Electrocardiogram signal processing for baseline noise removal using blind source separation techniques: A comparative analysis

2011 ◽  
Author(s):  
Akram Rashid ◽  
Zahooruddin ◽  
Ijaz Mansoor Qureshi ◽  
Aamer Saleem
Keyword(s):  
Signal Processing ◽  
Comparative Analysis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Noise Removal ◽  
Separation Techniques ◽  
Baseline Noise ◽  
Electrocardiogram Signal

Applications to Signal Processing - Blind Source Separation

2012 ◽  
pp. 119-138
Author(s):  
Hime Aguiar e Oliveira Junior ◽  
Lester Ingber ◽  
Antonio Petraglia ◽  
Mariane Rembold Petraglia ◽  
Maria Augusta Soares Machado
Keyword(s):  
Signal Processing ◽  
Blind Source Separation ◽  
Source Separation

scholarly journals Blind Source Separation Techniques for the Decomposition of Multiply Labeled Fluorescence Images

2009 ◽  
Vol 96 (9) ◽  
pp. 3791-3800 ◽  
Author(s):  
Richard A. Neher ◽  
Mišo Mitkovski ◽  
Frank Kirchhoff ◽  
Erwin Neher ◽  
Fabian J. Theis ◽  
...  
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Separation Techniques ◽  
Fluorescence Images

Removing muscle and eye artifacts using blind source separation techniques in ictal EEG source imaging

2009 ◽  
Vol 120 (7) ◽  
pp. 1262-1272 ◽  
Author(s):  
H. Hallez ◽  
M. De Vos ◽  
B. Vanrumste ◽  
P. Van Hese ◽  
S. Assecondi ◽  
...  
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Source Imaging ◽  
Separation Techniques ◽  
Ictal Eeg ◽  
Eeg Source Imaging

Robust Blind Source Separation by Beta Divergence

2002 ◽  
Vol 14 (8) ◽  
pp. 1859-1886 ◽  
Author(s):  
Minami Mihoko ◽  
Shinto Eguchi
Keyword(s):  
Signal Processing ◽  
Data Analysis ◽  
Blind Source Separation ◽  
Financial Market ◽  
Source Separation ◽  
Robust Method ◽  
Communication Engineering ◽  
Biological Signal Processing ◽  
Simulation Results ◽  
Linear Mixtures

Blind source separation is aimed at recovering original independent signals when their linear mixtures are observed. Various methods for estimating a recovering matrix have been proposed and applied to data in many fields, such as biological signal processing, communication engineering, and financial market data analysis. One problem these methods have is that they are often too sensitive to outliers, and the existence of a few outliers might change the estimate drastically. In this article, we propose a robust method of blind source separation based on theβ divergence. Shift parameters are explicitly included in our model instead of the conventional way which assumes that original signals have zero mean. The estimator gives smaller weights to possible outliers so that their influence on the estimate is weakened. Simulation results show that the proposed estimator significantly improves the performance over the existing methods when outliers exist; it keeps equal performance otherwise.

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