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 , ,  & .
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 ,  & . 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)  and 2) linear discriminant analysis (LDA) . 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 ,, and wounded-warrior triage .