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

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 mul...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-07, Vol.19 (13), p.3021
Main Authors: Seo, Wonju, Kim, Namho, Kim, Sehyeon, Lee, Chanhee, Park, Sung-Min
Format: Article
Language:English
Subjects:
Artificial intelligence
Biomedical engineering
Deep learning
electrocardiogram
Electrocardiography
Engineering
Heart rate
International conferences
Machine learning
mental stress detection
Nervous system
Neural networks
Occupational stress
Physiology
Psychological stress
Respiration
Signal processing
Stress
Stress relaxation
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19133021