Journal of Otolaryngology and Ophthalmology of Shandong University ›› 2023, Vol. 37 ›› Issue (6): 126-132.doi: 10.6040/j.issn.1673-3770.0.2023.168

• Clinical Study • Previous Articles     Next Articles

The sleep structure of Children with obstructive sleep apnea and the development of a sleep structure interpretation model

ZHANG Yitong1, LI Qingxiang2, SHI Zhenghao2, SHANG Lei3, YUAN Yuqi1, CAO Zine1, MA Lina1, LIU Haiqin1, REN Xiaoyong1, SHI Yewen1   

  1. 1. Department of Otorhinolaryngology & Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, Shaanxi, China2. School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, Shaanxi, China3. Department of Health Statistics, School of Public Health, Air Force Medical University, Xi'an 710038, Shaanxi, China
  • Published:2023-12-15

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Abstract: Objective To explore the sleep structure in children with different degrees of obstructive sleep apnea(OSA)and construct an automatic sleep structure interpretation model using deep learning. Methods We retrospectively analyzed the polysomnography(PSG)results of 146 school-aged children, and compared the parameters, such as respiratory events, oxygen saturation, and sleep architecture in children with different severity of OSA. Using deep learning, a hybrid neural network composed of convolutional neural networks(CNN), attention mechanism, and recurrent neural network(RNN)was used to construct an automatic sleep structure interpretation model. A combination of electroencephalogram and electrooculogram were used as input signals. The model’s performance was evaluated using public and private databases. Results There was no significant difference in the percentage of rapid eye movement(REM)and the percentage of non-rapid eye movement(NREM)stage 1 among the primary snoring, mild OSA, and moderate to severe OSA groups(P>0.05). The percentage of the N2 stage was significantly higher in the moderate-severe OSA group than in the primary snoring and the mild OSA groups(P=0.024). The percentage of the N3 stage was significantly lower in the moderate-severe OSA group than in the primary snoring and the mild OSA groups(P<0.001). The total arousal index(ARtotI)and respiratory arousal index(RAI)in the moderate-severe OSA group were significantly lower than those in the primary snoring and the mild OSA groups(P<0.001). Using the public database, the automatic sleep structure interpretation model proposed in this study had an overall accuracy of 79.90%, and F1 values of 45.90%, 85.40%, 83.70%, and 79.90% in N1, N2, N3, and R stages, respectively. Using the private database, the overall accuracy was 71.30%, and the F1 values were 40.20%、81.60%、77.70%, and 67.40% in N1, N2, N3, and R stages, respectively. Conclusion The sleep structure of children with moderate-to-severe OSA showed a prolonged NREM2 and a shortened NREM3. Overall, the deep learning-based automatic sleep structure interpretation model demonstrated stability and a good classification effect.

Key words: Obstructive sleep apnea, Children, Sleep structure, Artificial intelligence, Deep learning model

CLC Number: 

  • R766.4
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