Key result
Short-time Fourier Transform and autoregressive modeling methods yielded significantly different results for heart rate variability parameters, demonstrating that the two methods cannot be considered interchangeable.
Why the study?
Short-time Fourier Transform and time-varying autoregressive modeling are commonly used for time-varying heart rate variability spectral analysis, but their comparability and agreement during nonstationary events required evaluation.
p-value: p=≤0.014
In a rodent model of cardiovascular disease, parametric (AR) and nonparametric (STFT) methods for time-frequency heart rate variability analysis yielded significantly different results and cannot be considered interchangeable.
Caution advised comparing HRV across methods in rodent models; leaves open optimal technique for autonomic research.
The aim of time-varying heart rate variability spectral analysis is to detect and quantify changes in the heart rate variability spectrum components during nonstationary events. Of the methods available, the nonparametric short-time Fourier Transform and parametric time-varying autoregressive modeling are the most commonly employed. The current study (1) compares short-time Fourier Transform and autoregressive modeling methods influence on heart rate variability spectral characteristics over time and during an experimental ozone exposure in mature adult spontaneously hypertensive rats, (2) evaluates the agreement between short-time Fourier Transform and autoregressive modeling method results, and (3) describes the advantages and disadvantages of each method. Although similar trends were detected during ozone exposure, statistical comparisons identified significant differences between short-time Fourier Transform and autoregressive modeling analysis results. Significant differences were observed between methods for LF power (p ≤ 0.014); HF power (p ≤ 0.011); total power (p ≤ 0.027); and normalized HF power (p = 0.05). Furthermore, inconsistencies between exposure-related observations accentuated the lack of agreement between short-time Fourier Transform and autoregressive modeling overall. Thus, the short-time Fourier Transform and autoregressive modeling methods for time-varying heart rate variability analysis could not be considered interchangeable for evaluations with or without interventions that are known to affect cardio-autonomic activity.
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Wong et al. (2020) studied Cardiovascular disease (rodent model) (n=11). Autoregressive (AR) modeling vs. Short-time Fourier Transform (STFT) was evaluated on Differences between STFT and AR methods for HRV spectral characteristics (LF power, HF power, total power, normalized HF power) (p=≤0.014). Short-time Fourier Transform and autoregressive modeling methods yielded significantly different results for heart rate variability parameters, demonstrating that the two methods cannot be considered interchangeable.
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