Heart rate variability (HRV): an indicator of stress

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].

Recurrence Quantification Analysis of Heart Rate During Mental Arithmetic Stress in Young Females

“…To classify RR sequences, we explored linear discriminant analysis (LDA) commonly used for detection of stress (Melillo et al, 2011; Kaur et al, 2014). We used a log-transformation to meet assumptions of homogeneity of variance and normallydistributed residuals.…”

Personalized modeling of pressure recognition based on Heart Rate Variability

“…At present, pressure recognition based on HRV mai nly extracts relevant pressure features from the time domain and frequency domain for model analysi s. Related research work includes: Based on the improved Support Vector Machine (SVM) psycholo gical stress assessment algorithm [7], the clustering information is assigned to the loss function of the support vector machine by clustering the samples first; Strew color word experiments obtain HRV d ata and extract short-term(32s)HRV time and frequency domain features and higher-order statistics of these features. Using PNN and K nearest neighbors(KNN) classifiers, select different smoothing parameters and K values for pressure recognition [8] Using the time domain and frequency domain fe atures extracted by the HRV, the pressure and non-stress states are identified by Linear Discriminant Analysis (LDA) and Logistic regression(LR), and the classifier and HRV features with optimal detec tion pressure are sought [9]; a fuzzy model interpretation is proposed. The method of autonomic nerv ous system was used to evaluate stress [10].…”

“…The facial blood volume pulse, from which the heart rate and frequency domain of th e HRV are acquired, the relaxation and stress states are identified by Naive Bayesian and SVM algor ithms [11]. However, the literature [7][8][9][10][11] has the following deficiencies: no better HRV features are excavated, and the time domain and frequency domain information do not express the pressure state well; the classification model is too single and can not recognize various pressure states well .…”

“…Concerning the deficiencies in the literature [7][8][9][10][11]. While extracting the effective HRV time doma in and frequency domain information, the HRV nonlinear parameter analysis method was introduced to find pressure-related characteristic parameters.…”

Mobile Stress Recognition and Relaxation Support with SmartCoping: User-Adaptive Interpretation of Physiological Stress Parameters

“…Several studies use HRV to learn a classifier for distinguishing between relaxed states and stressful situations [2,17,18]. An approach described in [25] calculates a daily stress score from multiple inputs, among them HRV.…”