Cardiotocography (CTG) plays a vital role in fetal well-being monitoring by tracking fetal heart rate (FHR) and uterine contractions (UC). However, CTG interpretation suffers from subjectivity, resulting in low agreement among observers and potentially unnecessary medical interventions. Existing AI-based diagnostic models struggle with generalization and are only effective on distinct CTG samples. This study introduces a novel approach employing deep semi-supervised learning for anomaly detection in CTG signals, marking the first attempt in this direction. A modified GANomaly model is proposed, trained on normal CTG data, and evaluated for abnormality detection. This model employs an encoder-decoder structure with a discriminator, minimizing reconstruction and latent space errors while learning the normal CTG signal distribution. Leveraging the CTU-UHB dataset, our model demonstrates superior performance compared to existing methods during inference.

A privacy-preserving adversarial network (PPAN) was recently proposed as an information-theoretical framework to address the issue of privacy in data sharing. The main idea of this model was to use mutual information as the privacy measure and adversarial training of two deep neural networks, one as the mechanism and another as the adversary. The performance of the PPAN model for the discrete synthetic data, MNIST handwritten digits, and continuous Gaussian data was evaluated and compared to the analytically optimal trade-off. In this study, we evaluate the PPAN model for continuous non-Gaussian data, i.e. smart meters data, where lower and upper bounds of the privacy-preserving problem are used. These bounds include the Kraskov (KSG) estimation of entropy and mutual information that is based on the k-th nearest neighbor. In addition to the synthetic data sets, a practical case for hiding the actual electricity consumption from smart meter readings is examined. The results show that for continuous non-Gaussian data, the PPAN model performs within the determined optimal ranges and close to the lower bound.