Modelling stress in public speaking: Evolution of stress levels during conference presentations
Theerasak Chanwimalueang, Lisa Aufegger, Wilhelm von Rosenberg, Danilo P. Mandic
Abstract
The Electrocardiogram (ECG) collected in real-life scenarios is often noisy and contaminated with motion artefacts. This study proposes a new framework to analyse the heart rate variability (HRV) in mobile scenarios by introducing novel R-peak detection and HRV detrending algorithms. The R-peak detection combines matched filtering and Hilbert transform, while detrending the HRV is performed using empirical mode decomposition with novel physically meaningful stopping criteria. Next, four quantitative metrics-sample entropy, LFhrv, HFhrv and LF/HF ratio - are used to estimate stress levels in two public speaking events: (i) a presentation in front of an audience and (ii) an interactive poster presentation, both at ICASSP 2015. We show that the proposed framework makes it possible to detect distinctive `stress-patterns' in the structural complexity of the HRV, thus verifying the complexity-loss hypothesis in physiological research.
BibTeX
@inproceedings{icassp2016_modellingstressi,
title = {Modelling stress in public speaking: Evolution of stress levels during conference presentations},
author = {Theerasak Chanwimalueang and Lisa Aufegger and Wilhelm von Rosenberg and Danilo P. Mandic},
booktitle = {ICASSP 2016},
year = {2016}
}