A Novel Rapid Assessment of Mental Stress by Using PPG Signals Based on Deep Learning
Zhihao Wang, Yu-Chan Wu
Abstract
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.