Analysis of non-stationary heart rate series by evolutionary periodogram

Niccolai, M.; Varanini, M.; Macerata, A.; Pola, S.; Emdin, M.; Cipriani, M.; Marchesi, Carlo

doi:10.1109/cic.1995.482682

Cited by 4 publications

(2 citation statements)

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“…This method facilitates nonlinear testing as the a priori demonstration of stationarity is not strictly needed. This condition is rarely identified in HRV data ( Niccolai et al, 1995 ; Braun et al, 1998 ; Porta et al, 2004 ; Gao et al, 2013 ), particularly if these data are obtained from healthy subjects studied during daily or ambulatory conditions. Our findings show that even in controlled scenarios, most of healthy subjects and ESRD patients exhibit nonstationary behavior.…”

Section: Discussionmentioning

confidence: 99%

Recurrence Quantitative Analysis of Wavelet-Based Surrogate Data for Nonlinearity Testing in Heart Rate Variability

et al. 2022

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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.

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