Abstract
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.
Highlights
Excessive psychological stress, one of the major mental health problems of modern society[1] is related to many negative mental and physical health outcomes, for example, anxiety, depression disorders, heart disease, cancer, and infectious illnesses.[2]
Cross-validation of elastic-net result showed the best cut at lambda equals 0.0027 (Figure 5) and rejected standard deviation of beat-to-beat interval (SDNN) as a parameter of regression model
The meansquared error during the iteration was stable within the lambda space of 0–0.1324 (log Lambda 2 (–6, –2)), while the space was mostly taken by nine parameters formula, which only varied at the tail with a 1–3 parameter’s drop
Summary
One of the major mental health problems of modern society[1] is related to many negative mental and physical health outcomes, for example, anxiety, depression disorders, heart disease, cancer, and infectious illnesses.[2]. The activation of sympathetic nervous system is considered an outcome of mental stress, while parasympathetic nervous system works in an opposite way as a peacemaker to release the stress and HRV responses to this interplay.[28] With approaches of machine learning, models built in previous works were based on the hypothesis that stress may be classified into several (or binary) ranks.[10] Raw self-reports, for example, collected by Likert-type scales, were re-classified based on researchers’ experience, data distribution, and manipulation exposure.[12] To increase precision of the predictions, galvanic skin response, skin temperature, and respiration data were employed with HRV based on literature.[11,29,30] re-classification for self-report may introduce bias to the ground-truth. This study was underlined by its noninterference of subjective report and its realistic continuous assessment output
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