Martin Clinton Tosima Manullang, Yuan-Hsiang Lin, Nai-Kuan Chou
Blood pressure (BP) monitoring is essential for diagnosing and managing various health conditions. While traditional contact-based methods have been effective, they can be uncomfortable for continuous or prolonged monitoring. The innovative discovery of remote photoplethysmography (rPPG) brings a new era for noncontact BP measurement. In this article, a transformer-based deep learning network named BP network (BPNet) was proposed to estimate noncontact BP from RGB videos. The BPNet comprises three primary components: the signal branch, feature branch, and predictor. The architecture is designed to integrate information from rPPG signal and their derivatives, rPPG features, and user inputs. A standout feature of our model is its capability to work without the need for calibration, making it more user-friendly. We assessed our model, BPNet, using two diverse datasets: our BESTLab dataset and the externally sourced Vital Video (VV) dataset, which is noted for its varied subject demographics and extensive BP distribution. The results show that BPNet outperforms recent benchmarks, marking a significant advancement in noncontact BP measurement technology. It also showed greater efficiency in terms of inference time and model complexity. In the future, the approach might focus on developing a fully automated deep learning system that removes the need for manual preprocessing and rPPG extraction. Furthermore, adding subject's demographic features and medical history could improve accuracy. © 2024 IEEE.
National Taiwan University of Science and Technology, Department of Electronic and Computer Engineering, Taipei, 10607, Taiwan; Institut Teknologi Sumatera, Department of Informatics, Lampung, 106335, Indonesia; National Taiwan University Hospital, Department of Cardiovascular Surgery, Taipei, 10002, Taiwan
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