Prenatal stress assessment using heart rate variability and salivary cortisol: A machine learning-based approach

Abstract

Objective To develop a machine learning algorithm utilizing heart rate variability (HRV) and salivary cortisol to detect the presence of acute stress among pregnant women that may be applied to future clinical research. Methods ECG signals and salivary cortisol were analyzed from 29 pregnant women as part of a crossover study involving a standardized acute psychological stress exposure and a control non-stress condition. A filter-based features selection method was used to identify the importance of different features [heart rate (HR), time- and frequency-domain HRV parameters and salivary cortisol] for stress assessment and reduce the computational complexity. Five machine learning algorithms were implemented to assess the presence of stress with and without salivary cortisol values. Results On graphical visualization, an obvious difference in heart rate (HR), HRV parameters and cortisol were evident among 17 participants between the two visits, which helped the stress assessment model to distinguish between stress and non-stress exposures with greater accuracy. Eight participants did not display a clear difference in HR and HRV parameters but displayed a large increase in cortisol following stress compared to the non-stress conditions. The remaining four participants did not demonstrate an obvious difference in any feature. Six out of nine features emerged from the feature selection method: cortisol, three time-domain HRV parameters, and two frequency-domain parameters. Cortisol was the strongest contributing feature, increasing the assessment accuracy by 10.3% on average across all five classifiers. The highest assessment accuracy achieved was 92.3%, and the highest average assessment accuracy was 76.5%. Conclusion Salivary cortisol contributed a significant increase in accuracy of the assessment model compared to using a range of HRV parameters alone. Our machine learning model demonstrates acceptable accuracy in detection of acute stress among pregnant women when combining salivary cortisol with HR and HRV parameters.

Phenotyping the autonomic nervous system in pregnancy using remote sensors: potential for complication prediction

“…Understanding HRV seems to be crucial since, 1) as discussed earlier, it has potential to be predicted and, thus, can be adjusted for better outcomes, 2) more importantly, it can predict common pregnancy complications. For instance, there are a few studies that showed HRV can predict maternal mental stress and emotional states during pregnancy with high accuracy (Cao et al, 2022;Li et al, 2022). In addition to these studies that were concerned with the emotional and mental outcome of HRV, there are studies that applied HRV classification for physical pregnancy-induced complications such as hypertensive disorders.…”