用于无创连续血压预测的多维数据驱动混合Transformer框架
A Multidimensional Data-Driven Hybrid Transformer Framework for Non-invasive Continuous Blood Pressure Prediction
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中文总结 AI 辅助
该研究提出混合Transformer框架，结合多源时间编码器与动态条件融合解码器，在MIMIC-III数据库上实现无创连续血压预测，性能优于基线方法，符合AAMI和BHS A级阈值。
中文摘要 AI 辅助
目的：开发并评估一种使用时间生理特征和人口统计学特征的无袖带连续血压（BP）估计器。方法：我们提出一种混合Transformer框架，用于从心电图（ECG）/光电容积描记（PPG）衍生的特征序列中估计舒张压（DBP）和收缩压（SBP）。该框架不直接处理原始波形，而是对6个生理描述符和2个人口统计学协变量的10步序列进行建模。多源时间编码器模块结合Transformer、柯尔莫哥洛夫-阿诺德网络（Kolmogorov-Arnold Network）和XGBoost分支，以捕获互补的时间、非线性和表格信息；动态条件融合解码器应用差分多头注意力、令牌加权聚合和门控残差校正；采用鲁棒复合目标函数联合优化DBP和SBP。主要结果：使用MIMIC-III波形与临床数据库，源池包含203名受试者的28486个波形片段，特征生成后保留166名受试者的53621个观测值；在2431个片段级保留测试窗口上，舒张压的平均误差±标准差为0.41±3.74 mmHg，收缩压为-1.60±5.95 mmHg，95%一致性界限分别为[-6.93,7.74] mmHg和[-13.25,10.06] mmHg，10 mmHg范围内的比例分别为98.48%和94.36%；该框架在本地重训练基线中实现了最低的标准差和最窄的一致性界限。意义：该特征序列融合框架提高了与参考血压的一致性，在该划分上符合AAMI和BHS A级数值阈值；本回顾性分析并非正式设备验证，临床使用前仍需受试者不重叠和外部评估。
英文摘要
Objective. To develop and evaluate a cuffless continuous blood pressure (BP) estimator using temporal physiological and demographic features. We propose a hybrid Transformer framework to estimate diastolic and systolic BP from ECG/PPG-derived feature sequences. Approach. Rather than raw waveforms, the framework models 10-step sequences of six physiological descriptors and two demographic covariates. A Multi-Source Temporal Encoder Module combines Transformer, Kolmogorov-Arnold Network, and XGBoost branches to capture complementary temporal, nonlinear, and tabular information. A Dynamic Conditional Fusion-Decoder applies differential multi-head attention, token-weighted aggregation, and gated residual correction. A robust composite objective jointly optimizes DBP and SBP. Main results. Using the MIMIC-III Waveform and Clinical Databases, the source pool comprised 28,486 waveform segments from 203 subjects, and feature generation retained 53,621 observations from 166 subjects. On 2,431 segment-level held-out test windows, mean error +/- standard deviation was 0.41 +/- 3.74 mmHg for diastolic BP and -1.60 +/- 5.95 mmHg for systolic BP, with 95% limits of agreement of [-6.93, 7.74] and [-13.25, 10.06] mmHg, respectively. The proportions within 10 mmHg were 98.48% and 94.36%. The framework achieved the lowest standard deviations and narrowest limits of agreement among the locally retrained baselines. Significance. The feature-sequence fusion framework improved agreement with reference BP and fell within numerical AAMI and BHS Grade A thresholds on this split. This retrospective analysis is not formal device validation; subject-disjoint and external evaluation remain necessary before clinical use.
发表机构
- College of Computer Science, Beijing University of Technology（北京工业大学计算机学院）
机构由 AI 辅助整理，请以论文原文为准。
