Physio-Mamba: Physiologically-Guided Mamba Network for Continuous Calibration-Free Abp Reconstruction from PPG

Continuous Arterial Blood Pressure (ABP) monitoring requires invasive arterial catheterization, while non-invasive cuff-based devices only provide intermittent spot readings and are unsuitable for continuous monitoring. We introduce Physio-Mamba, a physiology-informed framework for reconstructing continuous ABP waveforms from photoplethysmography (PPG). The model encodes physiology at the input by processing PPG together with the acceleration photoplethysmogram (APG), and at the objective by using a Physiological-Dynamic loss that aligns second-order pressure dynamics to preserve clinically salient features. A Mamba-based MaskNet offers bidirectional long-context temporal modeling with linear time and memory complexity. In subject-independent splits of the Sensors dataset and cross-dataset testing on BCG, Physio-Mamba achieves competitive accuracy, preserves key morphology such as the systolic upstroke and the dicrotic notch, and generalizes across datasets. These results show that embedding explicit physiological priors is a practical and effective route to cuffless ABP reconstruction. Furthermore, they motivate causal real-time variants, uncertainty-aware outputs, and broader clinical validation.