Beat-to-beat ECG features for time resolution improvements in stress detection

Abstract

In stress sensing, Window-derived Heart Rate Variability (W-HRV) methods are by far the most heavily used feature extraction methods. However, these W-HRV methods come with a variety of tradeoffs that motivate the development of alternative methods in stress sensing. We compare our method of using HeartBeat Morphology (HBM) features for stress sensing to the traditional W-HRV method for feature extraction. In order to adequately evaluate these methods we conduct a Trier Social Stress Test (TSST) to elicit stress in a group of 13 firefighters while recording their ECG, actigraphy, and psychological self-assessment measures. We utilize the data from this experiment to analyze both feature extraction methods in terms of computational complexity, detection resolution performance, and event localization performance. Our results show that each method has an ideal niche for its use in stress sensing. HBM features tend to be more effective in an online, stress detection context. W-HRV shows to be more suitable for offline post processing to determine the exact localization of the stress event.