Key result
Machine learning using HRV distinguishes cognitive stress from rest with ~62% peak accuracy.
Why the study?
Predicting cognitive stress states is important to better monitor work performance, prompting exploration of machine learning models to detect cognitive load using heart rate variability signals.
Can machine learning models using heart rate variability signals accurately detect cognitive stress states?
Observational (n=30)
Can machine learning models using heart rate variability signals accurately detect cognitive stress states?
Machine learning models using HRV signals demonstrate acceptable performance in distinguishing between rest and cognitive stress states, offering potential for non-invasive personal work performance monitoring.
May support non-invasive cognitive stress monitoring; hypothesis-generating and requires larger validation before clinical adoption.
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.
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Izzah et al. (2022) conducted an observational in Cognitive stress (n=30). Machine learning models using HRV signals was evaluated on Detection of cognitive stress state (accuracy and F1-score). Machine learning models using heart rate variability signals distinguished between rest and cognitive states with accuracies and F1-scores ranging from 0.54 to 0.62, with LSVM performing best.
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