Real-Time Classification of Deep and Non-Deep Sleep With Comparative Intervention Experiments
Sleep is an essential part of human life, and sleep quality is a critical indicator of overall health. This paper presents a system that utilizes a Brain-Computer Interface and a Deep Learning Network for the real-time classification of non-deep sleep and deep sleep. By collecting, uploading, prepro...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10788712/ |
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| author | Mo Xia Hongxi Xue Boning Li Jianting Cao |
| author_facet | Mo Xia Hongxi Xue Boning Li Jianting Cao |
| author_sort | Mo Xia |
| collection | DOAJ |
| description | Sleep is an essential part of human life, and sleep quality is a critical indicator of overall health. This paper presents a system that utilizes a Brain-Computer Interface and a Deep Learning Network for the real-time classification of non-deep sleep and deep sleep. By collecting, uploading, preprocessing, and classifying sleep EEG data in real-time, the system quantitatively evaluates the subjects’ sleep quality. Additionally, we conducted sleep experiments on the same subjects under varying environments and interventions and compared the results. Using our deep learning model, we calculated the proportion of deep sleep in each experimental group and validated the effects of fatigue state and white noise environment on improving the proportion. Based on this system, we examined the specific effects of different interventions (fatigue state and white noise intervention) on sleep states, providing concrete data to determine whether these factors enhance sleep quality. |
| format | Article |
| id | doaj-art-20915bc4ac7943039aa4ca4757efa9df |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-20915bc4ac7943039aa4ca4757efa9df2025-02-05T00:01:12ZengIEEEIEEE Access2169-35362025-01-0113211072111610.1109/ACCESS.2024.351489010788712Real-Time Classification of Deep and Non-Deep Sleep With Comparative Intervention ExperimentsMo Xia0https://orcid.org/0009-0008-6692-1781Hongxi Xue1Boning Li2https://orcid.org/0009-0008-1727-5242Jianting Cao3https://orcid.org/0000-0002-7749-7188Graduate School of Engineering, Saitama Institute of Technology, Fukaya, JapanGraduate School of Engineering, Saitama Institute of Technology, Fukaya, JapanGraduate School of Engineering, Saitama Institute of Technology, Fukaya, JapanGraduate School of Engineering, Saitama Institute of Technology, Fukaya, JapanSleep is an essential part of human life, and sleep quality is a critical indicator of overall health. This paper presents a system that utilizes a Brain-Computer Interface and a Deep Learning Network for the real-time classification of non-deep sleep and deep sleep. By collecting, uploading, preprocessing, and classifying sleep EEG data in real-time, the system quantitatively evaluates the subjects’ sleep quality. Additionally, we conducted sleep experiments on the same subjects under varying environments and interventions and compared the results. Using our deep learning model, we calculated the proportion of deep sleep in each experimental group and validated the effects of fatigue state and white noise environment on improving the proportion. Based on this system, we examined the specific effects of different interventions (fatigue state and white noise intervention) on sleep states, providing concrete data to determine whether these factors enhance sleep quality.https://ieeexplore.ieee.org/document/10788712/Deep learningEEGreal-time classificationsleep health |
| spellingShingle | Mo Xia Hongxi Xue Boning Li Jianting Cao Real-Time Classification of Deep and Non-Deep Sleep With Comparative Intervention Experiments IEEE Access Deep learning EEG real-time classification sleep health |
| title | Real-Time Classification of Deep and Non-Deep Sleep With Comparative Intervention Experiments |
| title_full | Real-Time Classification of Deep and Non-Deep Sleep With Comparative Intervention Experiments |
| title_fullStr | Real-Time Classification of Deep and Non-Deep Sleep With Comparative Intervention Experiments |
| title_full_unstemmed | Real-Time Classification of Deep and Non-Deep Sleep With Comparative Intervention Experiments |
| title_short | Real-Time Classification of Deep and Non-Deep Sleep With Comparative Intervention Experiments |
| title_sort | real time classification of deep and non deep sleep with comparative intervention experiments |
| topic | Deep learning EEG real-time classification sleep health |
| url | https://ieeexplore.ieee.org/document/10788712/ |
| work_keys_str_mv | AT moxia realtimeclassificationofdeepandnondeepsleepwithcomparativeinterventionexperiments AT hongxixue realtimeclassificationofdeepandnondeepsleepwithcomparativeinterventionexperiments AT boningli realtimeclassificationofdeepandnondeepsleepwithcomparativeinterventionexperiments AT jiantingcao realtimeclassificationofdeepandnondeepsleepwithcomparativeinterventionexperiments |