Common Spatial Pattern with Deep Learning for Fetal Heart Rate Monitoring

In this paper, the problem of monitoring the electrical pulse of the fetal heart using non-invasive electrocardiogram (ECG) signal is studied. To overcome challenges like the overlap between maternal and fetal heart components and noise in the mother's abdomen signal, we propose a new single-channel signal processing technique to detect the fetal QRS (fQRS) events. We first propose a modified sample entropy technique to filter out noisy and complex signal components. Then, common spatial pattern (CSP) technique is employed to find a new sub-space with reduced overlap among maternal and fetal patterns. Then, a one-dimensional convolutional neural network is trained to detect the fQRS events from the abdominal ECG. The obtained results on several well-established datasets are promising and confirm the effectiveness of the proposed approach.