Abstract:

Hypertension is a major risk factor for CardioVascular Disease （CVD）， and continuous blood pressure monitoring is crucial for CVD prevention. To address the limitations of the existing models in multi-scale feature representation， long-term dependency modeling， and inter-task information interaction， a data-driven blood pressure prediction model named Multi-scale Transformer with Enhanced Graph neural network （MTEG） was proposed to integrate a multi-scale convolution， an enhanced Transformer encoder， a channel attention mechanism， and a graph attention-based multi-task learning strategy to model local and global features of PhotoPlethysmoGraphy （PPG） signals jointly. Specifically， a multi-scale feature extraction module was designed to capture local patterns across different temporal scales in PPG signals， while a Transformer encoder with Rotary Position Embedding （RoPE） and relative position bias was employed to model long-term dependencies in blood pressure variations， a feature-aware enhancement module was introduced to strengthen the response of channels related to blood pressure changes， and a multi-task feature aggregation module was constructed to achieve information collaboration among the prediction tasks of Systolic Blood Pressure （SBP）， Diastolic Blood Pressure （DBP）， and Mean Arterial Pressure （MAP）. Experimental results show that MTEG achieves the Mean Absolute Error （MAE） of 4.92， 2.68， and 2.59 mmHg in SBP， DBP， and MAP predictions， respectively. Predictions for DBP and MAP comply with the standards of the Association for the Advancement of Medical Instrumentation （AAMI） and achieve grade A of the British Hypertension Society （BHS） standard， while SBP prediction achieves grade B of the BHS standard， verifying the reliability and clinical application potential of the proposed model.

Key words: PhotoPlethysmoGraphy (PPG), multi-scale convolution, Transformer, attention mechanism, blood pressure prediction

摘要：

高血压是心血管疾病（CVD）的重要诱因，持续的血压监测对CVD的预防至关重要。针对现有模型在多尺度特征建模能力、长期依赖的建模能力和任务间的信息交互等方面的问题，提出一种名为增强图神经网络的多尺度Transformer （MTEG）的数据驱动血压预测模型，通过融合多尺度卷积、改进Transformer编码器、通道注意力机制和图注意力多任务学习策略，实现光电容积脉搏波（PPG）信号的局部与全局特征联合建模。具体地，通过多尺度特征提取模块捕获PPG信号中不同时间尺度的局部特征，采用基于旋转位置编码（RoPE）和相对位置偏置的Transformer编码器建模血压变化的长期依赖关系，利用特征感知增强模块强化对血压变化相关通道的响应，并构建多任务特征聚合模块实现收缩压（SBP）、舒张压（DBP）和平均动脉压（MAP）这3个预测任务间的信息协同。实验结果表明，MTEG在SBP、DBP和MAP预测中的平均绝对误差（MAE）分别为4.92、2.68和2.59 mmHg，DBP和MAP预测符合美国医疗器械促进协会（AAMI）标准，并达到英国高血压学会（BHS）标准的A级，SBP预测达到BHS标准的B级，验证了所提模型的可靠性与临床应用潜力。

关键词: 光电容积脉搏波, 多尺度卷积, Transformer, 注意力机制, 血压预测