Classifying Stress From Heart Rate Variability Using Salivary Biomarkers as Reference

An accurate and noninvasive stress assessment from human physiology is a strenuous task. In this paper, a pattern recognition system to learn complex correlates between heart rate variability (HRV) features and salivary stress biomarkers is proposed. Using the Trier social stress test, heart rate an...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2016-10, Vol.27 (10), p.2035-2046
Main Authors: Wei Shiung Liew, Seera, Manjeevan, Chu Kiong Loo, Einly Lim, Kubota, Naoyuki
Format: Article
Language:English
Subjects:
Alpha-amylase
Biological cells
Biomarkers
cortisol
Exercise Test
fuzzy ARTMAP (FAM)
Genetic algorithms
genetic optimization
Heart Rate
Heart rate variability
heart rate variability (HRV)
Hormones
Humans
Hydrocortisone
negative correlation (NC)
Neural Networks (Computer)
Pattern recognition systems
probabilistic voting
Saliva
Sociology
Statistics
Stress
Training
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Summary:An accurate and noninvasive stress assessment from human physiology is a strenuous task. In this paper, a pattern recognition system to learn complex correlates between heart rate variability (HRV) features and salivary stress biomarkers is proposed. Using the Trier social stress test, heart rate and salivary measurements were obtained from volunteers under varying levels of stress induction. Measurements of salivary alpha-amylase and cortisol were used as objective measures of stress, and were correlated with the HRV features using fuzzy ARTMAP (FAM). In improving the predictive ability of the ARTMAPs, techniques, such as genetic algorithms for parameter optimization and voting ensembles, were employed. The ensemble of FAMs can be used for predicting stress responses of salivary alpha-amylase or cortisol using heart rate measurements as the input. Using alpha-amylase as the stress indicator, the ensemble was able to classify stress from heart rate features with 75% accuracy, and 80% accuracy when cortisol was used.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2015.2468721