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.

中文翻译：

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通过多模式耦合评估探索年龄相关的心脏呼吸动力学变化

量化呼吸窦性心律不齐（RSA）可提供副交感神经功能的指标。傅立叶频谱分析是最广泛使用的方法，可估计呼吸频带中心率变化的功效。但是，它忽略了跃迁的时变特性以及心肺耦合的非线性特性。在这里，我们提出一种基于Hilbert-Huang变换的新颖方法，称为多峰耦合分析（MMCA）方法，通过检查两个相互关联的信号（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。与从基于小波的方法得出的对应结果进行比较。我们使用了在线数据库。提取并量化90分钟ECG的相应RSA成分和20位年轻和20位老年健康受试者的呼吸信号。还引入了基于周期的分析和同步压缩的小波变换来评估每个呼吸周期的幅度或相位变化。我们的结果表明，派生的动态RSA活动的均值和标准差的减少可以更好地区分老年和青年受试者。而且，通过MMCA方法，在老年受试者中，由每个呼吸周期的瞬时频率和平均频率之间的差异得出的逐周期RSA波形的非线性程度显着降低。