Key result
Low frequency power minimizes discriminability loss to ~11% among fetal HRV features.
Why the study?
Most fetal heart rate data are acquired using Doppler ultrasound, which uses autocorrelation that may cause loss of high frequency information affecting estimation bias and discriminability of FHR variability features.
Frequency domain features of fetal heart rate variability, particularly low frequency power, are more robust to Doppler ultrasound autocorrelation methods and additive noise than approximate entropy.
DUS autocorrelation reduces FHRV discriminability, especially ApEn; leaves open whether direct ECG signals improve ML-based fetal pathology detection.
Continuous electronic fetal monitoring and the access to databases of fetal heart rate (FHR) data have sparked the application of machine learning classifiers to identify fetal pathologies. However, most fetal heart rate data are acquired using Doppler ultrasound (DUS). DUS signals use autocorrelation (AC) to estimate the average heartbeat period within a window. In consequence, DUS FHR signals loses high frequency information to an extent that depends on the length of the AC window. We examined the effect of this on the estimation bias and discriminability of frequency domain features: low frequency power (LF: 0.03-0.15 Hz), movement frequency power (MF: 0.15-0.5 Hz), high frequency power (HF: 0.5-1 Hz), the LF/(MF + HF) ratio, and the nonlinear approximate entropy (ApEn) as a function of AC window length and signal to noise ratio. We found that the average discriminability loss across all evaluated AC window lengths and SNRs was 10.99% for LF 14.23% for MF, 13.33% for the HF, 10.39% for the LF/(MF + HF) ratio, and 24.17% for ApEn. This indicates that the frequency domain features are more robust to the AC method and additive noise than the ApEn. This is likely because additive noise increases the irregularity of the signals, which results in an overestimation of ApEn. In conclusion, our study found that the LF features are the most robust to the effects of the AC method and noise. Future studies should investigate the effect of other variables such as signal drop, gestational age, and the length of the analysis window on the estimation of fHRV features and their discriminability.
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Vargas-Calixto et al. (2021) studied Fetal distress (acidosis) (n=80). Autocorrelation window length and signal-to-noise ratio in Doppler ultrasound vs. RR interval-derived fetal heart rate (FHR_RRI) was evaluated on Discriminability loss (DAUC) of fetal heart rate variability features. The average discriminability loss across evaluated autocorrelation window lengths and signal-to-noise ratios was lowest for low frequency power (10.99%) and highest for approximate entropy (24.17%).
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