Photoplethysmography based psychological stress detection with pulse rate variability feature differences and elastic net
Detecting psychological stress in daily life is useful to stress management. However, existing stress-detection models with only heartbeat/pulse input are limited in prediction output granularity, and models with multiple prediction levels output usually require additional bio-signal other than hear...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-09-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147718803298 |
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| _version_ | 1832559111800094720 |
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| author | Fenghua Li Peida Xu Shichun Zheng Wenfeng Chen Yang Yan Suo Lu Zhengkui Liu |
| author_facet | Fenghua Li Peida Xu Shichun Zheng Wenfeng Chen Yang Yan Suo Lu Zhengkui Liu |
| author_sort | Fenghua Li |
| collection | DOAJ |
| description | Detecting psychological stress in daily life is useful to stress management. However, existing stress-detection models with only heartbeat/pulse input are limited in prediction output granularity, and models with multiple prediction levels output usually require additional bio-signal other than heartbeat, which may increase the number of sensors and be wearable unfriendly. In this study, we took a novel approach of incremental pulse rate variability and elastic-net regression in predicting mental stress. Mental arithmetic task paradigm was used during the experiments. A total of 178 participants involved in the model building, and the model was verified with a group of 29 participants in the laboratory and 40 participants in a 14-day follow-up field test. The result showed significant median correlations between self-report and model-prediction stress levels (cross-validation: r = 0.72 (p < 0.0001), laboratory verification: r = 0.70 (p < 0.0001), field test r = 0.56 (p < 0.0001)) with fine granularity ratings of 0–7 float numbers. The correct prediction took 86%–91% of the testing samples with error standard deviation of 0.68–0.81 in the label space of 14. By simplifying the process of prediction with a perspective of stress difference and handling the collinearity among pulse rate variability features with elastic net, we successfully built a stress prediction model with only pulse rate variability input source, fine granularity output and portable friendly sensor. |
| format | Article |
| id | doaj-art-f447a26a91404dcdad9cb924dbd4b76a |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2018-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-f447a26a91404dcdad9cb924dbd4b76a2025-02-03T01:30:50ZengWileyInternational Journal of Distributed Sensor Networks1550-14772018-09-011410.1177/1550147718803298Photoplethysmography based psychological stress detection with pulse rate variability feature differences and elastic netFenghua Li0Peida Xu1Shichun Zheng2Wenfeng Chen3Yang Yan4Suo Lu5Zhengkui Liu6University of Chinese Academy of Sciences, Beijing, People’s Republic of ChinaHuawei Device (Dongguan) Co., Ltd., Shenzhen, People’s Republic of ChinaUniversity of Chinese Academy of Sciences, Beijing, People’s Republic of ChinaState Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaUniversity of Chinese Academy of Sciences, Beijing, People’s Republic of ChinaUniversity of Chinese Academy of Sciences, Beijing, People’s Republic of ChinaKey Lab of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaDetecting psychological stress in daily life is useful to stress management. However, existing stress-detection models with only heartbeat/pulse input are limited in prediction output granularity, and models with multiple prediction levels output usually require additional bio-signal other than heartbeat, which may increase the number of sensors and be wearable unfriendly. In this study, we took a novel approach of incremental pulse rate variability and elastic-net regression in predicting mental stress. Mental arithmetic task paradigm was used during the experiments. A total of 178 participants involved in the model building, and the model was verified with a group of 29 participants in the laboratory and 40 participants in a 14-day follow-up field test. The result showed significant median correlations between self-report and model-prediction stress levels (cross-validation: r = 0.72 (p < 0.0001), laboratory verification: r = 0.70 (p < 0.0001), field test r = 0.56 (p < 0.0001)) with fine granularity ratings of 0–7 float numbers. The correct prediction took 86%–91% of the testing samples with error standard deviation of 0.68–0.81 in the label space of 14. By simplifying the process of prediction with a perspective of stress difference and handling the collinearity among pulse rate variability features with elastic net, we successfully built a stress prediction model with only pulse rate variability input source, fine granularity output and portable friendly sensor.https://doi.org/10.1177/1550147718803298 |
| spellingShingle | Fenghua Li Peida Xu Shichun Zheng Wenfeng Chen Yang Yan Suo Lu Zhengkui Liu Photoplethysmography based psychological stress detection with pulse rate variability feature differences and elastic net International Journal of Distributed Sensor Networks |
| title | Photoplethysmography based psychological stress detection with pulse rate variability feature differences and elastic net |
| title_full | Photoplethysmography based psychological stress detection with pulse rate variability feature differences and elastic net |
| title_fullStr | Photoplethysmography based psychological stress detection with pulse rate variability feature differences and elastic net |
| title_full_unstemmed | Photoplethysmography based psychological stress detection with pulse rate variability feature differences and elastic net |
| title_short | Photoplethysmography based psychological stress detection with pulse rate variability feature differences and elastic net |
| title_sort | photoplethysmography based psychological stress detection with pulse rate variability feature differences and elastic net |
| url | https://doi.org/10.1177/1550147718803298 |
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