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 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 dropouts, resulting in gaps in the recorded FHR data. Such missing data limits the extraction of meaningful insights and complicates automated (AI-based) analysis. Traditional approaches to handle 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 spatial 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.
Abstract (translated)
大约有10%的新生儿在出生时需要帮助才能开始呼吸，而约5%的婴儿则需要机械通气支持。胎儿心率（FHR）监测在产前护理中对于评估胎儿健康状况起着至关重要的作用，能够检测出异常模式并支持及时进行助产干预以降低分娩期间对胎儿的风险。利用人工智能（AI）方法分析大量连续FHR监测数据集，并结合不同的结果，可能为预测需要呼吸辅助或干预的新生儿风险提供新的见解。最近可穿戴式FHR监视器的进步使在不牺牲孕妇活动自由度的情况下实现持续性胎儿监控成为可能。然而，在母亲移动时传感器偏移以及胎位和母体位置变化常常会导致信号丢失，产生记录中的心率数据空白。这种缺失的数据限制了提取有意义的见解，并增加了自动化（基于AI）分析的复杂性。传统的处理缺失数据的方法，例如简单的插值技术，往往无法保留信号的频谱特性。 本文提出了一种基于掩码变换器的自动编码器方法来重建缺失的心率信号，该方法能够捕捉到数据的空间和频率成分。所提出的方法在面对不同长度的数据丢失时表现出稳健性，并可用于信号修补（inpainting）和预测。这一新方法可以应用于研究数据集以支持开发基于AI的风险算法的研究工作。在未来，这种方法可能被整合进可穿戴FHR监测设备中，从而实现更早且更为可靠的危险预警。
URL
https://arxiv.org/abs/2509.20852
