Machine learning advances the classification and prediction of responding from psychophysiological reactions

Abstract

Psychological responding to stressful situations such as experiential avoidance (EA) is central to the development of several psychopathologic conditions, yet it tends to be assessed solely via retrospective self-report. The purpose of this study is to utilize machine-learning algorithms to delineate and predict individuals classified as high vs. low in EA across stress-inducing situations based on their psychophysiological signals. Three samples (total N = 212) arising from previous research included 43 smokers, 86 females at high-risk for eating disorders, and 83 anxious individuals. Each of the studies involved a stress induction laboratory experiment. Supervised algorithms were used to compare the ability of physiological signals (skin conductance (SCR), heart rate variability (HRV), and facial electromyography (fEMG)) in classifying both individuals presenting with high vs. low EA (using common self-report measures) and situations. Overall accuracy of the Rectangular Window Methodology (RWM) model was 77.8%, with prediction of 88.9% accuracy of participants being high in EA. The features of SCR, fEMG and HRV accurately and stably classified individuals characterized by dispositional high EA as they navigated stressful situations. This study is the first to use machine learning to delineate and predict high vs. low EA in individuals and situations from psychophysiological signals. These parameters can be used for future real-time assessments and predictions.