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.