Insufficient sampling frequencies skew heart rate variability estimates: Implications for extracting heart rate metrics from neuroimaging and physiological data

Abstract

BackgroundWhile cardiac pulsations are widely present within physiological and neuroimaging data, it is unknown the extent this information can provide valid and reliable heart rate and heart rate variability (HRV) estimates. The objective of this study was to demonstrate how a slight temporal shift due to an insufficient sampling frequency can impact the validity/accuracy of deriving cardiac metrics. MethodsTwenty-two participants were instrumented with valid/reliable industry-standard or open-source electrocardiograms. Five-minute lead II recordings were collected at 1000 Hz in an upright orthostatic position. Following artifact removal, the 1000 Hz recording for each participant was downsampled to frequencies ranging 2–500 Hz. The validity of each participant’s downsampled recording was compared against their 1000 Hz recording (“reference-standard”) using Bland-Altman plots with 95 % limits of agreement (LOA), coefficient of variation (CoV), intraclass correlation coefficients, and adjusted r-squared values. ResultsDownsampled frequencies of ≥ 50 and ≥ 90 Hz produced highly robust measures with narrow log-transformed 95 % LOA (<±0.01) and low CoV values (≤3.5 %) for heart rate and HRV metrics, respectively. Below these thresholds, the log-transformed 95 % LOA became wider (LOA range: ±0.1–1.9) and more variable (CoV range: 1.5–111.6 %). ConclusionThese results provide an important consideration for obtaining cardiac information from physiological data. Compared to the “reference-standard” ECG, a seemingly negligible temporal shift of the systolic contraction (R wave) greater than 11-milliseconds (90 Hz) away from its true value, lessened the validity of the HRV. Further research is warranted to determine the minimum sampling frequency required to obtain valid heart rate/HRV metrics from pulsatile waveforms.