Relationship between Deceleration Morphology and Phase Rectified Signal Averaging-based Parameters during Labor

Abstract

During labor, uterine contractions trigger the response of the autonomic nervous system (ANS) of the fetus, producing sawtooth-like decelerations in the fetal heart rate (FHR) series. Under chronic hypoxia, ANS is known to regulate FHR differently with respect to healthy fetuses. In this study, we hypothesized that such different ANS regulation might also lead to a change in the FHR deceleration morphology. The hypothesis was tested in an animal model comprising 7 normoxic and 5 chronically hypoxic fetuses that underwent a protocol of umbilical cord occlusions (UCOs). Deceleration morphologies in the fetal inter-beat time interval (FRR) series were modeled using a trapezoid with four parameters, i.e., baseline b, deceleration depth a, UCO response time τ_u and recovery time τ_r. Comparing normoxic and hypoxic sheep, we found a clear difference for τ_u (24.8 ± 9.4 vs 39.8 ± 9.7 s; p < 0.05), a (268.1 ± 109.5 vs 373.0 ± 46.0 ms; p < 0.1) and Δτ = τ_u − τ_r (13.2 ± 6.9 vs 23.9 ± 7.5 s; p < 0.05). Therefore, the animal model supported the hypothesis that hypoxic fetuses have a longer response time τ_u and larger asymmetry Δτ as a response to UCOs. Assessing these morphological parameters during labor is challenging due to non-stationarity, phase desynchronization and noise. For this reason, in the second part of the study, we quantified whether acceleration capacity (AC), deceleration capacity (DC), and deceleration reserve (DR), computed through Phase-Rectified Signal Averaging (PRSA, known to be robust to noise), were correlated with the morphological parameters. DR and DC correlated with Δτ and τ_u for a wide range of the PRSA parameter T (max Pearson’s correlation ρ = 0.9, p < 0.05, and ρ = 0.6, p < 0.1, respectively). In conclusion, deceleration morphologies have been found to differ between normoxic and hypoxic sheep fetuses during UCOs. The same difference can be assessed through PRSA based parameters, further motivating future investigations on the translational potential of this methodology on human data.