Contactless Arterial Blood Pressure Waveform Monitoring with mmWave Radar

Abstract

Arterial blood pressure waveform (ABPW) offers comprehensive insights into cardiovascular health compared to discrete blood pressure measurements. However, accurately estimating shapes and pressure values of ABPW points in a beat-to-beat manner poses significant challenges. Current ABPW monitoring methods require invasive procedures or continuous skin contact, which are inconvenient and unsatisfactory. Thus, we propose WaveBP, the first contactless ABPW monitoring system utilizing a commercial mmWave radar, driven by the understanding that cardiac information serves as an implicit bridge between mmWave signals and ABPW based on a hemodynamics analysis model. To preserve waveform details, we design a hybrid Transformer model called mmFormer, incorporated with spatially-informed shortcuts. mmFormer enables consistent sequence-to-sequence transformations while accommodating different levels of personalization efforts. To mitigate the inherent instability of mmWave signals, we develop a beamforming-based data augmentation approach that has been empirically and theoretically proven to enhance robustness with multiple spatial observations. Additionally, we introduce a cross-modality knowledge transfer framework to fuse knowledge from cardiac modalities (ECG/PPG) with vibrations captured in mmWave reflections, improving accuracy without requiring extra deployment overhead. Extensive evaluations conducted on 43 subjects using a leave-one-subject-out setup validate that WaveBP achieves a high waveform correlation of 0.903 and exhibits a low (mean±standard deviation) error of point-level measurements at (-0.14±7.48) mmHg, which could be further reduced by subject-specific specialization. WaveBP demonstrates remarkable performance under challenging scenarios and exhibits potential for detailed cardiac estimations, as evidenced by our case studies on relative cardiac output estimation and cardiac abnormality detection.