Letters: Semi-blind source extraction for fetal electrocardiogram extraction by combining non-Gaussianity and time-correlation

Authors:
Zhenwei Shi;Changshui Zhang
Affiliations:
State Key Laboratory of Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing 100084, PR China;State Key Laboratory of Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing 100084, PR China
Venue:
Neurocomputing
Year:
2007

Citing 7
Cited 13

Independent component analysis by general nonlinear Hebbian-like learning rules

Signal Processing - Special issue on neural networks
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Extraction of Specific Signals with Temporal Structure

Neural Computation
Complexity Pursuit: Separating Interesting Components from Time Series

Neural Computation
Letters: A fast fixed-point algorithm for complexity pursuit

Neurocomputing
Letters: Gaussian moments for noisy complexity pursuit

Neurocomputing
An EM method for spatio-temporal blind source separation using an AR-MOG source model

ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation

Letters: MACBSE: Extracting signals with linear autocorrelations

Neurocomputing
Blind source extraction: Standard approaches and extensions to noisy and post-nonlinear mixing

Neurocomputing
Semi-blind source extraction algorithm for fetal electrocardiogram based on generalized autocorrelations and reference signals

Journal of Computational and Applied Mathematics
A fixed-point algorithm for blind source separation with nonlinear autocorrelation

Journal of Computational and Applied Mathematics
Blind Source Extraction for Noisy Mixtures by Combining Gaussian Moments and Generalized Autocorrelations

Neural Processing Letters
Sequential row-column independent component analysis for face recognition

Neurocomputing
Blind source separation with nonlinear autocorrelation and non-Gaussianity

Journal of Computational and Applied Mathematics
Blind source extraction based on generalized autocorrelations and complexity pursuit

Neurocomputing
Fast nonlinear autocorrelation algorithm for source separation

Pattern Recognition
Comparative analysis of electrocardiogram data by means of temporal locality approach with additional normalization

Transactions on computational science I
Blind Source Separation Using Quadratic form Innovation

Neural Processing Letters
Hybrid linear and nonlinear complexity pursuit for blind source separation

Journal of Computational and Applied Mathematics
Approaches and applications of semi-blind signal extraction for communication signals based on constrained independent component analysis: The complex case

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

Fetal electrocardiogram (FECG) extraction is a vital issue in biomedical signal processing and analysis. A promising approach is blind (semi-blind) source extraction. In this paper, we develop an objective function for extraction of temporally correlated sources. The objective function is based on the non-Gaussianity and the autocorrelations of source signals, and it contains the well-known mean squared error objective function presented by Barros and Cichocki [Extraction of specific signals with temporal structure, Neural Comput. 13(9) (2001) 1995-2003] as a special example. Minimizing the objective function, we propose a source extraction algorithm. The algorithm extracts the clearer FECG as the first extracted signal and is very robust to the estimated error of time delay. It means that the algorithm is an appealing method which obtains an accurate and reliable FECG.