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

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.

[1]  P. Melillo,et al.  Nonlinear Heart Rate Variability features for real-life stress detection. Case study: students under stress due to university examination , 2011, Biomedical engineering online.

[2]  P. Lang.  International affective picture system (IAPS) : affective ratings of pictures and instruction manual , 2005 .

[3]  Thomas Penzel,et al.  Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea , 2003, IEEE Transactions on Biomedical Engineering.

[4]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[5]  Marimuthu Palaniswami,et al.  Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? , 2001, IEEE Transactions on Biomedical Engineering.

[6]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[7]  W. Bardwell,et al.  Effects of stress on heart rate complexity—A comparison between short-term and chronic stress , 2009, Biological Psychology.

[8]  Enzo Pasquale Scilingo,et al.  The Role of Nonlinear Dynamics in Affective Valence and Arousal Recognition , 2012, IEEE Transactions on Affective Computing.

[9]  Sheldon Cohen,et al.  Psychological stress and disease. , 2007, JAMA.

[10]  A. K. Blangsted,et al.  The effect of mental stress on heart rate variability and blood pressure during computer work , 2004, European Journal of Applied Physiology.

[11]  D. Rees,et al.  The sympathetic nervous system in polycystic ovary syndrome: a novel therapeutic target? , 2012, Clinical endocrinology.

[12]  K. Chon,et al.  Approximate entropy for all signals , 2009, IEEE Engineering in Medicine and Biology Magazine.

[13]  Niels Wessel,et al.  Correlation dimension analysis of heart rate variability in patients with dilated cardiomyopathy , 2005, Comput. Methods Programs Biomed..

[14]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.