发表机构
Communication Science Laboratories, NTT Corporation（NTT 株式会社 通信科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过仿真推断评估心冲击图（BCG）对心血管生物标志物估计的补充价值，发现添加BCG显著提升估计性能，并能在病态情况下将多峰后验变为单峰，为信号选择提供依据。
AI 中文摘要
随着人口老龄化，心血管疾病患者数量持续增加，凸显了在进展为严重且不可逆的功能衰退之前进行早期检测的必要性。因此，从非侵入性生物信号（如光电容积脉搏波（PPG）和动脉压力波（APW）信号）估计心血管生物标志物已引起越来越多的关注。这些信号可通过可穿戴设备和袖带式设备测量。以往研究已使用PPG和APW信号来估计心血管生物标志物。然而，这些信号在时域和频域上表现出很强的相似性，且主要反映外周和动脉脉搏波形。因此，它们可能提供关于心脏机械功能的信息有限。相比之下，额外生物信号（如反映心脏射血引起身体微小机械反应的心冲击图（BCG））的定量影响仍不清楚。在本研究中，我们从统一的全身体心血管循环模型生成了合成PPG、APW和BCG信号，并评估了BCG对概率性心血管生物标志物估计的补充贡献。我们使用神经后验估计和基于仿真的推断来估计心血管生物标志物的后验分布。结果表明，添加BCG信号显著提高了估计性能。此外，即使在PPG和APW单独产生多峰后验分布的病态情况下，添加BCG也产生了单峰后验分布。这些发现为估计心血管动力学时的信号选择提供了基础性见解。
英文摘要
As the population ages, the number of patients with cardiovascular diseases continues to increase, highlighting the need for early detection before progression to severe and irreversible functional decline. Consequently, estimating cardiovascular biomarkers from non-invasive biosignals, such as photoplethysmography (PPG) and arterial pressure wave (APW) signals, has attracted increasing attention. These signals can be measured using wearable and cuff-type devices. Previous studies have used PPG and APW signals to estimate cardiovascular biomarkers. However, these signals exhibit strong similarities in both the temporal and frequency domains and primarily reflect peripheral and arterial pulse waveforms. Therefore, they may provide limited information about cardiac mechanical function. In contrast, the quantitative impact of additional biosignals, such as ballistocardiography (BCG), which reflect the body's minute mechanical responses to cardiac ejection, remains unclear. In this study, we generated synthetic PPG, APW, and BCG signals from a unified whole-body cardiovascular circulation model and evaluated the complementary contribution of BCG to probabilistic cardiovascular biomarker estimation. We estimated posterior distributions of cardiovascular biomarkers using neural posterior estimation and simulation-based inference. The results showed that adding BCG signals significantly improved estimation performance. Furthermore, even in ill-posed cases where PPG and APW alone produced multimodal posterior distributions, adding BCG yielded unimodal posterior distributions. These findings provide fundamental insights into signal selection for estimating cardiovascular dynamics.
Comments8 pages, 5 figures, 2 tables
