Why the study?
Mental stress affects daily life and can cause mental illness, but current detection processes are cumbersome, motivating rapid assessment methods.
Does a deep learning model using PPG-derived Poincaré maps improve the prediction of mental stress compared to conventional HRV analysis?
Does a deep learning model using PPG-derived Poincaré maps improve the prediction of mental stress compared to conventional HRV analysis?
Deep learning applied to PPG-derived Poincaré plots offers a potential noninvasive method for rapid mental stress assessment.
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PPG-HRV analysis may enable rapid mental stress screening; leaves open prospective validation before clinical use.
People’s daily life is easily affected by mental stress, which can lead to mental illness in the long term. The current mental stress detection process is cumbersome, and the development of rapid assessment methods will make a great contribution to medical care. In view of this, this study used a pulse oximeter to obtain noninvasive photoplethysmography (PPG) signals, the measurement information was analyzed using heart rate variability (HRV), and the Poincaré plot of the heartbeat cycle was the output. Poincaré maps are used as input to deep learning (DL) to perform conditional prediction of mental stress. Finally, the results of conventional HRV and DL are compared. From the experimental results, the classification of the feature of the PPG signal (Poincaré plot) by the model is a meaningful and good result.
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Wang et al. (2022) studied this question.
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