Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
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In this work, we tackle the problem of fetal electrocardiogram (ECG) extraction from a single sensor. The proposed method is based on non-parametric modelling of the ECG signal described thanks to its second order statistics. Each assumed source in the mixture is thus modelled as a second order process thanks to its covariance function. This modelling allows to reconstruct each source by maximizing the related posterior distribution. The proposed method is tested on synthetic data to evaluate its performance behavior to denoise ECG. It is then applied on real data to extract fetal ECG from a single maternal abdominal sensor.