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Image_3_Exploration of sleep function connection and classification strategies based on sub-period sleep stages.TIF
| العنوان: | Image_3_Exploration of sleep function connection and classification strategies based on sub-period sleep stages.TIF |
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| المؤلفون: | Fangzhou Xu, Jinzhao Zhao, Ming Liu, Xin Yu, Chongfeng Wang, Yitai Lou, Weiyou Shi, Yanbing Liu, Licai Gao, Qingbo Yang, Baokun Zhang, Shanshan Lu, Jiyou Tang, Jiancai Leng |
| سنة النشر: | 2023 |
| المجموعة: | Frontiers: Figshare |
| مصطلحات موضوعية: | Neuroscience, Biological Engineering, Developmental Biology, Stem Cells, Artificial Intelligence and Image Processing, Endocrinology, Radiology and Organ Imaging, Autonomic Nervous System, Cellular Nervous System, Central Nervous System, Sensory Systems, Clinical Nursing: Tertiary (Rehabilitative), Decision Making, Rehabilitation Engineering, Biomedical Engineering not elsewhere classified, Signal Processing, Neurogenetics, Image Processing, electroencephalography (EEG), sleep stage, classification, brain functional connectivity, phase-locked value (PLV) |
| الوصف: | Background As a medium for developing brain-computer interface systems, EEG signals are complex and difficult to identify due to their complexity, weakness, and differences between subjects. At present, most of the current research on sleep EEG signals are single-channel and dual-channel, ignoring the research on the relationship between different brain regions. Brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. Methods Phase-locked value (PLV) is used to construct a functional connection network. The connection network is used to analyze the connection mechanism and brain interaction in different sleep stages. Firstly, the entire EEG signal is divided into multiple sub-periods. Secondly, Phase-locked value is used for feature extraction on the sub-periods. Thirdly, the PLV of multiple sub-periods is used for feature fusion. Fourthly, the classification performance optimization strategy is used to discuss the impact of different frequency bands on sleep stage classification performance and to find the optimal frequency band. Finally, the brain function network is constructed by using the average value of the fusion features to analyze the interaction of brain regions in different frequency bands during sleep stages. Results The experimental results have shown that when the number of sub-periods is 30, the α (8–13 Hz) frequency band has the best classification effect, The classification result after 10-fold cross-validation reaches 92.59%. Conclusion The proposed algorithm has good sleep staging performance, which can effectively promote the development and application of an EEG sleep staging system. |
| نوع الوثيقة: | still image |
| اللغة: | unknown |
| Relation: | https://figshare.com/articles/figure/Image_3_Exploration_of_sleep_function_connection_and_classification_strategies_based_on_sub-period_sleep_stages_TIF/21953534 |
| DOI: | 10.3389/fnins.2022.1088116.s003 |
| الاتاحة: | https://doi.org/10.3389/fnins.2022.1088116.s003 https://figshare.com/articles/figure/Image_3_Exploration_of_sleep_function_connection_and_classification_strategies_based_on_sub-period_sleep_stages_TIF/21953534 |
| Rights: | CC BY 4.0 |
| رقم الانضمام: | edsbas.99335A4 |
| قاعدة البيانات: | BASE |
| DOI: | 10.3389/fnins.2022.1088116.s003 |
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