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Proceedings Paper

Heart rate variability (HRV): an indicator of stress
Author(s): Balvinder Kaur; Joseph J. Durek; Barbara L. O'Kane; Nhien Tran; Sophia Moses; Megha Luthra; Vasiliki N. Ikonomidou
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Paper Abstract

Heart rate variability (HRV) can be an important indicator of several conditions that affect the autonomic nervous system, including traumatic brain injury, post-traumatic stress disorder and peripheral neuropathy [3], [4], [10] & [11]. Recent work has shown that some of the HRV features can potentially be used for distinguishing a subject’s normal mental state from a stressed one [4], [13] & [14]. In all of these past works, although processing is done in both frequency and time domains, few classification algorithms have been explored for classifying normal from stressed RRintervals. In this paper we used 30 s intervals from the Electrocardiogram (ECG) time series collected during normal and stressed conditions, produced by means of a modified version of the Trier social stress test, to compute HRV-driven features and subsequently applied a set of classification algorithms to distinguish stressed from normal conditions. To classify RR-intervals, we explored classification algorithms that are commonly used for medical applications, namely 1) logistic regression (LR) [16] and 2) linear discriminant analysis (LDA) [6]. Classification performance for various levels of stress over the entire test was quantified using precision, accuracy, sensitivity and specificity measures. Results from both classifiers were then compared to find an optimal classifier and HRV features for stress detection. This work, performed under an IRB-approved protocol, not only provides a method for developing models and classifiers based on human data, but also provides a foundation for a stress indicator tool based on HRV. Further, these classification tools will not only benefit many civilian applications for detecting stress, but also security and military applications for screening such as: border patrol, stress detection for deception [3],[17], and wounded-warrior triage [12].

Paper Details

Date Published: 22 May 2014
PDF: 8 pages
Proc. SPIE 9118, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII, 91180V (22 May 2014); doi: 10.1117/12.2051148
Show Author Affiliations
Balvinder Kaur, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
Joseph J. Durek, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
Barbara L. O'Kane, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
Nhien Tran, George Mason Univ. (United States)
Sophia Moses, George Mason Univ. (United States)
Megha Luthra, George Mason Univ. (United States)
Vasiliki N. Ikonomidou, George Mason Univ. (United States)


Published in SPIE Proceedings Vol. 9118:
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII
Harold H. Szu; Liyi Dai, Editor(s)

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