Our research goal is to improve the intrapartum identification of tracings associated with severe acidosis at birth and subsequent hypoxic-ischemic encephalopathy so that timely interventions could avoid such complications without causing excessive unnecessary interventions in births with normal outcomes. The present study examines the evolution of fetal heart rate (FHR) features over the course of labor. We analyzed FHR signals collected in the last 6 hours before delivery in 21,853 births with normal neonatal outcomes and in 163 that developed hypoxic-ischemic encephalopathy (HIE) from 15 hospitals of Kaiser Permanente Northern California. We divided these six hours into 18 nonoverlapping 20-minute epochs. The power spectral density of each epoch was divided into three bands: low frequency (LF, 30–150 mHz), movement frequency (MF, 150–500 mHz), and high frequency (HF, 500–1000 mHz). We also estimated the LF/(MF+HF) ratio, the mean and standard deviation of the FHR signal, the approximate entropy (ApEn), and the deceleration capacity (DC). In our results, ApEn, the standard deviation, and DC showed a promising ability to detect risk of HIE as early as 120 minutes before birth, which gives enough leading time for timely interventions.

Visual assessment of the evolution of fetal heart rate (FHR) and uterine pressure (UP) patterns is the standard of care in the intrapartum period. Unfortunately, this assessment has high levels of intra-and inter-observer variability. This study processed and analyzed FHR and UP patterns using computerized pattern recognition tools. The goal was to evaluate differences in FHR and UP patterns between fetuses with normal outcomes and those who developed hypoxic-ischemic encephalopathy (HIE). For this purpose, we modeled the sequence of FHR patterns and uterine contractions using Multi-Chain Semi-Markov models (MCSMMs). These models estimate the probability of transitioning between FHR or UP patterns and the dwell time of each pattern. Our results showed that in comparison to the control group, the HIE group had: (1) more frequent uterine contractions during the last 12 hours before birth; (2) more frequent FHR decelerations during the last 12 hours before birth; (3) longer decelerations during the last eight hours before birth; and (4) shorter baseline durations during the last five hours before birth. These results demonstrate that the fetuses in the HIE group were subject to a more stressful environment than those in the normal group.Clinical Relevance-Our results revealed statistically significant differences in FHR/UP patterns between the normal and HIE groups in the hours before birth. This indicates that features derived using MCSMMs may be useful in a machine learning framework to detect infants at increased risk of developing HIE, allowing preventive interventions.

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.