A Dilated CNN-Based Model for Stress Detection Using Raw PPG Signals

Abstract

Stress, a common response to challenging situations, has become pervasive in contemporary daily life due to various factors. Persistent stress can weaken the human immune system, increasing the risk of chronic stress and contributing to a range of physical and mental health disorders. Therefore, timely detection of stress in its early stages is crucial for preventing adverse health outcomes. Physiological signals offer insights into the body's stress-induced changes and can be leveraged for stress detection applications. Among these signals, the photoplethysmogram (PPG) signal stands out due to its advantages. This article introduces an innovative stress detection model based on dilated convolutional neural networks (Dilated CNNs), a deep learning algorithm. This model distinguishes between an individual's stressed and non-stressed states by analysing PPG signals without requiring pre-processing, denoising, or feature extraction. Leveraging the Empatica E4 PPG signals from the Wearable Stress and Affect Detection (WESAD) dataset, the authors developed and evaluated the model, achieving remarkable results: a test accuracy of 93.56% and an area under the curve (AUC) of 96.52%. These outcomes are particularly noteworthy given the streamlined data preparation process and methodological simplicity. Beyond enabling early stress diagnosis, this advancement holds promise for enhancing overall health and well-being in the fast-paced and intricate world. Additionally, its simplicity makes it suitable for real-time stress detection and integration into wearable devices.