Neural Network Based Fetal ECG Extraction from Abdominal Signals

Non-invasive fetal ECG extraction from abdominal signals might provide significant information for long-term fetal monitoring, being very attractive for physicist. Nevertheless, accurate extraction of the fetal ECG is a challenging task, due to the disturbing signals, which overlap the signal of interest in the frequency domain. Among the current denoising methods, neural networks are very attractive due to their performance. The current paper proposes a linear feed-forward neural network that estimates very accurately the abdominal mECG, the strongest disturbing signal, based on two thoracic mECG, removing it thereafter. The obtained results are very promising, allowing the further investigation of the fHR, for the fetal well-being evaluation. The comparison with the event-synchronous interference canceller shows the advantage of the neural network in preserving the fECG morphology, with the cost of higher computation time. Both methods require the preprocessing of abdominal signal in order to remove the power line interference and the baseline wander.