Abstract 4369183: Artificial Intelligence in Non-Invasive Fetal ECG: A Systematic Review of Extraction and Analysis Techniques

Background: Abdominal fetal electrocardiography (fECG) is an emerging noninvasive tool for fetal monitoring. A key challenge is separating the weak fetal signal from overlapping maternal ECG and external noise. Recent advances in machine learning (ML), particularly in deep learning, hold promise for enhancing signal extraction and detecting subtle fetal cardiac issues, such as arrhythmias. These advances could standardize diagnosis and support early intervention, necessitating a structured review of current practices. Hypothesis: We hypothesize that advanced AI techniques and intensive learning models significantly improve the extraction and interpretation of abdominal fECG signals. These models are expected to outperform traditional signal processing in detecting key fetal cardiac features and abnormalities, including arrhythmias and congenital heart disease (CHD), thereby improving diagnostic accuracy and clinical decision-making. Aims: To review and synthesize studies utilizing AI methods for fECG signal processing, with a focus on diagnostic performance in fetal monitoring, arrhythmia detection, and CHD diagnosis. Methods: A systematic search of PubMed, Scopus, Web of Science, and Cochrane databases was conducted for studies published from 2015 to January 2025. Included studies applied AI techniques to fECG data in prenatal evaluation. Data were extracted using a standardized form. Results: Sixty-two studies were included, with sample sizes ranging from 5 to 757 (median: 68) and gestational ages from 21 to 42 weeks. The most commonly used preprocessing methods are bandpass filtering and resampling. Accuracy ranged from 71% to 100%, F1-score from 70.15% to 100%, precision from 75.33% to 100%, and sensitivity from 63% to 100%. CNN-BiLSTM achieved perfect performance across all metrics. Other models (CNN, DenseNet, W-NETR, U-Net) showed near-perfect results, especially for QRS and R-peak detection. For arrhythmia detection, accuracy reached 98.56%, with one CNN model achieving 100% specificity and sensitivity. CHD detection studies have shown accuracies of 71–95%, with the top models achieving 94% across precision, sensitivity, and F1-score. Hybrid and deep learning models consistently outperformed simpler approaches. Conclusion: AI-driven, intensive learning shows strong potential for improving fECG signal quality and diagnostic accuracy. While the results are promising, further clinical validation is necessary for routine implementation in prenatal care.