Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 320, Taiwan.
Department of Geriatrics, Tainan Hospital, Ministry of Health and Welfare, Tainan 700, Taiwan.
Chaos. 2020 Mar;30(3):033118. doi: 10.1063/1.5134868.
Quantifying respiratory sinus arrhythmia (RSA) can provide an index of parasympathetic function. Fourier spectral analysis, the most widely used approach, estimates the power of the heart rate variability in the frequency band of breathing. However, it neglects the time-varying characteristics of the transitions as well as the nonlinear properties of the cardio-respiratory coupling. Here, we propose a novel approach based on Hilbert-Huang transform, called the multimodal coupling analysis (MMCA) method, to assess cardio-respiratory dynamics by examining the instantaneous nonlinear phase interactions between two interconnected signals (i.e., heart rate and respiration) and compare with the counterparts derived from the wavelet-based method. We used an online database. The corresponding RSA components of the 90-min ECG and respiratory signals of 20 young and 20 elderly healthy subjects were extracted and quantified. A cycle-based analysis and a synchro-squeezed wavelet transform were also introduced to assess the amplitude or phase changes of each respiratory cycle. Our results demonstrated that the diminished mean and standard deviation of the derived dynamical RSA activities can better discriminate between elderly and young subjects. Moreover, the degree of nonlinearity of the cycle-by-cycle RSA waveform derived from the differences between the instantaneous frequency and the mean frequency of each respiratory cycle was significantly decreased in the elderly subjects by the MMCA method. The MMCA method in combination with the cycle-based analysis can potentially be a useful tool to depict the aging changes of the parasympathetic function as well as the waveform nonlinearity of RSA compared to the Fourier-based high-frequency power and the wavelet-based method.
量化呼吸窦性心律失常（RSA）可以提供副交感神经功能的指标。傅里叶谱分析是最广泛使用的方法，它估计心率变异性在呼吸频段的功率。然而，它忽略了过渡的时变特征以及心脏呼吸耦合的非线性特性。在这里，我们提出了一种基于希尔伯特-黄变换的新方法，称为多模态耦合分析（MMCA）方法，通过检查两个互联信号（即心率和呼吸）之间的瞬时非线性相位相互作用来评估心脏呼吸动力学，并与基于小波的方法得出的对应物进行比较。我们使用了一个在线数据库。提取并量化了 20 名年轻和 20 名老年健康受试者的 90 分钟 ECG 和呼吸信号的相应 RSA 分量。还介绍了基于循环的分析和同步挤压小波变换，以评估每个呼吸周期的幅度或相位变化。我们的结果表明，衍生动态 RSA 活动的均值和标准差的降低可以更好地区分老年和年轻受试者。此外，通过 MMCA 方法，老年受试者每个呼吸周期的瞬时频率和平均频率之间差异衍生的 RSA 循环对循环的非线性程度显著降低。与基于傅里叶的高频功率和基于小波的方法相比，MMCA 方法与基于循环的分析相结合可能是一种有用的工具，可以描绘副交感神经功能的老化变化以及 RSA 的波形非线性。
