DOI: 10.7280/d12q4p
title: HRV and salivary cortisol data in pregnant women

description: 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 to 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.
description: ECG signals and salivary cortisol were collected from pregnant
 women at 28–32 weeks gestation over 2 study visits in a cross-over study
 design. Visit 1 was the control, non-stress visit (15-minute friendly
 conversation), and visit 2 used the standardized 15-minute Trier Social
 Stress Test (TSST) protocol to induce temporary psychosocial stress. On
 both visits, saliva samples were collected at the following intervals:
 baseline, 15-min (before task), 30-min (after task), 45-min, 60-min,
 90-min, and 120-min. The ECG monitors (Actiheart) were placed before
 collecting the 15-min saliva sample and remained in place until after the
 last sample was collected. All visits occurred in the
 morning from approximately 8:30 am–11:30 am. Salivary cortisol values were
 adjusted for time of awakening by computing the time interval in minutes
 from reported wake time until time of arrival at the research setting,
 regressing each cortisol value against this time interval at a given
 visit, and saving the standardized values.  The
 Actiheart device provides Inter-beat Intervals (R-peak intervals)
 extracted from processed ECG signals. We used the 5-minute time windows of
 the Inter-beat Interval (IBI) signal to calculate the heart rate (HR,
 heartbeats per minute), time-domain parameters (i.e., RMSSD, AVNN, SDNN,
 and pNN50), and frequency-domain parameters (i.e., low frequency (LF),
 high frequency (HF), and LF/HF). The abnormal IBI and HRV values generated
 by motion artifacts were removed before proceeding with the analysis
 according to the removal criteria described in another study utilizing HRV
 measures (Cao et al., 2022, <em>J Med Internet Res</em>). The
 removal criteria are based on the normal range of HR and IBI
 values. 
description: Data provided in Excel.