Key result
A novel framework for analyzing heart rate variability detected distinctive stress-patterns in the structural complexity of HRV during public speaking events.
Observational
A novel ECG signal processing framework effectively detects stress patterns in heart rate variability during real-life public speaking scenarios.
Hypothesis-generating for real-time stress detection via HRV complexity; requires prospective validation before clinical use.
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.
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Chanwimalueang et al. (2016) conducted an observational in Stress in public speaking. Public speaking events was evaluated on Stress levels estimated by sample entropy, LFhrv, HFhrv, and LF/HF ratio. A novel framework for analyzing heart rate variability detected distinctive stress-patterns in the structural complexity of HRV during public speaking events.
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