Signal Quality Assessment for Reliable Fetal Heart Rate Detection Using Deep Learning

The present work is concerned with assessing the signal quality of a four-channel non-invasive fetal ECG recording. The fetal ECG recordings contain multiple fetal and maternal noise sources and are destined to be used to estimate the Fetal Heart Rate (FHR). High signal quality is associated with reliable FHR estimation and low quality with unreliable. We approached the topic from the perspective of anomaly detection, in which low quality signals are considered anomalies. For this purpose, two deep learning-based approaches for anomaly detection were explored. The first approach consisted of autoencoders trained on data considered non-anomalous. Then, the reconstruction error serves as an anomaly metric. To encapsulate the temporal interdependence of the data, we used an LSTM classifier that labelled a segment as an anomaly based on its reconstruction error as well as the reconstruction errors of the segments that preceded it. Two autoencoders were developed, one operating on the time domain and one on the frequency domain. The resulting final pipeline has relatively adequate performance. The second approach was the GAN based anomaly detection. In this technique, a GAN is trained to generate data that are considered non-anomalous using a random vector input sampled from a latent space. Afterwards, a query sample is mapped to this latent space, something that can be done with a few techniques that we discuss, and the mapped latent vector is used to re-generate the segment. The fidelity of the re-generated sample serves as an anomaly score. GAN-based anomaly detection can be conducted locally; so we can train GANs to generate long signals and assess the quality in their segments. In order to generate signals, we proposed a new GAN architecture aimed to generate signals based on their frequency representation. Unfortunately, the GAN based anomaly detection did not work on the target dataset. However, it did work successfully on a simpler dataset, showcasing its efficacy.

Read full text