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Automatic Sleep Stage Classification of Children with Sleep-Disordered Breathing Using the Modularized Network.

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收录情况： ◇ SCIE

作者：
Wang Huijun[1,2,3] * ; Lin Guodong[4] * ; Li Yanru[1,2,3] ; Zhang Xiaoqing[1,2,3] ; Xu Wen[1,2,3] ; Wang Xingjun[4,*2] ; Han Demin[1,2,3,*1] ;
机构： [1]Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China. [2]Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, People's Republic of China. [3]Key Laboratory of Otolaryngology-Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People's Republic of China. [4]Department of Electronic Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, People's Republic of China
出处：
DOI： 10.2147/NSS.S336344
ISSN：

关键词： sleep-disordered breathing SDB deep learning sleep stage children

摘要：
To develop an automatic sleep stage analysis model for children and evaluate the effect of the model on the diagnosis of sleep-disordered breathing (SDB).Three hundred and forty-four SDB patients aged between 2 to 18 years who completed polysomnography (PSG) to assess the severity of the disease were enrolled in this study. We developed deep neural networks to stage sleep from electroencephalography (EEG), electrooculography (EOG) and electromyogram (EMG). The model performance was estimated by accuracy, precision, recall, F1-score, and Cohen's Kappa coefficient (ĸ). And we compared the difference in calculation of sleep parameters among the technicians, the model ensemble, and the single-channel EEG model.The numbers of raw data divided into training, validation, and testing were 240, 36, and 68, respectively. The best performance appeared in the model ensemble of which the accuracy was 83.36% (ĸ=0.7817) in 5-stages, and the accuracy was 96.76% (ĸ=0.8236) in 2-stages. The single-channel EEG model showed the classification satisfyingly as well. There was no significant difference in TST, SE, SOL, time in W, time in N1+N2, time in N3, and OAHI between technician and the model (P>0.05). On the datasets from sleep-EDF-13 and sleep-EDF-18, the average classification accuracies achieved were 92.76% and 91.94% in 5-stages by using the proposed method, respectively.This research established the model for pediatric automatic sleep stage classification with satisfying reliability and generalizability. In addition, it could be applied for calculating quantitative sleep parameters and evaluating the severity of SDB.© 2021 Wang et al.

基金：
Science and Technology Innovation Committee of Shenzhen (WDZC20200818121348001), the National Natural Science Foundation of China (81970866, 81800894), the Consulting research project of Chinese Academy of Engineering (2019-XZ-29), Shenzhen Municipal Natural Science Foundation, and the Shenzhen Science and Technology Innovation Committee (KCXFZ2020020 11010487).
语种：
外文
被引次数：
WOS：
PubmedID：
中科院(CAS)分区：
出版当年[2020]版：
大类 | 2 区 医学
小类 | 3 区 临床神经病学 3 区 神经科学
最新[2025]版：
大类 | 3 区 医学
小类 | 3 区 临床神经病学 3 区 神经科学
JCR分区：
出版当年[2019]版：
Q1 CLINICAL NEUROLOGY Q2 NEUROSCIENCES
最新[2024]版：
Q2 CLINICAL NEUROLOGY Q2 NEUROSCIENCES

影响因子： 最新[2024版] 最新五年平均 出版当年[2019版] 出版当年五年平均 出版前一年[2018版] 出版后一年[2020版]

第一作者：
第一作者机构： [1]Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China. [2]Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, People's Republic of China. [3]Key Laboratory of Otolaryngology-Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People's Republic of China.
共同第一作者：
通讯作者：
通讯机构： [1]Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China. [2]Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, People's Republic of China. [3]Key Laboratory of Otolaryngology-Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People's Republic of China. [4]Department of Electronic Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, People's Republic of China [*1]Beijing Tongren Hospital, Capital Medical University, No. 1 Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, People’s Republic of China [*2]Tsinghua Shenzhen International Graduate School, University Town of Shenzhen, Nanshan District, Shenzhen, 518055, People’s Republic of China
推荐引用方式(GB/T 7714)：
Wang Huijun,Lin Guodong,Li Yanru,et al.Automatic Sleep Stage Classification of Children with Sleep-Disordered Breathing Using the Modularized Network.[J].NATURE AND SCIENCE OF SLEEP.2021,13:2101-2112.doi:10.2147/NSS.S336344.
APA：
Wang Huijun,Lin Guodong,Li Yanru,Zhang Xiaoqing,Xu Wen...&Han Demin.(2021).Automatic Sleep Stage Classification of Children with Sleep-Disordered Breathing Using the Modularized Network..NATURE AND SCIENCE OF SLEEP,13,
MLA：
Wang Huijun,et al."Automatic Sleep Stage Classification of Children with Sleep-Disordered Breathing Using the Modularized Network.".NATURE AND SCIENCE OF SLEEP 13.(2021):2101-2112

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