Key result
A PPG-based pulse rate variability model correlates strongly with self-reported stress levels.
Why the study?
Existing stress-detection models using only heartbeat or pulse input are limited in prediction output granularity or require additional biosignals that reduce wearability.
Does a photoplethysmography-based model using pulse rate variability accurately predict psychological stress compared to self-reports in human participants?
Observational (n=247)
Does a photoplethysmography-based model using pulse rate variability accurately predict psychological stress compared to self-reports in human participants?
Effect estimate: r = 0.56 to 0.72
p-value: p=<0.0001
A novel photoplethysmography-based model using pulse rate variability and elastic-net regression can accurately predict psychological stress levels with fine granularity using only heartbeat input.
May enable wearable stress monitoring; hypothesis-generating and requires prospective validation before clinical use.
Detecting psychological stress in daily life is useful to stress management. However, existing stress-detection models with only heartbeat/pulse input are limited in prediction output granularity, and models with multiple prediction levels output usually require additional bio-signal other than heartbeat, which may increase the number of sensors and be wearable unfriendly. In this study, we took a novel approach of incremental pulse rate variability and elastic-net regression in predicting mental stress. Mental arithmetic task paradigm was used during the experiments. A total of 178 participants involved in the model building, and the model was verified with a group of 29 participants in the laboratory and 40 participants in a 14-day follow-up field test. The result showed significant median correlations between self-report and model-prediction stress levels (cross-validation: r = 0.72 (p < 0.0001), laboratory verification: r = 0.70 (p < 0.0001), field test r = 0.56 (p < 0.0001)) with fine granularity ratings of 0–7 float numbers. The correct prediction took 86%–91% of the testing samples with error standard deviation of 0.68–0.81 in the label space of 14. By simplifying the process of prediction with a perspective of stress difference and handling the collinearity among pulse rate variability features with elastic net, we successfully built a stress prediction model with only pulse rate variability input source, fine granularity output and portable friendly sensor.
No takes yet. Share an insight, caveat, or question.
Li et al. (2018) conducted an observational in Psychological stress (n=247). Photoplethysmography-based stress detection model using pulse rate variability and elastic-net regression vs. Self-reported stress levels was evaluated on Correlation between self-report and model-prediction stress levels (r = 0.56 to 0.72, p=<0.0001). A photoplethysmography-based model using pulse rate variability significantly correlated with self-reported stress levels in cross-validation, lab, and field tests (r=0.56-0.72, p<0.0001).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: