A Study on Sleep Staging Algorithm for Patients with Sleep Apnea Syndrome
Lu Keke1,2,3, Qi Xia1,2,3, Zhang Jianbao1,2,3, Wang Gang1,2,3#*, Yan Xiangguo 1,2,3#*
1(Institute of Health and Rehabilitation Science, School of Life Science and Technology,The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi'an Jiaotong University, Xi'an 710049, China) 2(National Engineering Research Center for Healthcare Devices, Guangzhou 510500, China) 3(The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an 710049, China)
Abstract：Aiming at the lack of effective automatic sleep staging method for wearable sleep monitoring, an automatic sleep staging algorithm suitable for patients with sleep apnea syndrome was proposed in this paper. Through the R-R interval sequence of an ECG signal, the signals of heart rate variability (HRV), respiratory amplitude variability (RAV) and respiratory rate variability (RRV) were obtained. On this basis, 55 features of time, frequency, and nonlinear domains were extracted. Four sleep staging models with different classification granularity were constructed by using gated circulation unit network: W-S two classification, W-REM-NREM three classification, W-REM-LS-SWS four classification, W-REM-N1-N2-N3 five classification. A categorical weighting method for the loss function was used to effectively reduce the impact of data imbalance on the sleep staging results. The accuracy, Cohen's kappa coefficient and sleep structure indexes were used to evaluate the performance of the sleep staging algorithm. The results showed that the values of accuracy and kappa of the four classifiers were 85.06%, 75.44%, 63.80%, 62.13%, and 0.54, 0.49, 0.41, 0.41, respectively, and the sleep structure analyzing showed there was no statistically significant difference from clinical results. The method proposed displayed the ability of meeting the needs of sleep quality evaluation and is expected to be used for wearable sleep monitoring applications.
作者简介: #中国生物医学工程学会会员(Member, Chinese Society of Biomedical Engineering)
引用本文:
鲁柯柯, 祁霞, 张建保, 王刚, 闫相国. 睡眠呼吸暂停综合征患者的睡眠分期算法研究[J]. 中国生物医学工程学报, 2022, 41(3): 273-281.
Lu Keke, Qi Xia, Zhang Jianbao, Wang Gang, Yan Xiangguo. A Study on Sleep Staging Algorithm for Patients with Sleep Apnea Syndrome. Chinese Journal of Biomedical Engineering, 2022, 41(3): 273-281.
[1] Redmond SJ, Heneghan C. Cardiorespiratory-based sleep staging in subjects with obstructive sleep apnea [J]. IEEE Transactions on Biomedical Engineering, 2006, 53(3): 485-496. [2] 黄平, 高兴林. 阻塞性睡眠呼吸暂停低通气综合征与心血管疾病[J]. 中国临床保健杂志, 2006, 9(6): 529-530. [3] Berry RB, Budhiraja R, Gottlieb DJ, et al. Rules for scoring respiratory events in sleep: update of the 2007 AASM manual for the scoring of sleep and associated events[J]. Journal of Clinical Sleep Medicine, 2012, 8(5): 597-619. [4] Penzel T, Lo C, Ivanov PC, et al. Analysis of sleep fragmentation and sleep structure in patients with sleep apnea and normal volunteers[C]// 2005 27th Annual Conference IEEE-EMBC. Shanghai: IEEE, 2005: 2591-2594. [5] Masdeu MJ, Ayappa I, Hwang D, et al. Impact of clinical assessment on use of data from unattended limited monitoring as opposed to full-in lab PSG in sleep disordered breathing [J]. Journal of Clinical Sleep Medicine, 2010, 6(1): 51-58. [6] Yoon H, Hwang SH, Choi JW, et al. REM sleep estimation based on autonomic dynamics using R-R intervals[J]. Physiol Meas, 2017, 38(4):631-651. [7] Bunde A. Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea[J]. IEEE Transactions on Biomedical Engineering, 2003,50(10):1143-1151. [8] Wang J, Shih G, Chiang W, et al. Sleep stage classification of sleep apnea patients using decision-tree-based support vector machines based on ECG parameters[C] // Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics. Hong Kong: IEEE, 2012: 285-288. [9] Willemen T, Varon C, Caicedo Dorado A, et al. Probabilistic cardiac and respiratory based classification of sleep and apneic events in subjects with sleep apnea[J]. Physiological Measurement, 2015, 36(10): 2103-2118. [10] 黄文汉,张伟,胡立刚,周陈旺,向丹阳.基于心电与呼吸信号的睡眠分期算法研究[J]. 智能计算机与应用, 2018, 8(1): 49-54. [11] Fonseca P, Niek DT, Long X, et al. A comparison of probabilistic classifiers for sleep stage classification [J]. Physiological Measurement, 2018, 39(5): 55001. [12] Roth T. Predictors of objective level of daytime sleepiness in patients with sleep-related breathing disorders [J]. Chest, 1989, 95(6):1202-1206. [13] Thomas R. Obstructive Sleep apnea alters sleep stage transition dynamics[J]. PLoS ONE, 2010, 5(6):e11356. [14] Wei Y, Qi X, Wang H, et al. A multi-class automatic sleep staging method based on long short-term memory network using single-lead electrocardiogram signals [J]. IEEE Access, 2019, 7: 85959-85970. [15] Heneghan C. St. Vincent's University Hospital/Univeristy College Dublin Sleep Apnea Database[EB/OL]. https://www.physionet.org/physiobank/database/ucddb/, 2011-09-01/2021-8-10. [16] Doris M, Peter A, Georg G, et al. Sleep classification according to AASM and Rechtschaffen & Kales: effects on sleep scoring parameters [J]. Sleep, 2009, 2(32): 139-149. [17] Álvarez RA, Penín AJM, Sobrino XAV. A comparison of three QRS detection algorithms over a public database [J]. Procedia Technology, 2013, 9: 1159-1165. [18] Malik M. Heart rate variability: Standards of measurement, physiological interpretation, and clinical use [J]. Annals of Noninvasive Electrocardiology, 1996, 93(5):1043-1065. [19] 张兴华. 基于心电信号和深度神经网络的睡眠分期研究[D].天津:天津工业大学,2018. [20] Peng CK. Multiscale entropy analysis of complex physiologic time series [J]. Physical Review Letters, 2007, 89(6): 705-708. [21] Kantelhardt J W, Zschiegner S, Koscielnybunde E, et al. Multifractal detrended fluctuation analysis of nonstationary time series[J]. Physica A-statistical Mechanics and Its Applications, 2002, 316(1): 87-114. [22] Srivastava N, Hinton G E, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1): 1929-1958. [23] Bull A D. Convergence rates of efficient global optimization algorithms [J]. Journal of Machine Learning Research, 2011, 12(12): 2879-2904. [24] Cohen J. Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit [J]. Psychological Bulletin, 1968, 70(4): 213-220. [25] Shambroom JR, Fabregas SE, Johnstone J. Validation of an automated wireless system to monitor sleep in healthy adults [J]. Journal of Sleep Research, 2012, 21(2): 221-230. [26] Dankerhopfe H, Kunz D, Gruber G, et al. Interrater reliability between scorers from eight European sleep laboratories in subjects with different sleep disorders [J]. Journal of Sleep Research, 2004, 13(1): 63-69. [27] Mann HB, Whitney DR . On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other[J]. Annals of Mathematical Statistics, 1947, 18(1):50-60. [28] Goldberger A, Amaral L, 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.
