Extracting Fetal Electrocardiogram based on a modified fast independent component analysis

Fetal Electrocardiogram (FECG) is a weak signal from the maternal ECG indirectly measured by surface electrodes placed on mother's abdomen. The Fetal signals are buried in other interference signals. Extracting FECG from the strong background interference has an important value in clinical appl...

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Bibliographic Details
Published in:2012 9th International Conference on Fuzzy Systems and Knowledge Discovery pp. 1787 - 1791
Main Authors: Binfeng Xu, Haoyu Jin, Xin Tan, Yarong Hu, Xiaogang Luo
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2012
Subjects:
Algorithm design and analysis
BSS
Convergence
Covariance matrix
EEG
Electrocardiography
Electrodes
FastICA
FECG
ICA
Signal processing algorithms
Vectors
ISBN:9781467300254, 146730025X
Online Access:Get full text
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Summary:Fetal Electrocardiogram (FECG) is a weak signal from the maternal ECG indirectly measured by surface electrodes placed on mother's abdomen. The Fetal signals are buried in other interference signals. Extracting FECG from the strong background interference has an important value in clinical application. ICA is a method for separating blind signals based on signal statistic characteristics. In this paper, the fundamental, discrimination condition and practical algorithm of Independent Component Analysis are discussed. Then, a fast Independent Component Analysis algorithm (FastICA) is introduced. But like FastICA, its convergence is dependent in initial weight. By importing loose gene in the algorithm, the new algorithm could implement convergence in large-scale. By modifying kernel iterate course, several iterations of FastICA are merged into one iteration of Modified FastICA, so the convergence of ICA will be accelerated. Finally, Modified ICA is applied to FECG extraction. The simulation shows that using the improved algorithm convergence speed can be increased.
ISBN:9781467300254
146730025X
DOI:10.1109/FSKD.2012.6233878