STP: Self-supervised transfer learning based on transformer for noninvasive blood pressure estimation using photoplethysmography

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 finegrained 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 multisource 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.