AI-assisted fetal heart monitoring: a CTG classification model combining attention mechanism and convolutional neural networks

Objective: To develop a deep-learning-based computer vision approach for fetal heart rate (FHR) monitoring that can efficiently detect fetal hypoxia without relying on complex feature extraction methods. Methods: A hybrid attention mechanism was proposed for direct processing of fetal monitoring images (cardiotocography, CTG), eliminating the need for manual feature extraction. The method leverages deep learning to classify fetal health states based on real-time CTG images. Results: Experiments on a real-world clinical dataset demonstrated that the proposed method achieved a classification accuracy of 97.94%, indicating its high efficiency in detecting fetal hypoxia. Conclusion: The proposed hybrid attention-based deep learning approach provides reliable support for the early detection of fetal hypoxia, overcoming the limitations of traditional machine learning methods that rely on complex feature extraction.