Towards better reliability in fetal heart rate variability using time domain and spectral domain analyses. A new method for assessing fetal neurological state?

Abstract

Objectives Fetal heart rate variability (FHRV) has shown potential in fetal surveillance. Therefore, we aimed to evaluate the reliability of time domain and spectral domain parameters based on non-invasive fetal electrocardiography (NI-FECG). Method NI-FECG, with a sampling frequency of 1 kHz, was obtained in 75 healthy, singleton pregnant women between gestational age (GA) 20+0 to 41+0. The recording was divided into a) heart rate pattern (HRP) and b) periods fulfilling certain criteria of stationarity of RR-intervals, termed stationary heart rate pattern (SHRP). Within each recording, the first and the last time series from each HRP with less than 5% artifact correction were analyzed and compared. Standard deviation of normal-to-normal RR-intervals (SDNN), root mean square of successive differences (RMSSD), high frequency power (HF-power), low frequency power (LF-power), and LF-power/HF-power were performed. A multivariate mixed model was used and acceptable reliability was defined as intraclass correlation coefficient (ICC) ≥ 0.80 and a coefficient of variation (CV) ≤ 15%. Based on these results, the CV and ICC were computed if the average of two to six time series was used. Results For GA 28+0 to 34+6, SDNN and RMSSD exhibited acceptable reliability (CV < 15%; ICC > 90%), whereas GA 35+0 to 41+0and 20+0 to 27+6 showed higher CVs. Spectral domain parameters also showed high CVs However, by using the mean value of two to six time series, acceptable reliability in SDNN, RMSSD and HF-power from GA 28+0 was achieved. Stationarity of RR-intervals showed high influence on reliability and SHRP was superior to HRP, whereas the length of the time series showed minor influence. Conclusion Acceptable reliability seems achievable in SDNN, RMSSD and HF-power from gestational week 28. However, stationarity of RR-intervals should be considered when selecting time series for analyses.