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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/13/3021 |
| _version_ | 1811307693998080000 |
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| author | Wonju Seo Namho Kim Sehyeon Kim Chanhee Lee Sung-Min Park |
| author_facet | Wonju Seo Namho Kim Sehyeon Kim Chanhee Lee Sung-Min Park |
| author_sort | Wonju Seo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T09:08:46Z |
| format | Article |
| id | doaj.art-508710ba63a744bfac59063eaeb6546f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T09:08:46Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-508710ba63a744bfac59063eaeb6546f2022-12-22T02:52:55ZengMDPI AGSensors1424-82202019-07-011913302110.3390/s19133021s19133021Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental StressWonju Seo0Namho Kim1Sehyeon Kim2Chanhee Lee3Sung-Min Park4Department of Creative IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, KoreaDepartment of Creative IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, KoreaDepartment of Creative IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, KoreaResearch Center of ONESOFTDIGM, Pohang 37673, KoreaDepartment of Creative IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, KoreaUnmanaged 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.https://www.mdpi.com/1424-8220/19/13/3021mental stress detectionelectrocardiogramrespirationmachine learningdeep learning |
| spellingShingle | Wonju Seo Namho Kim Sehyeon Kim Chanhee Lee Sung-Min Park Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress Sensors mental stress detection electrocardiogram respiration machine learning deep learning |
| title | Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress |
| title_full | Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress |
| title_fullStr | Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress |
| title_full_unstemmed | Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress |
| title_short | Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress |
| title_sort | deep ecg respiration network deeper net for recognizing mental stress |
| topic | mental stress detection electrocardiogram respiration machine learning deep learning |
| url | https://www.mdpi.com/1424-8220/19/13/3021 |
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