HemoSC-P: A Hemodynamic Semantic Channel Paradigm for Cardiovascular Parameter Estimation

As a leading cause of death worldwide, cardiovascular disease demands more precise monitoring and early warning systems, posing significant challenges to modern healthcare. However, cardiovascular early warning systems often face two major dilemmas: the "black box dilemma" leads to unreliable estimation results due to limited physiological interpretability, and limited robustness across subjects and blood pressure states arises from physiological heterogeneity. This study innovatively maps the methods of semantic extraction and channel modeling to the cardiovascular system, inspired by the 6G network concept of transmitting meaning rather than data under the semantic communication paradigm. It proposes a novel hemodynamic channel-guided cardiovascular parameter estimation paradigm (HemoSC-P). This paradigm adopts a dual-pillar modeling framework: the semantic pillar utilizes a multi-scale convolutional and phase-aware attention architecture to model the non-stationary dynamics of cardiovascular signals, elevating feature alignment from the temporal domain to the physiological domain. The cardiovascular channel, parameterized by the Windkessel circuit model, serves dual roles as a semantic pathway from cardiac contractions to observable physiological signals and as a repository of physiological knowledge. It guides channel-level physiologically deformable attention to achieve inverse estimation of cardiovascular parameters. To validate this paradigm, this study employs non-invasive blood pressure estimation as a representative case study. Validation was conducted on three representative public datasets (UCI-BP, MIMIC-III, PPG-BP). On MIMIC-III, the mean absolute error $\pm$ standard deviation for systolic and diastolic blood pressure reached 3.04 $\pm$ 3.24 mmHg and 2.57 $\pm$ 2.70 mmHg, respectively. Multi-dataset validation indicates that this paradigm surpasses benchmark methods in accuracy and stability while maintaining scalability.