Non-contact blood pressure estimation using FMCW radar: A two-stream approach focused on central arterial activity

• Published in: Biomedical signal processing and control Vol. 106; p. 107718
• Main Authors: Bai, Zhongrui, Geng, Fanglin, Zhang, Hao, Chen, Xianxiang, Du, Lidong, Wang, Peng, Wu, Pang, Cheng, Gang, Fang, Zhen, Wu, Yirong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2025
• Subjects: Central-artery pulse transit time; Doppler Cardiogram; Non-contact blood pressure estimation; Two-stream neural network; Central-artery pulse transit time; Two-stream neural network; Non-contact blood pressure estimation; Doppler Cardiogram
• ISSN: 1746-8094
• DOI: 10.1016/j.bspc.2025.107718

This paper proposes a radar-based two-stream blood pressure (BP) estimation framework (R2S-BP), focusing on central arterial activity. It separately analyzes central-arterial pulse transit time (caPTT) and pulse wave morphology using multi-location Doppler Cardiogram (DCG) data from millimeter wave FMCW radar. Specifically, phase information at harmonic heart rate frequencies is used to compute time delay arrays, representing caPTT-related features. Additionally, k-Shape clustering is employed to select optimal DCGs from the neck and chest regions that contain BP-related morphological features. These features are processed through a two-stream neural network combining BiLSTM, ResNet, and multi-head attention modules. Subject-independent 9-fold cross-validation results show that the standard deviations of the errors for systolic and diastolic BP are 7.33 and 5.36 mmHg, respectively. The intra-subject correlation coefficient for both systolic and diastolic BP averages 0.82. Comparative and ablation studies demonstrate the superiority of the two-stream approach and the critical importance of its components. This approach integrates physiologically guided manual feature construction with a deep learning model, fully leveraging the capabilities of FMCW radar data. •Proposes a two-stream framework for FMCW radar-based blood pressure estimation.•Proposes multiple customized central arterial pulse feature extraction methods.•Utilizes a two-stream network with BiLSTM, ResNet, and attention for feature analysis.•Validates the method through experiments on 36 subjects with subject-independent scheme.