MATEC Web of Conferences
Volume 44, 20162016 International Conference on Electronic, Information and Computer Engineering
|Number of page(s)||9|
|Section||Computer, Algorithm, Control and Application Engineering|
|Published online||08 March 2016|
Extraction for fetal ECG using single channel blind source separation algorithm based on multi-algorithm fusion
Department of Automation, Northwestern Polytechnical University, Xi’an 710129, China
Nowadays, detecting fetal ECG using abdominal signal is a commonly used method, but fetal ECG signal will be affected by maternal ECG. Current FECG extraction algorithms are mainly aiming at multiple channels signal. They often assume there is only one fetus and did not consider multiple births. This paper proposed a single channel blind source separation (SCBSS) algorithm based on source number estimation using multi-algorithm fusion to process single abdominal signal. The method decomposed collected single channel signal into multiple intrinsic mode function (IMF) utilizing Empirical Mode Decomposition (EMD), mapping single channel into multiple channels. Four multiple channel source number estimation (MCSNE) methods (Bootstrap, Hough, AIC and PCA) were weighting fused to estimate accurate source number and the particle swarm optimization algorithm (PSO) was employed to determine weighted coefficient. According to source number and IMF, nonnegative matrix was constructed and nonnegative matrix factorization (NMF) was employed to separate mixed signals. Experiments used single channel signal mixed by four man-made signals and single channel ECG mixed by two to verify the proposed algorithm. Results showed that the proposed algorithm could determine number of independent signal in single acquired signal. FECG could be extracted from single channel observed signal and the algorithm can be used to solve separation of MECG and FECG.
Key words: Fetal ECG / Single channel blind source separation / Empirical mode decomposition / Multi-algorithm weighting fusion / Nonnegative matrix factorization
© Owned by the authors, published by EDP Sciences, 2016
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