Title :
On the use of simulated annealing to automatically assign decorrelated components in second-order blind source separation
Author :
Bohm, M. ; Stadlthanner, K. ; Gruber, P. ; Theis, F.J. ; Lang, E.W. ; Tome, A.M. ; Teixeira, A.R. ; Gronwald, W. ; Kalbitzer, H.R.
Author_Institution :
Inst. of Biophys., Regensburg Univ.
fDate :
5/1/2006 12:00:00 AM
Abstract :
In this paper, an automatic assignment tool, called BSS-AutoAssign,for artifact-related decorrelated components within a second-order blind source separation (BSS) is presented. The latter is based on the recently proposed algorithm dAMUSE, which provides an elegant solution to both the BSS and the denoising problem simultaneously. BSS-AutoAssign uses a local principal component analysis (PCA)to approximate the artifact signal and defines a suitable cost function which is optimized using simulated annealing. The algorithms dAMUSE plus BSS-AutoAssign are illustrated by applying them to the separation of water artifacts from two-dimensional nuclear overhauser enhancement (2-D NOESY)spectroscopy signals of proteins dissolved in water
Keywords :
biomedical NMR; blind source separation; decorrelation; medical signal processing; molecular biophysics; nuclear Overhauser effect; principal component analysis; proteins; signal denoising; simulated annealing; artifact-related decorrelated components; automatic assignment tool BSS-AutoAssign; dAMUSE; denoising problem; principal component analysis; proteins; second-order blind source separation; simulated annealing; two-dimensional nuclear Overhauser enhancement spectroscopy signals; water artifacts; Analytical models; Blind source separation; Cost function; Decorrelation; Noise reduction; Principal component analysis; Proteins; Simulated annealing; Source separation; Two dimensional displays; 2-D NOESY NMR; Blind source separation; generalized eigenvalue decomposition; matrix pencil; simulated annealing; Algorithms; Artifacts; Artificial Intelligence; Complex Mixtures; Magnetic Resonance Spectroscopy; Pattern Recognition, Automated; Proteins; Statistics as Topic; Water;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2005.863968