Synthesis of multi-channel fetal ECG using generative modeling.

Objective. Fetal electrocardiography (fECG) provides beat-to-beat cardiac information, offering the potential to improve the assessment of fetal response to uterine activity. However, the accuracy of fECG-derived cardiotocograms depends on signal quality. Scalp electrode recordings offer reliable fECG but are invasive and measurable only during labor. In contrast, non-invasive fECG measurements suffer from a low signal-to-noise ratio and maternal interference. To advance research on the use of non-invasive fECG, there is a need for large datasets with non-invasive multi-channel fECG recorded simultaneously with a clear reference fECG, such as from a scalp electrode.Approach. We propose a method to address this data scarcity by synthesizing multi-channel fECG signals by modifying inter-beat intervals based on a target fetal heart rate (fHR). The proposed method adjusts the duration of the TP segments between consecutive fECG complexes to effectively match the inter-beat intervals of the target fHR. The adjusted multi-channel TP segments are generated by a generative model that has learned the structure of the TP segments in our dataset. The morphology of the fetal QRS complexes remains unchanged. The fidelity of the synthesized signals is evaluated using signal quality indices (SQIs) and fHR estimates.Main results. Most SQIs computed on the input and synthesized signals exhibited strong correlation (), with low mean absolute differences. The error in the standard deviation of the estimated fHR and that of the target fHR wasBPM. Morphology-based SQIs exhibited strong correlations (), validating the preservation of fetal QRS complexes.Significance. Our approach generates realistic multi-channel fECG signals that match the target fHR patterns, supporting data augmentation and algorithm development for fetal monitoring. It addresses the scarcity of multi-channel fECG measurements with a clean reference available for algorithm development. It could enhance the robustness of data-driven and source separation methods.