Classification of acute stress using linear and non-linear heart rate variability analysis derived from sternal ECG

Abstract

Chronic stress detection is an important factor in predicting and reducing the risk of cardiovascular disease. This work is a pilot study with a focus on developing a method for detecting short-term psychophysiological changes through heart rate variability (HRV) features. The purpose of this pilot study is to establish and to gain insight on a set of features that could be used to detect psychophysiological changes that occur during chronic stress. This study elicited four different types of arousal by images, sounds, mental tasks and rest, and classified them using linear and non-linear HRV features from electrocardiograms (ECG) acquired by the wireless wearable ePatch® recorder. The highest recognition rates were acquired for the neutral stage (90%), the acute stress stage (80%) and the baseline stage (80%) by sample entropy, detrended fluctuation analysis and normalized high frequency features. Standardizing non-linear HRV features for each subject was found to be an important factor for the improvement of the classification results.

Linear and non-linear heart rate metrics for the assessment of anaesthetists’ workload during general anaesthesia

“…22 However, there is no consensus yet on which of the non-linear methods are eligible for reflecting accurately mental workload. [23][24][25][26] Thus, the investigation of parameters derived from non-linear methods is largely an exploratory analysis. In this study, the standard deviations of the Poincaré plot (SD1, SD2), 27 the approximate entropy (ApEn), 25 28 the sample entropy (SampEn), 25 28 the Shannon entropy of diagonal line lengths' probability distribution (ShanEn), correlation dimension (D2) 25 28 and the permutation entropy (PeEn) are presented.…”

A Flexible and Wearable Human Stress Monitoring Patch

“…Based on the previous studies, it is proved that the physiological data for stress monitoring are responses of human autonomous nervous system (ANS) 10 . The ANS is excited by various kinds of stressors (origins of the stress), such as audial 32 , visual 33 , thermal 34 35 , working 8 36 , and exercising 37 38 stimulations. Therefore ANS signal analysis enables the various kinds of the stress analysis.…”

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

“…[7,29,39]). It correlates well with salivary measurements of cortisol and alpha-amylase as has been shown in [22].…”