Cui Jiajia, Huang Zhipei, Wu Jiankang, Jiang Hong
Sensor Networks and Application Research Center, School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China.
CAS Institute of Healthcare Technologies, Nanjing, China.
Front Physiol. 2020 Aug 5;11:867. doi: 10.3389/fphys.2020.00867. eCollection 2020.
Respiratory sinus arrhythmia (RSA) represents a physiological phenomenon of cardiopulmonary interaction. It is known as a measure of efficiency of the circulation system, as well as a biomarker of cardiac vagal and well-being. In this article, RSA is modeled as modulation of heart rate by respiration in an interactive cardiopulmonary system with the most effective system state of resonance. By mathematically modeling of this modulation, we propose a quantitative measurement for RSA referred to as "Cardiopulmonary Resonance Function (CRF) and Cardiopulmonary Resonance Indices (CRI)," which are derived by disentanglement of the RR-intervals series into respiratory-modulation component, R-HRV, and the rest, NR-HRV using spectral G-causality. Evaluation of CRI performance in quantifying RSA has been conducted in the scenarios of paced breathing and in the different sleep stages. The preliminary experimental results have shown superior representation ability of CRF and CRI compared to Heart Rate Variability (HRV) and Cardiopulmonary Coupling index (CPC).
呼吸性窦性心律不齐(RSA)是一种心肺相互作用的生理现象。它被认为是循环系统效率的一种度量,也是心脏迷走神经功能和健康状况的生物标志物。在本文中,RSA被建模为在具有最有效共振系统状态的交互式心肺系统中,呼吸对心率的调制。通过对这种调制进行数学建模,我们提出了一种针对RSA的定量测量方法,称为“心肺共振功能(CRF)和心肺共振指数(CRI)”,它们是通过使用频谱G因果关系将RR间期序列分解为呼吸调制成分R-HRV和其余部分NR-HRV而得出的。已经在定频呼吸场景和不同睡眠阶段对CRI量化RSA的性能进行了评估。初步实验结果表明,与心率变异性(HRV)和心肺耦合指数(CPC)相比,CRF和CRI具有更好的表征能力。