Blood pressure (BP) is a key marker for cardiovascular risk assessment and therapeutic decision-making, and Photoplethysmography (PPG) enables low-cost, wearable-friendly cuffless BP estimation. However, even with recent progress, many PPG-based models are trained with BP regression alone and may rely on amplitude-dominated shortcuts. In addition, demographic covariates that systematically modulate vascular compliance are often incorporated only via late fusion, limiting subject-specific representation learning. We propose a Transformer-based network for cuffless BP estimation from PPG signal, leveraging self-attention to capture long-range dependencies across multiple cardiac cycles. To account for subject-specific vascular differences, the model is conditioned on demographics via FiLM-style feature modulation applied through the attention and feed-forward sublayers of Transformer blocks. In addition, we add an auxiliary morphology head to guide the model to attend to BP-relevant waveform morphology associated with arterial stiffness and wave reflection. Under calibration-based evaluation protocols on the large-scale PulseDB dataset, the proposed method achieves MAE of 4.56 mmHg for systolic BP and 2.62 mmHg for diastolic BP, reducing errors by 47% and 50% compared with prior demographic-enhanced PPG baselines. The resulting lightweight, single-sensor model supports scalable and clinically grounded cuffless BP estimation in calibration-enabled deployment settings.

翻译：血压（BP）是心血管风险评估和治疗决策的关键指标，光电容积描记术（PPG）可实现低成本、可穿戴友好的无袖带血压估计。然而，即便近期取得进展，许多基于PPG的模型仅依赖血压回归训练，可能过度利用以振幅为主导的捷径特征。此外，系统性调控血管顺应性的人口统计学协变量通常仅通过后期融合方式引入，限制了主体特异性表征学习。我们提出一种基于Transformer架构的网络，从PPG信号实现无袖带血压估计，通过自注意力机制捕获跨多个心动周期的长程依赖关系。为考虑主体特异性血管差异，模型通过FiLM风格的特征调制实现人口统计信息条件化，该调制方式作用于Transformer模块的注意力层与前馈子层。此外，我们增设辅助形态学引导头，促使模型关注与动脉僵硬度及波反射相关的血压相关波形形态。在大规模PulseDB数据集上基于校准评估协议，所提方法在收缩压与舒张压估计中分别取得4.56 mmHg与2.62 mmHg的平均绝对误差，较此前人口统计增强型PPG基线方法分别降低47%与50%的误差。该轻量化单传感器模型在支持校准的部署场景中，可实现可扩展且具备临床基础的无袖带血压估计。