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A systematic review on AI-driven noninvasive fetal ECG processing and analysis methods
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DOI：10.3389/fdgth.2026.1926578.png)
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Abstract
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IntroductionFetal electrocardiogram is a critical tool for fetal health monitoring; and it is obtained nowadays in clinical practice through an invasive procedure. An alternative is the use of noninvasive fetal electrocardiogram (NI-fECG) that can be obtained by placing a matrix of electrodes on the maternal abdomen. However; the extraction of NI-fECG from abdominal signals (ADSs) remains challenging due to the low amplitude of fetal signals; overlapping with maternal ECG; and because of contamination from other noise sources. Traditional signal processing methods have been widely used but are often limited by incomplete separation; signal distortion; and the need for manual parameter tuning. Artificial intelligence (AI) has emerged as a powerful solution; focusing on deep learning techniques to improve NI-fECG extraction and noise suppression; offering automated feature extraction; adaptability to signal variability; and real-time processing; and addressing the limitations of classical methods. This study presents a comprehensive systematic review of AI methods in NI-fECG processing and analysis; evaluating their effectiveness and limitations.MethodsThe review follows a structured methodology; including study selection and analysis of AI techniques; following PRISMA 2020 guidelines; and covering peer-reviewed studies published between 2014 and 2025. It categorizes AI models into neural network-based; generative; hybrid; and specialized architectures.ResultsTwenty-two studies met the inclusion criteria. AI-based approaches outperformed traditional signal processing in NI-fECG extraction and denoising. Generative and specialized architectures showed the strongest morphology preservation; while hybrid models added robustness. Six studies applied AI to diagnostic tasks; including fetal heart rate estimation; arrhythmia detection; and congenital heart disease classification. Limitations include scarce ground-truth datasets and a lack of standardized evaluation protocols.DiscussionNo single architecture is universally superior across all pipeline stages. A key research gap is the need for AI models that conserve NI-fECG morphology for medical decision-making; alongside standardized benchmarking and explainable models.
Keywords:
artificial intelligence
deep learning
signal processing
fetal monitoring
noninvasive fetal electrocardiogram
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