arXiv：cs.LG· Kjersti Engan, Neel Kanwal, Anita Yeconia, Ladislaus Blacy, Yuda Munyaw, Estomih Mduma, Hege Ersdal·· 4 小时前AI 评分28
FHRFormer：面向胎心率时间序列补全与预测的自监督掩码 Transformer 框架
FHRFormer: A Self-Supervised Masked Transformer Framework for Fetal Heart Rate Time-Series Inpainting and Forecasting
AI 导读
研究者提出 FHRFormer，一种基于掩码 Transformer 自编码器的自监督框架，用于重建缺失的胎心率（FHR）信号，可同时捕捉局部时序与频率成分，支持信号补全和预测，并在不同缺失时长下表现稳健。该方法可用于回顾性处理研究数据集以支持 AI 风险算法开发，未来有望集成到可穿戴 FHR 监测设备中实现更早的风险检测。
正文
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 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.
| Comments: | Submitted to Frontiers in Digital Health |
| Subjects: | Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG); Probability (math.PR) |
| Cite as: | arXiv:2605.29695 [cs.AI] |
| (or arXiv:2605.29695v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.29695 arXiv-issued DOI via DataCite |
Submission history
From: Neel Kanwal PhD [view email]
[v1]
Thu, 28 May 2026 09:55:14 UTC (4,506 KB)
[v2]
Tue, 6 Oct 2026 08:34:15 UTC (3,673 KB)
来源：arXiv：cs.LG · arxiv.org