Abstract
This work presents our findings to map salivary cortisol measurements to electrocardiogram (ECG) features to create a physiological stress identification system. An experiment modelled on the Trier Social Stress Test (TSST) was used to simulate stress and control conditions, whereby salivary measurements and ECG measurements were obtained from student volunteers. The salivary measurements of stress biomarkers were used as objective stress measures to assign a three-class labelling (Low-Medium-High stress) to the extracted ECG features. The labelled features were then used for training and classification using a genetic-ordered ARTMAP with probabilistic voting for analysis on the efficacy of the ECG features used for physiological stress recognition. The ECG features include time-domain features of the heart rate variability and the ECG signal, and frequency-domain analysis of specific frequency bands related to the autonomic nervous activity. The resulting classification method scored approximately 60-69% success rate for predicting the three stress classes.
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Loo, C.K. et al. (2012). Genetic-Optimized Classifier Ensemble for Cortisol Salivary Measurement Mapping to Electrocardiogram Features for Stress Evaluation. In: Anthony, P., Ishizuka, M., Lukose, D. (eds) PRICAI 2012: Trends in Artificial Intelligence. PRICAI 2012. Lecture Notes in Computer Science(), vol 7458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32695-0_26
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DOI: https://doi.org/10.1007/978-3-642-32695-0_26
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