Cardiotocography Signal Abnormality Detection Based on Deep Semi-Unsupervised Learning

Cardiotocography Signal Abnormality Detection Based on Deep Semi-Unsupervised Learning

연구 분야: Artificial Intelligence

학회: BDCAT '23: Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies

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.