In this paper, an extended nonlinear Bayesian filtering framework for extracting
electrocardiograms (ECGs) from a single channel as encountered in the fetal ECG extraction
from abdominal sensor is presented.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 magneto cardiograms.
Key words : Extended Kalman filtering(KKF), fetal electrocardiogram (fECG) extraction, model
based filtering, nonlinear Baysian filtering, twin magnetocardiogram (MCG) extraction.