LAUSR

OBJECTIVE Non-invasive fetal electrocardiography has the potential of providing vital information for evaluating the health status of the fetus. However, the low signal-to-noise ratio of the fetal electrocardiogram (ECG) impedes the applicability of the method in clinical practice. Quality improvement of the fetal ECG is of great importance for providing accurate information, enabling support in medical decision making. In this paper, we propose the use of artificial intelligence for the task of one-channel fetal ECG enhancement as a post-processing step after maternal ECG suppression. APPROACH We propose a deep fully convolutional encoder-decoder framework, learning end-to-end mappings from noise-contaminated fetal ECGs to clean ones. Symmetric skip-layer connections are used between corresponding convolutional and transposed convolutional layers to help recovering the signal details. MAIN RESULTS Experiments on synthetic data show an average signal-to-noise ratio (SNR) improvement of 7.5dB for input SNR in the range of -15 to 15dB. Application of the method on real signals and subsequent ECG interval analysis demonstrates a root mean squared error of 9.9 and 14ms for the PR and QT interval, respectively, when compared with simultaneous scalp measurements. The proposed network can achieve a substantial noise removal both on synthetic and real data. In cases of highly noise-contaminated signals some morphological features might be unreliably reconstructed. SIGNIFICANCE The presented method has the advantage of preserving individual variations in pulse shape and beat-to-beat intervals. Moreover, no prior knowledge on the power spectra of the noise or the pulse locations is required.