Key result
Deep neural networks using PPG-derived PRV classify mental stress with ~91% accuracy, beating multi-layer perceptrons.
Why the study?
Mental stress is an increasing cause of work-related illness that raises the risk of cardiovascular diseases, creating a need for non-obtrusive tools to detect it.
Can deep neural networks accurately classify mental stress levels using pulse rate variability from PPG signals?
Observational (n=15)
Can deep neural networks accurately classify mental stress levels using pulse rate variability from PPG signals?
Deep neural networks can accurately classify mental stress levels using non-obtrusive PPG signals, offering a potential tool for continuous stress monitoring.
Answers are grounded in this paper and cite their sources.
May enable wearable stress monitoring; leaves open prospective clinical validation before practice adoption.
Due to the pace of modern life and the shift of nature of work from physical to cognitive, mental stress is increasing in every profession. Mental stress has now become a leading cause of work-related illness. There are numerous sedentary occupations, such as those in the IT industry, where individuals are required to work for extended periods of time, leading to stress. Working for extended periods under mental stress can increase the risk of life-threatening diseases like cardiovascular diseases, mental health disorders etc. There is, thus, a requirement for a non-obtrusive tool to detect mental stress. In this work, pulse rate variability (PRV) of 15 subjects during 5 cognitive states (relaxation, deep breathing, and during three mental tasks involving three levels of mental stress) was examined using photoplethysmography (PPG). The result of this study indicates that 18 features (9-time domain and 9 frequency domain) are statistically significant at p < 0.05 as per the Friedman test in 5 cognitive states. The machine-learning algorithm based upon a multi-layer perceptron (MLP) was able to classify with an overall accuracy of 85.1±1.1%. Classification accuracy further improved by using deep neural networks (DNN) to 91±1.1%.
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Kalra et al. (2020) conducted an observational in Mental stress (n=15). Photoplethysmography (PPG) with Deep Neural Networks (DNN) vs. Multi-layer perceptron (MLP) was evaluated on Classification accuracy of cognitive states. Deep neural networks using photoplethysmography-derived pulse rate variability classified mental stress across 5 cognitive states with 91±1.1% accuracy, improving upon multi-layer perceptron.
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