FHRFormer: A Self-Supervised Masked Transformer Framework for Fetal Heart Rate Time-Series Inpainting and Forecasting
arXiv:2605.29695v1 Announce Type: cross Abstract: Approximately 10% of newborns require assistance to initiate breathing at birth, and around 5% need ventilation support. Fetal heart rate (FHR) monitoring plays a crucial role in assessing fetal well-being during prenatal care, enabling the detection of abnormal patterns and supporting timely obstetric interventions to mitigate fetal risks during labor. Applying artificial intelligence (AI) methods to analyze large datasets of continuous FHR monitoring episodes with diverse outcomes may offer novel insights into predicting the risk of needing b
The increasing availability of large, continuous fetal heart rate monitoring datasets and advancements in transformer-based AI models are converging to enable more sophisticated applications in healthcare.
This research demonstrates the potential for AI to dramatically improve prenatal care outcomes by better predicting fetal distress, leading to timely interventions and reducing neonatal complications.
The ability to accurately inpaint missing FHR data and forecast future patterns using self-supervised AI models enhances the reliability and predictive power of an established diagnostic tool.
- · Healthcare providers
- · Expectant parents
- · AI healthcare tech companies
- · Medical device manufacturers
- · Traditional FHR monitoring methods without AI enhancement
Improved detection of fetal distress and reduced rates of neonatal morbidity.
Increased adoption of AI-powered diagnostic tools in obstetrics leading to standardized, data-driven prenatal care protocols.
Potential for integration with other biometric data to create holistic predictive maternal and fetal health platforms, changing the scope of prenatal intervention.
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Read at arXiv cs.LG