基于LSTM-CNN的睡眠呼吸暂停与低通气事件实时检测算法研究

doi:10.3969/j.issn.0258-8021.2020.03.07

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Abstract Sleep apnea and hypopnea syndrome (SAHS) is a potentially fatal disease as well as affects sleep quality. In order to balance the accuracy and time resolution of apnea and hypopnea (AH) event detection, this paper proposed a long-short term memory-convolutional neural network (LSTM-CNN) to predict AH event precisely, meanwhile an apnea-hypopnea index (AHI) estimation method based on event detection results was adopted to quantitatively assess the SAHS severity. The algorithm was tested using 54 subjects’ abdomen movement signals from National Heart Lung & Blood Institute. For over 900, 000 data fragments after preprocessing, the accuracy, sensitivity and specificity were 88.6%, 88.2% and 88.7% respectively. For the 54 subjects’ AHI, the Pearson correlation coefficient between estimated AHI and AHI scored from polysomnography (PSG) reached 0.98 and the Cohen's kappa coefficient for SAHS severity was 0.95. The results showed that this method not only realized a high-precision detection of AH event, but also accurately estimated the AHI and the severity of SAHS, therefore held the potential to be used for SAHS diagnosis before PSG or long-term monitoring of SAHS at home.

Key words： long-short term memory convolutional neural network sleep apnea and hypopnea syndrome polysomnography

Received: 19 April 2019

PACS:

R318

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	Articles by authors
	Yu Hui
	Wang Shuo
	Li Xinrui
	Deng Chenyang
	Sun Jinglai
	Zhang Lixin
	Cao Yuzhen

Cite this article:

Yu Hui,Wang Shuo,Li Xinrui, et al. Algorithm Study of Real-Time Detection of Sleep Apnea-Hypopnea Event Based on Long-Short Term Memory-Convolutional Neural Network[J]. Chinese Journal of Biomedical Engineering, 2020, 39(3): 303-310.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2020.03.07 OR http://cjbme.csbme.org/EN/Y2020/V39/I3/303

[1] Jordan AS, Mcsharry DG, Malhotra A. Adult obstructive sleep apnoea [J]. Lancet, 2014, 383(9918): 736-747.
[2] Young T, Palta M, Dempsey J, et al. The occurrence of sleep-disordered breathing among middle-aged adults [J]. The New England Journal of Medicine, 1993, 328(17): 1230-1235.
[3] Mcnames JN, Fraser AM. Obstructive sleep apnea classification based on spectrogram patterns in the electrocardiogram[C]//Computers in Cardiology. Cambridge:IEEE, 2000:749-752.
[4] Timus O, Dogru Bolat E. k-NN-based classification of sleep apnea types using ECG [J]. Turkish Journal of Electrical Engineering and Computer Sciences, 2017, 25(4): 3008-3023.
[5] 周洪建, 黄大同. 利用ECG信号检测睡眠呼吸暂停的小波包分析方法 [J]. 中国医学物理学杂志, 2003, 20(4): 71-72, 84.
[6] Choi SH, Yoon H, Kim HS, et al. Real-time apnea-hypopnea event detection during sleep by convolutional neural networks [J]. Computers in Biology and Medicine, 2018, 100: 123-131.
[7] Gutierrez-Tobal GC, Alvarez D, Del Campo F, et al. Utility of AdaBoost to detect sleep apnea-hypopnea syndrome from single-channel airflow [J]. IEEE Trans Biomed Eng, 2016, 63(3): 636-646.
[8] Lee H, Park J, Kim H, et al. New rule-based algorithm for real-time detecting sleep apnea and hypopnea events using a nasal pressure signal [J]. Journal of Medical Systems, 2016, 40(12): 282.
[9] Nakano H, Tanigawao T, Furukawa T, et al. Automatic detection of sleep-disordered breathing from a single-channel airflow record [J]. European Respiratory Journal, 2007, 29(4): 728-736.
[10] Jung DW, Hwang SH, Cho JG, et al. Real-time automatic apneic event detection using nocturnal pulse oximetry [J]. IEEE Trans Biomed Eng, 2018, 65(3): 706-712.
[11] Sola-Soler J, Antonio Fiz J, Morera J, et al. Multiclass classification of subjects with sleep apnoea-hypopnoea syndrome through snoring analysis [J]. Medical Engineering & Physics, 2012, 34(9): 1213-1220.
[12] 陈伟伟. 基于鼾声检测的睡眠呼吸暂停低通气综合症诊断 [D]; 大连:大连理工大学, 2010.
[13] Huang W, Guo B, Shen Y, et al. A novel method to precisely detect apnea and hypopnea events by airflow and oximetry signals [J]. Computers in Biology and Medicine, 2017, 88: 32-40.
[14] Xie B, Minn H. Real-time sleep apnea detection by classifier combination [J]. IEEE Transactions on Information Technology in Biomedicine, 2012, 16(3): 469-477.
[15] Bsoul M, Minn H, Tamil L. Apnea MedAssist: Real-time sleep apnea monitor using single-lead ECG [J]. IEEE Transactions on Information Technology in Biomedicine, 2011, 15(3): 416-427.
[16] Hoa Dinh N, Wilkins BA, Cheng Q, et al. An online sleep apnea detection method based on recurrence quantification analysis [J]. IEEE Journal of Biomedical and Health Informatics, 2014, 18(4): 1285-1293.
[17] 金海龙, 张志慧. 基于希尔伯特-黄变换和BP神经网络的运动想象脑电研究 [J]. 生物医学工程学杂志, 2013, 30(2): 249-253.
[18] 魏琛, 陈兰岚, 张傲. 基于集成卷积神经网络的脑电情感识别 [J]. 华东理工大学学报(自然科学版), 2019, 45(4):614-622.
[19] 余明, 陈锋, 张广, 等. 应用遗传算法优化神经网络的致死性心电节律辨识算法研究 [J]. 生物医学工程学杂志, 2017, 34(3): 421-430.
[20] Zhang GQ, Cui L, Mueller R, et al. The National Sleep Research Resource: Towards a sleep data commons [J].JAMIA, 2018, 25(10): 1351-1358.
[21] Dean DA, Goldberger AL, Mueller R, et al. Scaling up scientific discovery in sleep medicine: The National Sleep Research Resource [J]. Sleep, 2016, 39(5): 1151-1164.
[22] Graves A. Long short-term memory [J]. Neural Computation, 1997, 9(8): 1735-1780.
[23] 米切尔.机器学习[M].北京:机械工业出版社, 2003:276-278.
[24] Yu H, Deng C, Sun J, et al. Cascading detection model for prediction of apnea-hypopnea events based on nasal flow and arterial blood oxygen saturation [J]. Sleep & Breathing, 2019, 7(5):1-8.