生物医学工程研究2026，Vol.45Issue(2)：80-84,5.DOI:10.19529/j.cnki.1672-6278.2026.02.01
基于趋势-残差分解与跨组件注意力的低血压预测网络
Hypotension prediction network based on trend-residual decomposition and cross-component attention
摘要
Abstract
To address the problem that existing deep learning models hard to fully utilize the complementary information between the slow-changing hemodynamic trends and fast-changing morphological features in physiological signals,we proposed a hypotension prediction network based on trend-residual decomposition and cross-component attention.Firstly,the trend-residual decomposition strategy was employed to explicitly decompose the continuous physiological signals into low-frequency trend components and high-fre-quency residual components,which correspond to the macroscopic evolution of blood pressure and the microscopic pulsation patterns,respectively.Then,a cross-component attention mechanism was designed to construct bidirectional interactions between the two sets of features,capture long-term trends while sensitively detecting early compensatory signs hidden within the high-frequency waveforms.Experiments on the VitalDB dataset demonstrated that the area under the receiver operating characteristic(AUROC)curve was 89.81%,81.30%and 80.59%for 5,10 and 15 min prediction windows,respectively,significantly outperforming the traditional meth-ods.This model can provide doctors with continuous and accurate intraoperative risk quantitative assessment.关键词
低血压预测/趋势-残差分解/深度学习/注意力机制/心电图/光电容积脉搏波/动脉压Key words
Hypotension prediction/Trend-residual decomposition/Deep learning/Attention mechanism/Electrocardiogram/Photoplethysmogram/Arterial blood pressure分类
医药卫生引用本文复制引用
江科,吴庞,王鹏,方震..基于趋势-残差分解与跨组件注意力的低血压预测网络[J].生物医学工程研究,2026,45(2):80-84,5.基金项目
国家自然科学基金重点项目(62401547) （62401547）
国家自然科学基金项目(62371441) （62371441）
北京市自然科学基金-小米创新联合基金项目(L253012). （L253012）
