山东大学耳鼻喉眼学报 ›› 2023, Vol. 37 ›› Issue (6): 126-132.doi: 10.6040/j.issn.1673-3770.0.2023.168

• 临床研究 • 上一篇    下一篇

阻塞性睡眠呼吸暂停儿童睡眠结构研究及睡眠结构判读模型建立

张一彤1,李青香2,石争浩2,尚磊3,袁钰淇1,曹子讷1,麻莉娜1,刘海琴1,任晓勇1,施叶雯1   

  1. 1. 西安交通大学第二附属医院 耳鼻咽喉头颈外科, 陕西 西安 710004;
    2. 西安理工大学 计算机科学与工程学院, 陕西 西安 710048;
    3. 空军军医大学军事预防医学院 卫生统计学教研室, 陕西 西安 710038
  • 发布日期:2023-12-15
  • 通讯作者: 施叶雯. E-mail:shiyewen8813@126.com
  • 基金资助:
    国家自然科学基金项目(62076198)

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

PDF (PC)

39

摘要： 目的 讨论不同程度阻塞性睡眠呼吸暂停(obstructive sleep apnea, OSA)儿童睡眠结构的改变,并基于深度学习方法构建睡眠结构自动判读模型,提高睡眠结构判读的精准度和效率。 方法 回顾性分析146例学龄期打鼾儿童的多导睡眠监测(polysomnography, PSG)结果,比较不同严重程度OSA儿童呼吸事件、血氧饱和度和睡眠结构等参数的差异。基于深度学习方法,以脑电和眼电联合通道作为输入信号,由卷积神经网络(convolutional neural networks, CNN)、注意力机制和循环神经网络(recurrent neural network, RNN)组成混合神经网络,构建睡眠结构自动判读模型,并于公开数据集和私人数据集进行模型性能评估。 结果 单纯打鼾组、轻度OSA组和中重度OSA组间快动眼(rapid eye movement, REM)期百分比和非快动眼睡眠(non-rapid eye movement, NREM)1期百分比比较差异未见统计学意义(P>0.05)。中重度OSA组N2期百分比明显高于单纯打鼾组、轻度OSA组(P=0.024),中重度OSA组N3期百分比明显低于单纯打鼾组、轻度OSA组(P<0.001),中重度OSA组觉醒指数(total arousal index, ARtotI)和呼吸事件相关觉醒指数(respiratory arousal index, RAI)明显高于单纯打鼾组、轻度OSA组(P<0.001),差异均具有统计学意义。本研究提出的睡眠结构自动判读模型,在公开数据集上总体准确率为79.90%,在N1期、N2期、N3期及R期的F1值分别为45.90%、85.40%、83.70%和79.90%,在私人数据集上总体准确率为71.30%,在N1期、N2期、N3期及R期的F1值分别为40.20%、81.60%、77.70%和67.40%。 结论 OSA儿童随疾病进展,睡眠结构出现N2期延长和N3期缩短,本研究提出的基于深度学习的睡眠结构自动判读模型,具有良好的分类效果和稳定性。

关键词: 阻塞性睡眠呼吸暂停, 儿童, 睡眠结构, 人工智能, 自动判读模型

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

中图分类号: 

  • R766.4
[1] Spruyt K, Capdevila OS, Serpero LD, et al. Dietary and physical activity patterns in children with obstructive sleep apnea[J]. J Pediatr, 2010, 156(5): 724-730.e3. doi:10.1016/j.jpeds.2009.11.010
[2] 王岩. 儿童睡眠呼吸疾病的分类与治疗进展[J]. 山东大学耳鼻喉眼学报, 2016, 30(5): 5-8. doi: 10.6040/j.issn.1673-3770.0.2016.405 WANG Yan. Progress in the classification and treatment of sleep respiratory diseases in children[J]. China Industrial Economics, 2016, 30(5): 5-8. doi: 10.6040/j.issn.1673-3770.0.2016.405
[3] 刘大波. 重视儿童阻塞性睡眠呼吸暂停低通气综合征睡眠结构紊乱[J]. 山东大学耳鼻喉眼学报, 2018, 32(2): 6-8. doi: 10.6040/j.issn.1673-3770.0.2017.534 LIU Dabo. Evaluation of sleep structure disorder in children with obstructive sleep apnea hypopnea syndrome[J]. Journal of Otolaryngology and Ophthalmology of Shandong University, 2018, 32(2): 6-8. doi: 10.6040/j.issn.1673-3770.0.2017.534
[4] Berry RB, Brooks R, Gamaldo C, et al. AASM scoring manual updates for 2017(version 2.4)[J]. J Clin Sleep Med, 2017, 13(5): 665-666. doi:10.5664/jcsm.6576
[5] 中国儿童OSA诊断与治疗指南制订工作组, 中华医学会耳鼻咽喉头颈外科学分会小儿学组, 中华医学会儿科学分会呼吸学组, 等. 中国儿童阻塞性睡眠呼吸暂停诊断与治疗指南(2020)[J]. 中华耳鼻咽喉头颈外科杂志, 2020, 55(8): 729-747. doi:10.3760/cma.j.cn115330-20200521-00431 Working Group of Chinese Guideline for the Diagnosis and Treatment of Childhood OSA; Subspecialty Group of Pediatrics, Society of Otorhinolaryngology Head and Neck Surgery, Chinese Medical Association; Subspecialty Group of Respiratory Diseases, Society of Pediatrics, Chinese Medical Association, et al. Chinese guideline for the diagnosis and treatment of childhood obstructive sleep apnea(2020)[J]. Chin J Otorhinolaryngol Head Neck Surg, 2020, 55(8): 729-747. doi:10.3760/cma.j.cn115330-20200521-00431
[6] Craik A, He YT, Contreras-Vidal JL. Deep learning for electroencephalogram(EEG)classification tasks: a review[J]. J Neural Eng, 2019, 16(3): 031001. doi:10.1088/1741-2552/ab0ab5
[7] Yu Y, Si XS, Hu CH, et al. A review of recurrent neural networks: LSTM cells and network architectures[J]. Neural Comput, 2019, 31(7): 1235-1270. doi:10.1162/neco_a_01199
[8] Goldberger AL, Amaral LA, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals[J]. Circulation, 2000, 101(23): E215-E220. doi:10.1161/01.cir.101.23.e215
[9] Qu W, Wang ZY, Hong H, et al. A residual based attention model for EEG based sleep staging[J]. IEEE J Biomed Health Inform, 2020, 24(10): 2833-2843. doi:10.1109/jbhi.2020.2978004
[10] Supratak A, Dong H, Wu C, et al. DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG[J]. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(11): 1998-2008. doi:10.1109/tnsre.2017.2721116
[11] Humayun AI, Sushmit AS, Hasan T, et al. End-to-end sleep staging with raw single channel EEG using deep residual ConvNets[C] //2019 IEEE EMBS International Conference on Biomedical & Health Informatics(BHI), 2019, 5:1-5. doi:10.1109/BHI.2019.8834483
[12] Andreotti F, Phan H, Cooray N, et al. Multichannel sleep stage classification and transfer learning using convolutional neural networks[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2018: 171-174. doi:10.1109/EMBC.2018.8512214
[13] Phan H, Andreotti F, Cooray N, et al. Joint classification and prediction CNN framework for automatic sleep stage classification[J]. IEEE Trans Biomed Eng, 2019, 66(5): 1285-1296. doi:10.1109/tbme.2018.2872652
[14] Babkoff H, Caspy T, Mikulincer M, et al. Monotonic and rhythmic influences: a challenge for sleep deprivation research[J]. Psychol Bull, 1991, 109(3): 411-428. doi:10.1037/0033-2909.109.3.411
[15] Garpestad E, Ringler J, Parker JA, et al. Sleep stage influences the hemodynamic response to obstructive apneas[J]. Am J Respir Crit Care Med, 1995, 152(1): 199-203. doi:10.1164/ajrccm.152.1.7599824
[16] Durdik P, Sujanska A, Suroviakova S, et al. Sleep architecture in children with common phenotype of obstructive sleep apnea[J]. J Clin Sleep Med, 2018, 14(1): 9-14. doi:10.5664/jcsm.6868
[17] Heubi CH, Knollman P, Wiley S, et al. Sleep architecture in children with down syndrome with and without obstructive sleep apnea[J] Otolaryngol Head Neck Surg, 2021, 164(5): 1108-1115. doi: 10.1177/0194599820960454
[18] 张丰珍, 王桂香, 许志飞, 等. 儿童重度OSAHS睡眠结构及相关因素分析[J]. 临床耳鼻咽喉头颈外科杂志, 2019,(5): 441-446. doi:10.13201/j.issn.1001-1781.2019.05.014 Zhang FZ, Wang GX, Xu ZF, et al. Analysis of sleep structure and related factors in children with severe obstructive sleep apnea-hypopnea syndrome[J]. Journal of Clinical Otorhinolaryngology Head and Neck Surgery, 2019,(5): 441-446. doi:10.13201/j.issn.1001-1781.2019.05.014
[19] Krajca V, Petranek S, Paul K, et al. Automatic detection of sleep stages in neonatal EEG using the structural time profiles[J]. Conf Proc IEEE Eng Med Biol Soc., 2005: 6014-6016. doi:10.1109/iembs.2005.1615862
[20] Tsinalis O, Matthews PM, Guo YK. Automatic sleep stage scoring using time-frequency analysis and stacked sparse autoencoders[J]. Ann Biomed Eng, 2016, 44(5): 1587-1597. doi:10.1007/s10439-015-1444-y
[21] He KM, Zhang XY, Ren SQ, et al. Deep residual learning for image recognition[C] //2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). June 27-30, 2016, Las Vegas, NV, USA. IEEE, 2016: 770-778. doi:10.1109/CVPR.2016.90
[22] Begawan IA, Djamal EC, Djajasasmita D, et al. Sleep stage identification based on EEG signals using parallel convolutional neural network and recurrent neural network[C] //2022 International Conference on Advanced Computer Science and Information Systems(ICACSIS). October 1-3, 2022, Depok, Indonesia. IEEE, 2022: 39-44. doi:10.1109/ICACSIS56558.2022.9922962
[1] 鲁欢,何本超,廖红明. 儿童急性鼻窦炎合并眶眼蜂窝织炎并发颅内感染1例并文献复习[J]. 山东大学耳鼻喉眼学报, 2024, 38(1): 46-50.
[2] 王亚芳,赵海涛,王娟,刘晓峰,阎玉彦,温鑫. 以喉鸣为首发症状儿童喉结核1例并文献复习[J]. 山东大学耳鼻喉眼学报, 2024, 38(1): 51-53.
[3] 陈坤,陆慧,李磊,张帆,杨军,黄琦. 累及儿童多部位的浆细胞型Castleman病1例并文献复习[J]. 山东大学耳鼻喉眼学报, 2024, 38(1): 54-58.
[4] 任晓勇. 阻塞性睡眠呼吸暂停合并症的研究现状和未来展望[J]. 山东大学耳鼻喉眼学报, 2023, 37(6): 1-5.
[5] 胡婷婷,王越华. 阻塞性睡眠呼吸暂停与乳腺癌相关性研究进展[J]. 山东大学耳鼻喉眼学报, 2023, 37(6): 41-45.
[6] 朱雅欣,刘峰,关建,殷善开. 儿童扁桃体腺样体肥大组织淋巴细胞改变的研究进展[J]. 山东大学耳鼻喉眼学报, 2023, 37(6): 62-67.
[7] 王璐,张云云,郭华,崔小川. 脂质代谢标志物在早期筛查阻塞性睡眠呼吸暂停患者中阿尔兹海默症的应用价值[J]. 山东大学耳鼻喉眼学报, 2023, 37(6): 68-74.
[8] 王磊,李保卫,王刚,刘红丹,韩浩伦,张晓丽,吴玮. 阻塞性睡眠呼吸暂停低通气综合征患者夜间碱反流初步研究[J]. 山东大学耳鼻喉眼学报, 2023, 37(6): 75-79.
[9] 王腾,余林,李穗. 血清γ-谷氨酰基转移酶与阻塞性睡眠呼吸暂停综合征伴发高血压的相关性研究[J]. 山东大学耳鼻喉眼学报, 2023, 37(6): 80-84.
[10] 袁钰淇,曹子讷,牛晓欣,谢雨杉,苏永龙,朱思敏,张一彤,刘海琴,任晓勇,施叶雯. 外周血炎症指标在阻塞性睡眠呼吸暂停低通气综合征伴高血压中的临床意义[J]. 山东大学耳鼻喉眼学报, 2023, 37(6): 85-92.
[11] 王钰彧,朱梅. 体位性睡眠呼吸暂停患者的临床特点及其与低觉醒阈值的相关性研究[J]. 山东大学耳鼻喉眼学报, 2023, 37(6): 101-105.
[12] 邢亮,袁钰淇,谢雨杉,苏永龙,牛晓欣,麻莉娜,王子桐,刘海琴,施叶雯,任晓勇. 不同觉醒阈值阻塞性睡眠呼吸暂停低通气患者对血气、糖脂代谢水平的影响[J]. 山东大学耳鼻喉眼学报, 2023, 37(6): 106-111.
[13] 张玉焕,张俊波,尹国平,袁雪梅,曹鑫,孙宇,陈强,叶京英. 鼾症患者前后半夜多导睡眠监测参数的对比分析[J]. 山东大学耳鼻喉眼学报, 2023, 37(6): 112-117.
[14] 宗春琳,陆金标,杨勇,苏忠平,于擘. MDT联合翻转课堂在儿童OSA临床教学中的应用研究[J]. 山东大学耳鼻喉眼学报, 2023, 37(6): 139-144.
[15] 袁晨阳,冯晨,王岩,李延忠,房振胜. 儿童阻塞性睡眠呼吸暂停相关血液学指标的临床研究[J]. 山东大学耳鼻喉眼学报, 2023, 37(6): 145-152.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] 马宝峰,党光福 . 折叠式人工晶状体经巩膜缝线固定术16例[J]. 山东大学耳鼻喉眼学报, 2008, 22(4): 376 -378 .
[2] 封新荣,张红霞,许贞姬,曲君君 . 射频消融术治疗慢性肥厚性鼻炎[J]. 山东大学耳鼻喉眼学报, 2006, 20(3): 225 -226 .
[3] 张秋贵,何海燕 . 青岛地区变应性鼻炎常见变应原调查分析[J]. 山东大学耳鼻喉眼学报, 2006, 20(6): 520 -521 .
[4] 李海燕,赵秋良,韩晓攀,于丽 . 阻塞性睡眠呼吸暂停综合征患者自由基损伤与高血压的关系[J]. 山东大学耳鼻喉眼学报, 2006, 20(6): 527 -529 .
[5] 宋西成,张华,张庆泉 . 血循环DNA的检测与头颈部肿瘤的诊断[J]. 山东大学耳鼻喉眼学报, 2006, 20(5): 385 -387 .
[6] 詹善强,孔凡俐,操文娟,汤杰,沈菊,詹文波 . 电刀切除扁桃体236例[J]. 山东大学耳鼻喉眼学报, 2006, 20(6): 559 -560 .
[7] 张 虎,艾 琴 . 甲状腺癌再手术15例[J]. 山东大学耳鼻喉眼学报, 2007, 21(2): 166 -167 .
[8] 胡 明,李招娜,李云杰,陶祥臣 . 硬性透气性角膜接触镜对圆锥角膜的塑形作用[J]. 山东大学耳鼻喉眼学报, 2007, 21(2): 171 -173 .
[9] 娄 锋 . 喉上神经阻滞预防喉显微手术心血管反应[J]. 山东大学耳鼻喉眼学报, 2008, 22(2): 173 -175 .
[10] 陈伟雄 王跃建 曾勇 何发尧 张剑利 郑立岗 汤苏成. 胸骨上小切口无注气内镜甲状腺手术的临床体会[J]. 山东大学耳鼻喉眼学报, 2009, 23(4): 24 -26 .
网站版权所有 © 2018 《山东大学耳鼻喉眼学报》编辑部　
地址：山东省济南市山大南路27号（250100）电话：+86-0531-88366912 E-mail: ebhxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发