Monitoring Fetal Heart Rate based on Fine-tuned Transfer Learning and Hybrid Models

Fetal health monitoring is critical for ensuring the well-being of both mother and fetus, with fetal electrocardiography (FECG) playing a pivotal role in detecting heart abnormalities such as arrhythmias and congenital defects. Traditional methods often struggle with challenges like noisy data and interference from maternal direct FECG, which hinder timely and accurate diagnoses. This research aims to develop an advanced deep learning-based classification system to detect abnormal FECG signals, monitor fetal heart patterns, and support healthcare providers with diagnostic insights for improved prenatal care. The study uses advanced deep learning models, such as LSTMs, GRUs, CNN-GRUs, and Transformers, to analyze time-series data from direct FECG recording signals. The dataset, sourced from the PhysioNet FECG database, underwent preprocessing approaches such as bandpass filtering, Independent Component Analysis (ICA), and R-peak detection, etc. to enhance signal quality. The novel CNN-GRU hybrid model achieved the highest performance, with an accuracy of 96.88% and an F1 score of 0.9836, outperforming standalone LSTM and GRU models with an accuracy of 93.75% and the Transformer model with an accuracy of 88.54%. The recommendation system added practical value by generating tailored reports for doctors and patients, bridging the gap between technical analysis and actionable healthcare guidance. By integrating accurate classification, advanced preprocessing, and actionable recommendations, this research study enhances early detection and monitoring of fetal heart conditions. This approach demonstrates the potential for AI-driven solutions to improve maternal-fetal health outcomes, paving the way for future real-time applications in prenatal care.