BACKGROUND

Assessing real-time stress in individuals to prevent the accumulation of stress is necessary due to the adverse effects of excessive psychological stress on health. Since both stress and circadian rhythms affect the excitability of the nervous system, the influence of circadian rhythms needs to be considered during stress assessment. Most studies train classifiers using physiological data collected during fixed short time periods, overlooking the assessment of stress levels at other times.

METHODS

In this work, we propose a method for training a classifier capable of identifying stress and resting states throughout the day, based on 10 short-term heart rate variability (HRV) feature data obtained from morning, noon, and evening. To characterize the circadian rhythms of HRV features, heartbeat interval data were collected and analyzed from 50 volunteers over three consecutive days. The circadian rhythm trends in the HRV features were then removed using the Smoothness Priors Approach (SPA), and XGBoost models were trained to assess stress.

RESULTS

The results show that all HRV features exhibit 12-h and 24-h circadian rhythms, and the circadian rhythm differences across different days for individuals are relatively small. Furthermore, training classifiers on detrended data can improve the overall accuracy of stress assessment across all time periods. Specifically, when combining data from different time periods as the training dataset, the accuracy of the classifier trained on detrended data increases by 13.67%.

DISCUSSION

These findings indicate that using HRV features with circadian rhythm trends removed is an effective method for assessing stress at all times throughout the day.