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

• URL: http://arxiv.org/abs/2509.23720v1
• Date: Sun, 28 Sep 2025 08:02:28 GMT
• Title: A Self-Adaptive Frequency Domain Network for Continuous Intraoperative Hypotension Prediction
• Authors: Xian Zeng, Tianze Xu, Kai Yang, Jie Sun, Youran Wang, Jun Xu, Mucheng Ren,
• Abstract summary: Intraoperative hypotension (IOH) is strongly associated with postoperative complications, including delirium and increased mortality.<n>Existing methods face limitations in incorporating both time and frequency domain information.<n>We propose a novel Self-Adaptive Frequency Domain Network (SAFDNet)
• Score: 9.841996321633298
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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