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

A technical paper not of general interest, but useful to my research. They find that the most distinguishing parameters of HRV are Sample Entropy ( a parameter I’m already looking at), normalized HF (already looking at) and detrended fluctuation analysis (need to look at more closely.)

I look at LF/HF because that is what I find correlates best with clinical outcomes, but am willing to be wrong if these parameters are more sensitive.

Conf Proc IEEE Eng Med Biol Soc. 2014 Aug;2014:3386-9. doi: 10.1109/EMBC.2014.6944349.

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

Tanev G, Saadi DB, Hoppe K, Sorensen HB.

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.