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 cortis

SUBMITTER: Cao R 

PROVIDER: S-EPMC9462678 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature