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 breathing assistance or interventions. Recent advances in wearable FHR monitors have enabled continuous fetal monitoring without compromising maternal mobility. However, sensor displacement during maternal movement, as well as changes in fetal or maternal position, often lead to signal dropout, resulting in gaps in recorded FHR data. Such missing data limits the extraction of meaningful insights and complicates automated (AI-based) analysis. Traditional approaches to handling missing data, such as simple interpolation techniques, often fail to preserve the spectral characteristics of the signals. In this paper, we propose a masked transformer-based autoencoder approach to reconstruct missing FHR signals by capturing both local temporal and frequency components of the data. The proposed method demonstrates robustness across varying durations of missing data and can be used for signal inpainting and forecasting. The proposed approach can be applied retrospectively to research datasets to support the development of AI-based risk algorithms. In the future, the proposed method could be integrated into wearable FHR monitoring devices to achieve earlier and more robust risk detection.

翻译：约10%的新生儿在出生时需要辅助呼吸，约5%需要通气支持。胎心率监测在产前护理中对评估胎儿健康状况起着关键作用，能够检测异常模式并支持及时产科干预，以降低分娩过程中的胎儿风险。应用人工智能方法分析具有不同结局的大规模连续胎心率监测数据集，可能为预测需要呼吸辅助或干预的风险提供新见解。近年来可穿戴胎心率监测设备的进步实现了不限制产妇活动的连续胎儿监测。然而，产妇活动期间的传感器位移以及胎儿或产妇体位变化常导致信号丢失，造成记录的胎心率数据出现缺失。此类缺失数据限制了有意义的洞察提取，并增加了基于AI的自动化分析的复杂性。传统的缺失数据处理方法（如简单插值技术）往往无法保留信号的频谱特性。本文提出一种基于掩码Transformer的自编码器方法，通过捕获数据的局部时间与频率成分来重建缺失的胎心率信号。该方法在不同长度的数据缺失区间均表现出鲁棒性，可用于信号补全和预测。所提出的方法可回顾性地应用于研究数据集，以支持基于AI的风险算法开发。未来，该方法可集成至可穿戴胎心率监测设备中，实现更早期、更稳健的风险检测。