Nonlinear Heart Rate Variability features for real-life stress detection. Case study: students under stress due to university examination

Abstract

BackgroundThis study investigates the variations of Heart Rate Variability (HRV) due to a real-life stressor and proposes a classifier based on nonlinear features of HRV for automatic stress detection.Methods42 students volunteered to participate to the study about HRV and stress. For each student, two recordings were performed: one during an on-going university examination, assumed as a real-life stressor, and one after holidays. Nonlinear analysis of HRV was performed by using Poincaré Plot, Approximate Entropy, Correlation dimension, Detrended Fluctuation Analysis, Recurrence Plot. For statistical comparison, we adopted the Wilcoxon Signed Rank test and for development of a classifier we adopted the Linear Discriminant Analysis (LDA).ResultsAlmost all HRV features measuring heart rate complexity were significantly decreased in the stress session. LDA generated a simple classifier based on the two Poincaré Plot parameters and Approximate Entropy, which enables stress detection with a total classification accuracy, a sensitivity and a specificity rate of 90%, 86%, and 95% respectively.ConclusionsThe results of the current study suggest that nonlinear HRV analysis using short term ECG recording could be effective in automatically detecting real-life stress condition, such as a university examination.

Welfare implication of measuring heart rate and heart rate variability in dairy cattle: literature review and conclusions for future research

“…Since the first publication of the HRV standards (Malik et al, 1996), a number of non-linear methods has been proposed to quantify HRV, which are derived from chaos theory and non-linear system theory (Voss et al, 2009). Non-linear measures differ from the conventional HRV methods as they are not designed to assess the magnitude of variability but rather the quality, scaling and correlation properties of the HR dynamics (Cervantes et al, 2009;Melillo et al, 2011). Human HRV studies focus on mainly non-linear methods that are implemented in Kubios, a free software for HRV analysis (The Biomedical Signal and Medical Imaging Analysis Group, Department of Applied Physics, University of Kuopio, Finland).…”

Heart Rate Variability Analysis and Performance during a Repeated Mental Workload Task

“…HRV analysis was performed on 5 min excerpts using Kubios (version 2.2) [12]. Time and frequency-domain features were analyzed according to international guidelines [13], while non-linear measures were analyzed as described in [14]. Frequency domain features were extracted from power spectrum estimated with autoregressive (AR) model methods [12].…”

Preliminary Results from a Proof of Concept Study for Fall Detection via ECG Morphology

“…LDA aims to find linear combinations of the input features that can provide an adequate separation between two classes, in the current study, lying down vs standing up. The discriminant function used by LDA was built up as a linear combination of the variables that seek to maximize the differences between the classes [13]. The performance of the classifier was evaluated by computing the rate of correctly recognized lying down (true positives, TP), the rate of correctly recognized standing up (true negatives, TN), the rate of non-recognised lying down (false negative, FN) and the rate of standing classified as lying down (false positive, FP) [13][14][15].…”

“…Total classification accuracy represents the ability of the classifier to discriminate between the two sessions, sensitivity refers to the ability to identify records while lying down and specificity refers to the ability to identify records while standing [13].…”