South China University of Technology, Guangzhou, China

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Abstract—In biomedical engineering, to extract the fetal electrocardiogram (FECG) exactly is a significant and challenging research topic, and so it has been a hot field in biomedical research. Now, a variety of different methods have been proposed to address this problem. From the perspective of blind signal processing, FECG extraction can be modeled as Blind source separation (BSS). In this paper, we present a novel approach, which apply the technique of independent component analysis (ICA) and the theory of wavelet transform, to obtain FECG from the real-life sampled recordings. And in this system diagram, we firstly adopt wavelet de-trending and wavelet de-noising as preprocessing stages to eliminate various kinds of noise, then for these ECG signals processed, in the view of BSS, FastICA algorithm as an ICA method was used to estimate the fetal electrocardiogram signals. Moreover, two different de-trending algorithms are presented to remove the baseline noise. The last but not the least, Performance analysis was provided on the results of the experiment.

Index Terms—Blind source separation, independent component analysis, wavelet detrending, wavelet denoising, fastICA,fetal electrocardiogram extraction.

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Cite: Zhiheng Zhou, Kaiyong Yang, "Fetal Electrocardiogram Extraction and Performance Analysis," Journal of Computers vol. 7, no. 11, pp. 2821-2828, 2012.