Indian Institute of Technology, Department of Electrical Engineering, Kharagpur, 721302, West Bengal, India.
University of Maryland-Baltimore County, Department of Computer Science and Electrical Engineering, Baltimore, 14701, MD, USA.
Comput Biol Med. 2024 Nov;182:109109. doi: 10.1016/j.compbiomed.2024.109109. Epub 2024 Sep 10.
The cardiovascular system interacts continuously with the respiratory system to maintain the vital balance of oxygen and carbon dioxide in our body. The interplay between the sympathetic and parasympathetic branches of the autonomic nervous system regulates the aforesaid involuntary functions. This study analyzes the dynamics of the cardio-respiratory (CR) interactions using RR Intervals (RRI), Systolic Blood Pressure (SBP), and Respiration signals after first-order differencing to make them stationary. It investigates their variation with cognitive load induced by a virtual reality (VR) based Go-NoGo shooting task with low and high levels of task difficulty. We use Pearson's correlation-based linear and mutual information-based nonlinear measures of association to indicate the reduction in RRI-SBP and RRI-Respiration interactions with cognitive load. However, no linear correlation difference was observed in SBP-Respiration interactions with cognitive load, but their mutual information increased. A couple of open-loop autoregressive models with exogenous input (ARX) are estimated using RRI and SBP, and one closed-loop ARX model is estimated using RRI, SBP, and Respiration. The impulse responses (IRs) are derived for each input-output pair, and a reduction in the positive and negative peak amplitude of all the IRs is observed with cognitive load. Some novel parameters are derived by representing the IR as a double exponential curve with cosine modulation and show significant differences with cognitive load compared to other measures, especially for the IR between SBP and Respiration.
心血管系统与呼吸系统不断相互作用,以维持我们体内氧气和二氧化碳的重要平衡。自主神经系统的交感和副交感分支之间的相互作用调节上述无意识功能。本研究使用 RR 间隔(RRI)、收缩压(SBP)和呼吸信号分析了心肺(CR)相互作用的动力学,对它们进行一阶差分以使其平稳。它研究了它们在虚拟现实(VR)基于 Go-NoGo 射击任务中的变化,该任务具有低和高任务难度水平。我们使用基于 Pearson 相关的线性和基于互信息的非线性关联度量来表示与认知负荷相关的 RRI-SBP 和 RRI-Respiration 相互作用的减少。然而,在认知负荷下,SBP-Respiration 相互作用没有观察到线性相关差异,但它们的互信息增加了。使用 RRI 和 SBP 估计了几个具有外部输入的开环自回归模型(ARX),并使用 RRI、SBP 和 Respiration 估计了一个闭环 ARX 模型。为每个输入-输出对推导了脉冲响应(IR),并观察到随着认知负荷的增加,所有 IR 的正负峰值幅度都减小。通过将 IR 表示为具有余弦调制的双指数曲线来推导一些新的参数,与认知负荷相比,与其他参数相比,这些参数显示出显著差异,特别是对于 SBP 和 Respiration 之间的 IR。