Calderón-Juárez Martín, González Gómez Gertrudis Hortensia, Echeverría Juan C, Pérez-Grovas Héctor, Quintanar Eduardo, Lerma Claudia
Plan de Estudios Combinados en Medicina, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Departamento de Instrumentación Electromecánica, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City, Mexico.
Front Physiol. 2022 Feb 9;13:807250. doi: 10.3389/fphys.2022.807250. eCollection 2022.
Exploring the presence of nonlinearity through surrogate data testing provides insights into the nature of physical and biological systems like those obtained from heart rate variability (HRV). Short-term HRV time series are of great clinical interest to study autonomic impairments manifested in chronic diseases such as the end stage renal disease (ESRD) and the response of patients to treatment with hemodialysis (HD). In contrast to Iterative Amplitude Adjusted Fourier Transform (IAAFT), the Pinned Wavelet Iterative Amplitude Adjusted Fourier Transform (PWIAAFT) surrogates preserve nonstationary behavior in time series, a common characteristic of HRV. We aimed to test synthetic data and HRV time series for the existence of nonlinearity. Recurrence Quantitative Analysis (RQA) indices were used as discriminative statistics in IAAFT and PWIAAFT surrogates of linear stationary and nonstationary processes. HRV time series of healthy subjects and 29 ESRD patients before and after HD were tested in this setting during an active standing test. Contrary to PWIAAFT, linear nonstationary time series may be erroneously regarded as nonlinear according to the IAAFT surrogates. Here, a lower proportion of HRV time series was classified as nonlinear with PWIAAFT, compared to IAAFT, confirming that the nonstationarity condition influences the testing of nonlinear behavior in HRV. A contribution of nonlinearity was found in the HRV data of healthy individuals. A lower proportion of nonlinear time series was also found in ESRD patients, but statistical significance was not found. Although this proportion tends to be lower in ESRD patients, as much as 60% of time series proved to be nonlinear in healthy subjects. Given the important contribution of nonlinearity in HRV data, a nonlinear point of view is required to achieve a broader understanding of cardiovascular physiology.
通过替代数据测试探索非线性的存在，可为诸如心率变异性（HRV）所获得的物理和生物系统的本质提供见解。短期HRV时间序列对于研究慢性疾病（如终末期肾病（ESRD））中表现出的自主神经功能障碍以及患者对血液透析（HD）治疗的反应具有重要的临床意义。与迭代幅度调整傅里叶变换（IAAFT）不同，固定小波迭代幅度调整傅里叶变换（PWIAAFT）替代数据保留了时间序列中的非平稳行为，这是HRV的一个常见特征。我们旨在测试合成数据和HRV时间序列中非线性的存在情况。递归定量分析（RQA）指标被用作线性平稳和非平稳过程的IAAFT及PWIAAFT替代数据的判别统计量。在主动站立测试期间，对健康受试者以及29名ESRD患者HD前后的HRV时间序列进行了测试。与PWIAAFT相反，根据IAAFT替代数据，线性非平稳时间序列可能会被错误地视为非线性。在此，与IAAFT相比，PWIAAFT将HRV时间序列分类为非线性的比例更低，这证实了非平稳条件会影响HRV中非线性行为检测。在健康个体的HRV数据中发现了非线性的作用。在ESRD患者中也发现非线性时间序列的比例较低，但未发现统计学意义。尽管ESRD患者中的这一比例往往较低，但在健康受试者中高达60%的时间序列被证明是非线性的。鉴于非线性在HRV数据中的重要作用，需要从非线性角度来更全面地理解心血管生理学。
