engineering
Nyckelord
- orthogonal
- event-related signal
- adaptive filters
- deterministic input
- expansions
Status
Published
ISBN/ISSN/Övrigt
- ISSN: 1053-587X