Key result
CNN model using raw PPG signals identifies stress with ~94% accuracy.
Why the study?
Timely detection of stress in its early stages is crucial for preventing adverse physical and mental health disorders, and photoplethysmogram signals can be leveraged for this purpose.
Does a Dilated CNN-based model accurately detect stress using raw PPG signals?
Does a Dilated CNN-based model accurately detect stress using raw PPG signals?
Effect estimate: AUC 96.52%
A Dilated CNN-based model can accurately detect stress using raw PPG signals without requiring pre-processing or feature extraction, achieving 93.56% accuracy.
Demonstrates feasibility of raw PPG stress detection; hypothesis-generating and requires prospective validation before clinical use.
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.
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Motaman et al. (2025) studied Stress. Dilated convolutional neural network (Dilated CNN) model was evaluated on Stress detection (distinguishing between stressed and non-stressed states) (AUC 96.52%). A dilated CNN-based model using raw PPG signals successfully distinguished between stressed and non-stressed states with a test accuracy of 93.56% and an AUC of 96.52%.
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