A Bayesian filtering framework for accurate extracting of the non-invasive FECG morphology

Introduction: The electrocardiogram (ECG) allows for interpretation of the electrical activity of the heart. The information which can be derived from the foetal ECG (FECG) goes beyond heart rate and heart rate variability. However morphological analysis of the FECG waveform is usually not performed in clinical practice. Methods: A Bayesian Filtering Framework based on an Extended Kalman Filter (EKF) for extracting the FECG from a single abdominal channel is described using a training database of 20, one minute maternal-foetal mixtures and evaluated on 200, one minute mixtures. (Data was generated using the simulator, fecgsyn, used to generate a subset of the signals of the Physionet Challenge 2013.) A single pass of the EKF (EKFS) was performed to cancel out the maternal ECG (MECG) in order to build an average FECG morphology. A dual EKF (EKFD, i.e. where both the MECG and FECG cycle morphology were modelled) was then applied to separate the three sources present in the signal mixture (noise, MECG and FECG). A normalised root mean square error and absolute QT error after EKFS and EKFD were calculated. Results: An SNR improvement of 1.97 dB after EKFS and 14.14 dB after EKFD on the test set were achieved. Median absolute error on QT measurement was 17.0 ms for the EKFS and 4.0 ms for the EKFD. Conclusion: This work is a proof of concept that the EKFD allows accurate beat to beat extraction of the FECG morphology from abdominal recordings.