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

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(+0)and 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.