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

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Huijun Wang,1– 3,* Guodong Lin,4,* Yanru Li,1– 3 Xiaoqing Zhang,1– 3 Wen Xu,1– 3 Xingjun Wang,4 Demin Han1– 3 1Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, People’s Republic of China; 3Key Laboratory of Otolaryngology-Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People’s Republic of China; 4Department of Electronic Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, People’s Republic of China*These authors contributed equally to this workCorrespondence: Demin HanBeijing Tongren Hospital, Capital Medical University, No. 1 Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, People’s Republic of ChinaTel +86-010-58269335Fax +86-010-58269331Email deminhan_ent@hotmail.comXingjun WangTsinghua Shenzhen International Graduate School, University Town of Shenzhen, Nanshan District, Shenzhen, 518055, People’s Republic of ChinaTel +86-18038153071Email wangxingjun@tsinghua.edu.cnPurpose: 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).Patients and Methods: 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.Results: 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.Conclusion: 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.Keywords: sleep-disordered breathing, SDB, deep learning, sleep stage, children

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