Machine Learning models for the Cognitive Stress Detection Using Heart Rate Variability Signals

Abstract

Cognitive domains play a critical role in daily functioning. The prediction of cognitive stress state is important to better monitor work performance. This study aims to explore machine learning models to detect cognitive load or state using heart rate variability (HRV) signals. HRV data were recorded from thirty subjects during rest, two cognitive tasks (d2 Attention and Featuring Switcher task), and recovery. Seven HRV indexes from both time and frequency domains, extracted from raw R-R intervals, were used to identify whether subjects performed cognitive tasks or not. Five classifier models: linear support vector machine (LSVM), kernel SVM radial basis function, k-nearest neighbor (KNN), and random forest (RF), were trained and evaluated using a leave-one-out cross-validation approach. The accuracies and F1-score range from 0.54 to 0.62, with LSVM, showing the best. These acceptable performances indicate the machine learning approach could be used to further distinguish between rest and cognitive state. With the ubiquity of non-invasive and low-cost wearable devices, this finding offers insight to be incorporated into personal work performance monitoring in the digital age.

Impedance cardiogram based exploration of cardiac mechanisms in post‐traumatic stress disorder during trauma recall

“…It is useful when the data are not independent, which may be the case with the twin pairs' ICGs in our data set. However, this model performs a linear analysis, whereas researchers are also applying non‐linear methods on heart rate variability signals for mental stress assessment (Izzah et al., 2022; Lee et al., 2022).…”