A novel method of FECG extraction combined self-correlation analysis with ICA

Chaolan Li, B. Fang, Huijie Li, Pu Wang
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引用次数: 5

Abstract

The extraction of fetal electrocardiogram (FECG) signal has an important value in clinical application. The FECG is extracted from the maternal abdominal signal which is collected by several electrodes placed on the maternal abdomen. The traditional independent component analysis (ICA) model does not consider the temporal correlation in the process of separating maternal abdomenal signal. In this paper, a new method for extracting FECG is proposed, which combines self-correlation analysis with independent component analysis. Firstly, the self-correlation analysis is used for intercepting signals, which can decrease the temporal correlation. Then FastICA is applied to obtain the model parameters of ICA model. Finally, bring the mixed-signal into this model to extract the fetal ECG. The experiments are conducted by clinical signals. The results indicate that the method proposed in this paper could extract FECG well. This method is superior to traditional FastICA.
一种将自相关分析与ICA相结合的feg提取新方法
胎儿心电图信号的提取在临床应用中具有重要价值。FECG是从母体腹部信号中提取的,该信号由放置在母体腹部的几个电极收集。传统的独立分量分析(ICA)模型在分离母体腹部信号的过程中没有考虑时间相关性。本文提出了一种将自相关分析与独立分量分析相结合的feg提取方法。首先,对信号进行自相关分析,降低了信号的时间相关性;然后应用FastICA软件获取ICA模型的模型参数。最后,将混合信号引入该模型,提取胎儿心电信号。实验是通过临床信号进行的。结果表明,该方法能较好地提取脑电图。该方法优于传统的FastICA。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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