Fetal ECG Extraction by Extended State Kalman Filtering Based on Single-Channel Recordings

被引：101

作者：

Niknazar, Mohammad ^{[1
]}

Rivet, Bertrand ^{[1
]}

Jutten, Christian ^{[1
,2
]}

机构：

[1] Univ Grenoble 1, GIPSA Lab, CNRS, UMR 5216, F-38402 Grenoble, France

[2] Inst Univ France, F-75005 Paris, France

来源：

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | 2013年 / 60卷 / 05期

关键词：

Extended Kalman filtering (EKF); fetal electrocardiogram (fECG) extraction; model-based filtering; nonlinear Bayesian filtering; twin magnetocardiogram (MCG) extraction; ELECTROCARDIOGRAM EXTRACTION; SEPARATION;

D O I：

10.1109/TBME.2012.2234456

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

In this paper, we present an extended nonlinear Bayesian filtering framework for extracting electrocardiograms (ECGs) from a single channel as encountered in the fetal ECG extraction from abdominal sensor. The recorded signals are modeled as the summation of several ECGs. Each of them is described by a nonlinear dynamic model, previously presented for the generation of a highly realistic synthetic ECG. Consequently, each ECG has a corresponding term in this model and can thus be efficiently discriminated even if the waves overlap in time. The parameter sensitivity analysis for different values of noise level, amplitude, and heart rate ratios between fetal and maternal ECGs shows its effectiveness for a large set of values of these parameters. This framework is also validated on the extractions of fetal ECG from actual abdominal recordings, as well as of actual twin magnetocardiograms.

引用

页码：1345 / 1352

页数：8