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Author:

Li, Chaolan (Li, Chaolan.) | Fang, Bin (Fang, Bin.) | Li, Huijie (Li, Huijie.) | Wang, Pu (Wang, Pu.)

Indexed by:

EI Scopus

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. © 2016 IEEE.

Keyword:

Independent component analysis Extraction Correlation methods Electrocardiography

Author Community:

  • [ 1 ] [Li, Chaolan]College of Electronic and Control Engineering, Beijing University of Technology, Engineering Research Center of Digital Community, Ministry of Education, Beijing, China
  • [ 2 ] [Fang, Bin]College of Electronic and Control Engineering, Beijing University of Technology, Engineering Research Center of Digital Community, Ministry of Education, Beijing, China
  • [ 3 ] [Li, Huijie]College of Electronic and Control Engineering, Beijing University of Technology, Engineering Research Center of Digital Community, Ministry of Education, Beijing, China
  • [ 4 ] [Wang, Pu]Engineering Research Center of Digital Community Ministry of Education, Beijing Laboratory for Urban Mass Transit, Beijing, China

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Year: 2016

Page: 107-111

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 6

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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