Heart rate variability (HRV) during acute stress: a comparison of three methods for time–frequency analysis

Objective. Time–frequency (TF) analysis is used to identify oscillatory patterns in complex signals. Cardiac signals under stress conditions are highly dynamic, yet heart rate variability (HRV) is often analysed using classical methods that assume stationarity or linearity. This study applied TF analyses to beat-to-beat RR time-series data extracted from electrocardiograms of 30 healthy adults during three stress tasks: mental calculation, noise exposure, and cold pressor test. Approach. Continuous wavelet transform (CWT), and ensemble empirical mode decomposition (EEMD) were compared to the standard short-term Fourier transform (STFT). Signals were divided into anticipation, stress, and recovery periods. Main results. When analysed in 30 s windows, all three methods detected dynamic time variations in standard frequency bands (low-frequency (LF) [0.04–0.15 Hz], high-frequency (HF) [0.15–0.40 Hz]) during stress compared to baseline. Compared to SFFT, EEMD and CWT showed greater sensitivity than STFT to identify LF and HF differences. Spectrograms identified regions of interest outside standard frequency bands, where CWT provided superior temporal and frequency resolution, especially at low frequencies. While EEMD spectrograms were uninterpretable, analysis of individual EEMD modes enabled tracking instantaneous changes in both frequency and amplitude. Significance. In conclusion, CWT and EEMD proved most valuable for identifying patterns in stress-evoked HRV and providing information on autonomic nervous system activation latency, responsiveness, and adaptability.