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Joint symbolic dynamics identifies differences in the maternal-fetal cardiac coupling between nonlaboring and laboring women

Biomedical Signal Processing and Control, ISSN: 1746-8094, Vol: 68, Page: 102769
2021
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Article Description

There is a growing interest in investigating maternal-fetal cardiac coupling (MFCC) to identify the physiological processes involved in fetal development and well-being during pregnancy. No evidence of MFCC has been reported during active labor, a critical stage at the end of pregnancy for the mother-child binomial. Therefore, this exploratory study compares the short-term MFCC between nonlaboring and laboring women by applying nonlinear joint symbolic dynamics (JSD). Forty-four pairs of maternal-fetal RR interbeat intervals series were longitudinally analyzed during the third trimester of pregnancy (TT, 36.5 ±1.7 weeks of gestation) and later at active parturition at term (P, 39.4 ±1.2 weeks gestation), corresponding to low-risk pregnant women (N=22). For each pair of maternal-fetal interbeat intervals series at TT and P, a word distribution density matrix was calculated to assess general and partial coupling behavior. The JSD approach revealed changes in the MFCC, permitting the identification of differences between the TT and P conditions (p < 0.0001). The general indices revealed a significant increment of the short-term MFCC at P and the probability of similar (alike) and opposite cardiovascular fluctuations. Partial indices also confirmed coupling changes between both conditions. Our results show short-term MFCC increments at P, probably influenced by the effects of the intense uterine contractile activity and other interactions on both fetal and maternal heart rhythm fluctuations. We conjecture that JSD analysis of maternal and fetal heart rate during labor may be appropriate for monitoring physiological mechanisms involved in the mother-child binomial.

Bibliographic Details

Carla Andrea Tepichín-Castro; Claudia Ivette Ledesma-Ramírez; Miguel Ángel Peña-Castillo; Gustavo Pacheco-López; Juan Carlos Echeverría; José Javier Reyes-Lagos

Elsevier BV

Computer Science; Engineering; Medicine

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