For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
An Improved Multivariate Wavelet Denoising Method Using Subspace Projection
Huan HAO Huali WANG Naveed ur REHMAN Liang CHEN Hui TIAN
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2017/03/01
Online ISSN: 1745-1337
Type of Manuscript: PAPER
Category: Digital Signal Processing
wavelet denoising, multivariate signal processing, subspace, principal component analysis,
Full Text: PDF(1.2MB)>>
An improved multivariate wavelet denoising algorithm combined with subspace and principal component analysis is presented in this paper. The key element is deriving an optimal orthogonal matrix that can project the multivariate observation signal to a signal subspace from observation space. Univariate wavelet shrinkage operator is then applied to the projected signals channel-wise resulting in the improvement of the output SNR. Finally, principal component analysis is performed on the denoised signal in the observation space to further improve the denoising performance. Experimental results based on synthesized and real world ECG data verify the effectiveness of the proposed algorithm.