Abstract
This paper presents a novel method for extracting the fetal ECG (FECG) from a single-lead abdominal signal. A dynamical model for a modified abdominal signal is proposed, in which both the maternal ECG (MECG) and the FECG are modeled, and then a parallel marginalized particle filter (par-MPF) is used for tracking the abdominal signal. Finally, the FECG and MECG are simultaneously separated. Several experiments are conducted using both simulated and clinical signals. The results indicate that the method proposed in this paper effectively extracts the FECG and outperforms other Bayesian filtering algorithms.
Highlights
Fetal ECG (FECG) reflects changes in fetal heart activity and is used as a primary method for evaluating fetal heart status
It is well understood that the abdominal electrocardiogram (AECG) is a mixture of the maternal ECG (MECG), fetal ECG (FECG) and noise
To verify the feasibility and reliability of the proposed algorithm, both simulated and clinical data were used to study the performance of the proposed method, and other methods were tested with the same signals, including extended Kalman smoother (EKS), Extended Kalman filtering (EKF) and the Bayesian adaptive neuro fuzzy inference system [18]
Summary
Fetal ECG (FECG) reflects changes in fetal heart activity and is used as a primary method for evaluating fetal heart status. The properties of the wavelet transformation allow the extraction of the ECG waveforms from noise and artifact cancellation These methods require the proper selection of the mother wavelet and scale to identify the frequency components in the signal. If the assumption does not hold, the prevailing Bayesian filtering methods that are based on the Gaussian model [13] may fail to address the non-Gaussian and nonlinear FECG signals. Another technique, referred to as MPF, is proposed in this paper. A modified abdominal signal dynamical model containing MECG and FECG is proposed. The results and discussion of the different filtering methods are presented in Section 5; in Section 6, some of the main conclusions are noted
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