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Open AccessArticle

Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress

Department of Creative IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
Research Center of ONESOFTDIGM, Pohang 37673, Korea
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(13), 3021;
Received: 16 June 2019 / Revised: 4 July 2019 / Accepted: 7 July 2019 / Published: 9 July 2019
PDF [1404 KB, uploaded 10 July 2019]


Unmanaged long-term mental stress in the workplace can lead to serious health problems and reduced productivity. To prevent this, it is important to recognize and relieve mental stress in a timely manner. Here, we propose a novel stress detection algorithm based on end-to-end deep learning using multiple physiological signals, such as electrocardiogram (ECG) and respiration (RESP) signal. To mimic workplace stress in our experiments, we used Stroop and math tasks as stressors, with each stressor being followed by a relaxation task. Herein, we recruited 18 subjects and measured both ECG and RESP signals using Zephyr BioHarness 3.0. After five-fold cross validation, the proposed network performed well, with an average accuracy of 83.9%, an average F1 score of 0.81, and an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.92, demonstrating its superiority over conventional machine learning models. Furthermore, by visualizing the activation of the trained network’s neurons, we found that they were activated by specific ECG and RESP patterns. In conclusion, we successfully validated the feasibility of end-to-end deep learning using multiple physiological signals for recognition of mental stress in the workplace. We believe that this is a promising approach that will help to improve the quality of life of people suffering from long-term work-related mental stress. View Full-Text
Keywords: mental stress detection; electrocardiogram; respiration; machine learning; deep learning mental stress detection; electrocardiogram; respiration; machine learning; deep learning

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Seo, W.; Kim, N.; Kim, S.; Lee, C.; Park, S.-M. Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress. Sensors 2019, 19, 3021.

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