  Arterial blood pressure (ABP) holds substantial promise for proactive
cardiovascular health management. Notwithstanding its potential, the invasive
nature of ABP measurements confines their utility primarily to clinical
environments, limiting their applicability for continuous monitoring beyond
medical facilities. The conversion of photoplethysmography (PPG) signals into
ABP equivalents has garnered significant attention due to its potential in
revolutionizing cardiovascular disease management. Recent strides in PPG-to-ABP
prediction encompass the integration of generative and discriminative models.
Despite these advances, the efficacy of these models is curtailed by the latent
space shift predicament, stemming from alterations in PPG data distribution
across disparate hardware and individuals, potentially leading to distorted ABP
waveforms. To tackle this problem, we present an innovative solution named the
Latent Space Constraint Transformer (LSCT), leveraging a quantized codebook to
yield robust latent spaces by employing multiple discretizing bases. To
facilitate improved reconstruction, the Correlation-boosted Attention Module
(CAM) is introduced to systematically query pertinent bases on a global scale.
Furthermore, to enhance expressive capacity, we propose the Multi-Spectrum
Enhancement Knowledge (MSEK), which fosters local information flow within the
channels of latent code and provides additional embedding for reconstruction.
Through comprehensive experimentation on both publicly available datasets and a
private downstream task dataset, the proposed approach demonstrates noteworthy
performance enhancements compared to existing methods. Extensive ablation
studies further substantiate the effectiveness of each introduced module.
