Signal processing
HRV-Based Stress Detection from PPG
HRV-based stress detection algorithms classify psychological and physiological stress states by detecting autonomic nervous system activation patterns in PPG-derived HRV features — reduced RMSSD, elevated LF/HF ratio, decreased HF power, and altered nonlinear complexity measures.
How it works
Schematic summary of this guide. Waveforms are illustrative, not measured data.
Acute psychological stress activates the sympathetic nervous system and withdraws parasympathetic tone, typically lowering RMSSD and HF power and raising heart rate. These features form the input to binary stress classifiers (stress vs. no-stress) or multi-class models (baseline, low stress, high stress).
Feature importance analysis consistently ranks RMSSD, LF/HF ratio, HF power (nu), and SD1/SD2 ratio from Poincaré analysis as the top discriminative features.
The fundamental challenge in real-world stress detection is separating mental stress from physical stress (exercise), which produces overlapping HRV signatures. Context-aware models add accelerometer data, time of day and recent activity to reduce this confusion. Consumer stress scores are proprietary combinations of HRV and other signals.
Frequently Asked Questions
How quickly can PPG detect acute stress onset?
HRV changes are detectable within 30–60 seconds of stress onset using 1-minute analysis windows. However, reliable classification typically requires 2–5 minutes to separate transient physiological fluctuations from sustained stress responses.
Can PPG differentiate types of stress?
Physical vs. mental stress can be partially differentiated using accelerometer context.