Abstract
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.
Highlights
Continuous electronic fetal monitoring (EFM) is a standard of care during the antepartum and intrapartum periods (American College of Obstetricians and Gynecologists, 2014)
Confidence intervals of the power spectral density (PSD) and approximate entropy (ApEn) features reported by Gonçalves et al to those estimated from our simulated sequences
Our results indicate that 1) our simulated FHRRRI sequences have fetal heart rate variability (fHRV) features with distributions similar to those of real data, 2) the estimation of high frequency power (HF) power and ApEn are the most affected by the AC method and additive noise, and 3) the loss of discriminability due to the AC method is largest for the ApEn and smallest for the low frequency power (LF) power and LF/(MF + HF) ratio
Summary
Continuous electronic fetal monitoring (EFM) is a standard of care during the antepartum and intrapartum periods (American College of Obstetricians and Gynecologists, 2014). EFM involves measuring two signals: fetal heart rate (FHR) and uterine pressure (UP). These two signals make up what is known as cardiotocography (CTG). Uterine pressure is commonly acquired using external sensors that measure the tension in the maternal abdominal wall (Smyth, 1957). There are other acquisition methods: fetal scalp electrocardiography (ECG) for FHR; and intrauterine probes for uterine pressure (Ayres-De-Campos and Nogueira-Reis, 2016). These methods are invasive and are typically used only when external monitoring is not possible
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