Gaussian Modelling Characteristics of Peripheral Arterial Pulse: Difference between Measurements from the Three Trimesters of Healthy Pregnancy

Arterial pulse wave analysis has been attempted to monitor the maternal physiological changes of circulatory system during pregnancy. This study aimed to quantify the difference of Gaussian modelling characteristics derived from radial pulses measured from the three trimesters of healthy pregnant women. Radial pulses were recorded from seventy pregnant women between gestational week 11–13, week 20–22, and then week 37–39. They were then normalized and decomposed into three independent Gaussian waves for deriving four key modelling characteristic parameters: including the peak time interval (T) and peak amplitude ratio (R) between the first and second Gaussian waves (T(1,2) and R(1,2)), and their corresponding values between the first and third Gaussian waves (T(1,3) and R(1,3)). Post hoc multiple comparisons after analysis of variance was then applied to study the within-subject differences in Gaussian modelling characteristics between the three trimesters. The key results were that T(1,2) and T(1,3) increased significantly (T(1,2): 12.8 ± 1.3 vs 13.2 ± 1.3, p < 0.05; T(1,3): 39.5 ± 4.3 vs 45.4 ± 5.1, p < 0.001), and R(1,3) decreased significantly from the first to second trimester (0.60 ± 0.15 vs 0.53 ± 0.11, p < 0.001). From the second to third trimester, T(1,2) decreased significantly (13.2 ± 1.3 vs 12.8 ± 1.2, p < 0.01), and T(1,3) and R(1,3) decreased slightly but nonsignificantly. Since larger T(1,2) and T(1,3) and smaller R(1,3) are associated with more compliant peripheral arteries, our results indicated that peripheral arteries become more compliant from the first to second trimester and then have a tendency of returning to baseline during normal pregnancy. In conclusion, this study has quantitatively demonstrated significant changes of Gaussian modelling characteristics derived from radial pulses at the three trimesters of normal pregnant women, suggesting that these modelling characteristics could be used as parameters in monitoring maternal physiological changes during normal pregnancy.