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

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.

5 figures , 2 tables

Cited by in corpus (3)

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• Multimodal signal fusion for stress detection using deep neural networks: a novel approach for converting 1D signals to unified 2D images
• Continuous Wavelet Transformation and VGG16 Deep Neural Network for Stress Classification in PPG Signals