用自适应频域网络提前预警术中低血压，提升预测准确率。
A Self-Adaptive Frequency Domain Network for Continuous Intraoperative Hypotension Prediction
- 结合时域与频域信息，自适应滤除信号噪声。
- 在两个真实数据集上达97.3% AUROC，优于现有模型。
- 适合临床实用，对噪声不敏感，可实时预警。
术中低血压（IOH）与术后谵妄和死亡率显著相关，早期预警对围手术期管理至关重要。尽管已有多种人工智能模型用于提供IOH预警，但现有方法在融合时间与频域信息、捕捉长短时依赖关系以及应对生物信号噪声方面仍存在局限。为此，我们提出一种新型自适应频域网络（SAFDNet）。SAFDNet引入自适应谱块，利用傅里叶分析提取频域特征，并通过自适应阈值抑制噪声；同时设计交互注意力块，有效捕获数据中的长短期依赖。在两个大规模真实世界数据集上的内外部验证表明，SAFDNet在IOH早期预警中达到最高97.3%的AUROC，显著优于当前先进模型。此外，该模型具备强鲁棒性与低噪声敏感度，适用于实际临床应用。
原文摘要 · Abstract (English)
Intraoperative hypotension (IOH) is strongly associated with postoperative complications, including postoperative delirium and increased mortality, making its early prediction crucial in perioperative care. While several artificial intelligence-based models have been developed to provide IOH warnings, existing methods face limitations in incorporating both time and frequency domain information, capturing short- and long-term dependencies, and handling noise sensitivity in biosignal data. To address these challenges, we propose a novel Self-Adaptive Frequency Domain Network (SAFDNet). Specifically, SAFDNet integrates an adaptive spectral block, which leverages Fourier analysis to extract frequency-domain features and employs self-adaptive thresholding to mitigate noise. Additionally, an interactive attention block is introduced to capture both long-term and short-term dependencies in the data. Extensive internal and external validations on two large-scale real-world datasets demonstrate that SAFDNet achieves up to 97.3\% AUROC in IOH early warning, outperforming state-of-the-art models. Furthermore, SAFDNet exhibits robust predictive performance and low sensitivity to noise, making it well-suited for practical clinical applications.
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