A deep learning approach to fetal-ECG signal reconstruction

Fetal electrocardiogram (FECG) monitoring has become essential due to the current increase in the relative number of cardiac patients worldwide. This paper proposes to use a deep learning approach to compress/recover FECG signals, improving the computation speed in a telemonitoring system. The problem is analogous to the reconstruction of a non-sparse signal in compressive sensing (CS) framework. The architecture incorporates a non-linear mapping using a stacked denoising autoencoder (SDAE). The compression of the raw non-sparse FECG data takes place at the transmitter side using a deep neural network. After pre-training, the whole deep SDAE can be further fine tuned by the mini-batch gradient descent-based back-propagation algorithm. Although the training for SDAE is usually time-consuming, it does not affect the performance due to the one-time off-line training process. The real-time FECG reconstruction is faster due to a few matrix-vector multiplications at the receiver end. The simulations performed by employing standard non-invasive FECG databases shows promising results in terms of the reconstruction quality.