A Novel Pulmonary Artery Pressure Waveform Reconstruction Framework Based on Wearable Seismocardiogram

Pulmonary hypertension (PH) is a heterogeneous and highly morbid disease, diagnosed with mean pulmonary artery pressure (PAP) measured by invasive right heart catheterization (RHC). However, RHC risks complications and cannot provide continuous PAP monitoring over long durations. Noninvasive assessment of PAP has gained interest for its low risk and convenience compared with invasive measurement. Recently, seismocardiogram (SCG), which unobtrusively records chest vibrations from cardiovascular motions, has demonstrated feasibility for PAP estimation. In this article, we propose an attentive conditional adversarial generative network (CGAN) framework for the purpose of continuous PAP assessment using SCG. This is the first time that the PAP waveforms morphology has been accurately reconstructed from SCG. The proposed framework involves a U-net generator with a soft attention module to focus on salient SCG features, along with a PatchGAN discriminator for localized assessment. It uses Wasserstein distance in the loss function to improve training stability, which is compared with the existing generative models using the same SCG-RHC dataset. Results show that our model yields superior PAP waveform reconstruction performance with a Pearson correlation coefficient (PCC) of 0.982. The reconstructed waveforms exhibit high correlation with invasive RHC measurement, with quantitative statistics and physiological indices validating strong agreement. This work represents a novel approach for continuous, noninvasive PAP estimation, offering valuable improvements in the management of PH.