[1] [1] AINGE-ALLEN HW, YEE BJ, IP MSM, et al. Contemporary concise review 2020: sleep[J]. Respirology, 2021, 26(7): 700-706.

[2] [2] CHANG HY, YEH CY, LEE CT, et al. A sleep apnea detection system based on a one-dimensional deep convolution neural network model using single-lead electrocardiogram [J]. Sensors (Basel), 2020, 20(15): 4157. DOI: 10.3390/s20154157.

[3] [3] MASSIE F, VITS S, KHACHATRYAN A, et al. Central sleep apnea detection by means of finger photoplethysmography [J]. IEEE J Transl Eng Health Med, 2023, 11: 126-136.

[4] [4] SUN Y, ZHANG K, SUN C. Model-based transfer reinforcement learning based on graphical model representations [J]. IEEE Trans Neural Netw Learn Syst, 2023, 34(2): 1035-1048.

[5] [5] ZHANG Y, LI H, DONG H, et al. Transfer learning algorithm design for feature transfer problem in motor imagery brain-computer interface [J]. China Commun, 2022, 19(2): 39-46.

[6] [6] AL GHUNAIMI B, HOSSEN A, HASSAN MO. Screening of obstructive sleep apnea based on statistical signal characterization of Hilbert transform of RRI data [J]. Technol Health Care, 2004, 12(1): 67-78.

[7] [7] TRIPATHY RK, RAJENDRA AU. Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework [J]. Biocybern Biomed Eng, 2018, 38(2): 890-902.

[8] [8] LAZAZZERA R, DEVIAENE M, VARON C, et al. Detection and classification of sleep apnea and hypopnea using PPG and SpO2 signals [J]. IEEE Trans Biomed Eng, 2021, 68(5): 1496-1506.

[9] [9] AYDOAN O, TER A, GNEY K, et al. Automatic diagnosis of obstructive sleep apnea/hypopnea events using respiratory signals [J]. J Med Syst, 2016, 40(12): 274. DOI: 10.1007/s10916-016-0624-0.

[10] [10] PENZEL T, MOODY GB, MARK RG, et al. The apnea-ECG database: Computers in cardiology [C]. Piscataway, USA: IEEE, 2000: 255-258.

[11] [11] ICHIMARU Y, MOODY GB. Development of the polysomnographic database on CD-ROM [J]. Psychiatry Clin Neurosci, 1999, 53(2): 175-177.

[12] [12] 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.

[13] [13] AHMAD S, BATKIN I, KELLY O, et al. Multiparameter physiological analysis in obstructive sleep apnea simulated with mueller maneuver[J]. IEEE T Instrum Meas, 2013, 62(10): 2751-2762.

[14] [14] MORA GG, KORTELAINEN JM, HERNANDEZ ERP, et al. Evaluation of pressure bed sensor for automatic SAHS screening [J]. IEEE T Instrum Meas, 2015, 64(7): 1935-1943.

[15] [15] NAKAYAMA C, FUJIWARA K, SUMI Y, et al. Obstructive sleep apnea screening by heart rate variability-based apnea/normal respiration discriminant model [J]. Physiol Meas, 2019, 40(12): 125001. DOI: 10.1088/1361-6579/ab57be.

[16] [16] HOPPENBROUWER X, FABIUS T, EIJSVOGEL M, et al. Airflow from nasal pulse oximetry in the screening of obstructive sleep apnea [C]. 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019: 2572-2575.

[17] [17] LI K, PAN W, LI Y, et al. A method to detect sleep apnea based on deep neural network and hidden markov model using single-lead ECG signal [J]. Neurocomputing, 2018, 294(14): 94-101.

[18] [18] NASSI TE, GANGLBERGER W, SUN H, et al. Automated scoring of respiratory events in sleep with a single effort belt and deep neural networks [J]. IEEE T Bio-Med Eng, 2022, 69(6): 2094-2104.

[19] [19] NIKKONEN S, KORKALAINEN H, LEINO A, et al. Automatic respiratory event scoring in obstructive sleep apnea using a long short-term memory neural network[J]. IEEE J Biomed Health Inform, 2021, 25(8): 2917-2927.