Non-invasive blood pressure (BP) monitoring plays a crucial role in cardiovascular disease prevention, but traditional cuff-based methods lack continuous monitoring capability. Photoplethysmography (PPG) offers a promising alternative by capturing blood volume changes optically. The challenge of effectively capturing fine-grained discriminative features of BP using limited paired signals persists, despite the benefits offered by deep models trained on extensive data. The objective of this study is to create a Transformer framework for continuous monitoring of noninvasive BP through self-supervised transfer learning and BP pattern adaptation. We developed a data preprocessing strategy that integrates eleven physiological signal transformations to perform unsupervised pseudo-labels for PPG signals. Then, we developed a self-supervised learning network based on the Transformer architecture to extract robust signal representations from transformed PPGs during the pretraining phase. Furthermore, we designed a transfer learning approach that incorporates BP pattern adaptation to derive discriminative features for accurate estimation of BP values. The proposed STP model was evaluated on multi-source datasets containing 1,213 subjects based on a subject-wise paradigm. The clinical standard was met with estimation errors of 0.85 ± 4.21 mmHg and 0.49 ± 2.76 mmHg for systolic BP and diastolic BP, respectively. These results indicate that the STP model performs competitively in terms of BP estimation when compared to current state-of-the-art approaches. Our study presents a novel STP approach for continuous monitoring of noninvasive BP using PPG signals. By integrating BP pattern adaptation, our approach achieves a competitive performance in estimating BP values, demonstrating its potential for clinical applications.

中文翻译：

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STP：基于 Transformer 的自监督迁移学习，利用光电体积描记法进行无创血压估计

无创血压（BP）监测在心血管疾病预防中发挥着至关重要的作用，但传统的基于袖带的方法缺乏连续监测能力。光电体积描记法（PPG）通过光学捕获血容量变化提供了一种有前途的替代方案。尽管基于大量数据训练的深度模型带来了好处，但使用有限的配对信号有效捕获 BP 的细粒度判别特征的挑战仍然存在。本研究的目的是创建一个 Transformer 框架，通过自我监督的迁移学习和血压模式适应来连续监测无创血压。我们开发了一种数据预处理策略，集成了 11 种生理信号转换，以对 PPG 信号执行无监督伪标签。然后，我们开发了一个基于 Transformer 架构的自监督学习网络，以在预训练阶段从转换后的 PPG 中提取鲁棒的信号表示。此外，我们设计了一种迁移学习方法，该方法结合了 BP 模式自适应来导出判别特征，从而准确估计 BP 值。所提出的 STP 模型基于主题范式在包含 1,213 个主题的多源数据集上进行了评估。收缩压和舒张压的估计误差分别为 0.85 ± 4.21 mmHg 和 0.49 ± 2.76 mmHg，符合临床标准。这些结果表明，与当前最先进的方法相比，STP 模型在 BP 估计方面具有竞争力。我们的研究提出了一种新颖的 STP 方法，用于使用 PPG 信号连续监测无创血压。通过整合血压模式适应，我们的方法在估计血压值方面取得了有竞争力的性能，展示了其临床应用的潜力。