Fetal heart rate (FHR) monitoring is one of the central parts of obstetric care. Ultrasound-based technologies such as cardiotocography (CTG) remain the most common method for FHR monitoring. The CTG’s limitations, including subjective interpretation, high interobserver variability, and the need for skilled professionals, led to the development of computerized CTG (cCTG). While cCTG demonstrated advantages, its superiority over visual interpretation remains inconclusive. This has prompted the exploration of alternatives like noninvasive fetal electrocardiography (NIFECG). This review explores the landscape of antenatal FHR monitoring and the need for remote FHR monitoring in a patient-centered care model. Additionally, FHR monitoring needs to evolve from the traditional approach to incorporate artificial intelligence and machine learning. The review underscores the importance of aligning fetal monitoring with modern healthcare, leveraging artificial intelligence algorithms for accurate assessments, and enhancing patient engagement. The physiology of FHR variability (FHRV) is explained emphasizing its significance in assessing fetal well-being. Other measures of FHRV and their relevance are described. It delves into the promising realm of NIFECG, detailing its history and recent technological advancements. The potential advantages of NIFECG are objective FHR assessment, beat-to-beat variability, patient comfort, remote prolonged use, and less signal loss with increased maternal body mass index. Despite its promise, challenges such as signal loss must be addressed. The clinical application of NIFECG, its correlation with cCTG measures, and ongoing technological advancements are discussed. In conclusion, this review explores the evolution of antenatal FHR monitoring, emphasizing the potential of NIFECG in providing reliable, home-based monitoring solutions. Future research directions are outlined, urging longitudinal studies and evidence generation to establish NIFECG’s role in enhancing fetal well-being assessments during pregnancy.

Fetal heart rate (FHR) monitoring is one of the central parts of obstetric care. Ultrasound-based technologies such as cardiotocography (CTG) remain the most common method for FHR monitoring. The CTG’s limitations, including subjective interpretation, high interobserver variability, and the need for skilled professionals, led to the development of computerized CTG (cCTG). While cCTG demonstrated advantages, its superiority over visual interpretation remains inconclusive. This has prompted the exploration of alternatives like noninvasive fetal electrocardiography (NIFECG). This review explores the landscape of antenatal FHR monitoring and the need for remote FHR monitoring in a patient-centered care model. Additionally, FHR monitoring needs to evolve from the traditional approach to incorporate artificial intelligence and machine learning. The review underscores the importance of aligning fetal monitoring with modern healthcare, leveraging artificial intelligence algorithms for accurate assessments, and enhancing patient engagement. The physiology of FHR variability (FHRV) is explained emphasizing its significance in assessing fetal well-being. Other measures of FHRV and their relevance are described. It delves into the promising realm of NIFECG, detailing its history and recent technological advancements. The potential advantages of NIFECG are objective FHR assessment, beat-to-beat variability, patient comfort, remote prolonged use, and less signal loss with increased maternal body mass index. Despite its promise, challenges such as signal loss must be addressed. The clinical application of NIFECG, its correlation with cCTG measures, and ongoing technological advancements are discussed. In conclusion, this review explores the evolution of antenatal FHR monitoring, emphasizing the potential of NIFECG in providing reliable, home-based monitoring solutions. Future research directions are outlined, urging longitudinal studies and evidence generation to establish NIFECG’s role in enhancing fetal well-being assessments during pregnancy.