Stress Detection Using PPG Signal and Combined Deep CNN-MLP Network

TL;DR

This paper presents a deep learning model combining CNN and MLP to detect stress from PPG signals with about 82% accuracy, utilizing a new publicly available dataset.

Contribution

It introduces a novel CNN-MLP deep learning approach for stress detection using PPG signals from the UBFC-Phys dataset.

Findings

Stress can be detected with approximately 82% accuracy.

PPG signals are effective for early stress detection.

The proposed model outperforms traditional methods.

Abstract

Stress has become a fact in people's lives. It has a significant effect on the function of body systems and many key systems of the body including respiratory, cardiovascular, and even reproductive systems are impacted by stress. It can be very helpful to detect stress episodes in early steps of its appearance to avoid damages it can cause to body systems. Using physiological signals can be useful for stress detection as they reflect very important information about the human body. PPG signal due to its advantages is one of the mostly used signal in this field. In this research work, we take advantage of PPG signals to detect stress events. The PPG signals used in this work are collected from one of the newest publicly available datasets named as UBFC-Phys and a model is developed by using CNN-MLP deep learning algorithm. The results obtained from the proposed model indicate that stress can be detected with an accuracy of approximately 82 percent.