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arXiv · 2509.23720

A Self-Adaptive Frequency Domain Network for Continuous Intraoperative Hypotension Prediction

Xian Zeng·Tianze Xu·Kai Yang·Jie Sun·Youran Wang·Jun Xu·Mucheng Ren
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Abstract

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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Xian Zeng, Tianze Xu, Kai Yang, Jie Sun, Youran Wang, Jun Xu, Mucheng Ren. 2025-09-28. A Self-Adaptive Frequency Domain Network for Continuous Intraoperative Hypotension Prediction. https://arxiv.org/abs/2509.23720

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