A Fetal Electrocardiogram Signal Extraction Algorithm Based on Fast One-Unit Independent Component Analysis with Reference.

Yanfei Jia,Xiaodong Yang

doi:10.1155/2016/5127978

Abstract

Fetal electrocardiogram (FECG) extraction is very important procedure for fetal health assessment. In this article, we propose a fast one-unit independent component analysis with reference (ICA-R) that is suitable to extract the FECG. Most previous ICA-R algorithms only focused on how to optimize the cost function of the ICA-R and payed little attention to the improvement of cost function. They did not fully take advantage of the prior information about the desired signal to improve the ICA-R. In this paper, we first use the kurtosis information of the desired FECG signal to simplify the non-Gaussian measurement function and then construct a new cost function by directly using a nonquadratic function of the extracted signal to measure its non-Gaussianity. The new cost function does not involve the computation of the difference between the function of the Gaussian random vector and that of the extracted signal, which is time consuming. Centering and whitening are also used to preprocess the observed signal to further reduce the computation complexity. While the proposed method has the same error performance as other improved one-unit ICA-R methods, it actually has lower computation complexity than those other methods. Simulations are performed separately on artificial and real-world electrocardiogram signals.

Highlights

The fetal electrocardiogram (FECG) contains much important information about the health and possible diseases of the fetus, which reflects the complete view of the heart activities
The FECG signal is considerably weaker than the maternal electrocardiogram (MECG) and is often embedded in the noise, MECG, baseline wandering, power line interference, and so forth
A number of methods have been reported for extracting the FECG signal, like filtering method [1], singular value decomposition [2], wavelet transform [3], independent component analysis (ICA) [4], and others [5]

Summary

Introduction

The fetal electrocardiogram (FECG) contains much important information about the health and possible diseases of the fetus, which reflects the complete view of the heart activities. It is more sensitive than color Doppler ultrasound in the case of fetal acidosis and anoxia. The FECG extraction based on the ICA methods considers the extracting FECG signal as a blind source separation problem. They consider the unknown FECG signal, MECG, and other interference signals as source signals and the measured signals from the maternal abdomen and chest as mixed signals (measured signals). When the number of measured signals is very large, the extraction of the FECG signal will consume plenty of time

Objectives

Discussion

Conclusion