A review of automated sleep stage based on EEG signals

• Autorzy: Zhang Xiaoli , Zhang Xizhen , Huang Qiong , Lv Yang , Chen Fuming

Identyfikatory

DOI

10.1016/j.bbe.2024.06.004

• Języki publikacji: EN

Abstrakty

EN

Sleep disorders have increasingly impacted healthy lifestyles. Accurate scoring of sleep stages is crucial for diagnosing patients with sleep disorders. The precision of sleep staging differs notably between healthy individuals and those with sleep apnea (SA). SA disrupts the regularity of sleep stages, affecting the performance of sleep stage detection and influencing the accuracy of sleep staging, thereby impacting sleep quality assessment results. The study compares the accuracy of sleep staging between healthy individuals and SA patients using the same algorithm, revealing variations in performance based on different severities of sleep apnea. This suggests limitations in the generalization ability of current sleep staging methods. Accordingly, researchers are working to develop sleep staging methods that can diminish the impact of sleep apnea and exhibit better generalization capabilities. Furthermore, the study emphasizes the advantages of automated methods over manual scoring due to being less subjective and resource-intensive, making them more suitable for practical applications. The emphasis is on recent research findings on automatic sleep stage classification based on electroencephalography (EEG). The study outlines potential applications and distinctions of various algorithm models rooted in machine learning and deep learning within the context of sleep staging. These methods are applied to the well-known public EEG dataset Sleep-EDF. The study applies four widely studied algorithms to the single-channel EEG of 20 subjects, comparing the results of the models’ automatic sleep staging with the manual sleep staging annotations by clinical experts.

Słowa kluczowe

EN

EEG signal; automatic sleep staging; deep learning; clinical diagnosis

PL

sygnał EEG; uczenie głębokie; diagnoza kliniczna

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2024
• Tom: Vol. 44, no. 3
• Strony: 651--673
• Opis fizyczny: Bibliogr. 219 poz., rys., tab., wykr.

Twórcy

autor

Zhang Xiaoli

• Medical Security Center, The 940th Hospital of Joint Logistics Support Force of Chinese People’s Liberation Army, Lanzhou, Gansu 730050, China
• Department of Biomedical Engineering, School of Information Engineering, Gansu University of Traditional Chinese Medicine, China

autor

Zhang Xizhen

• Medical Security Center, The 940th Hospital of Joint Logistics Support Force of Chinese People’s Liberation Army, Lanzhou, Gansu 730050, China
• Department of Biomedical Engineering, School of Information Engineering, Gansu University of Traditional Chinese Medicine, China

autor

Huang Qiong

• Medical Security Center, The 940th Hospital of Joint Logistics Support Force of Chinese People’s Liberation Army, Lanzhou, Gansu 730050, China

autor

Lv Yang

• Medical Security Center, The 940th Hospital of Joint Logistics Support Force of Chinese People’s Liberation Army, Lanzhou, Gansu 730050, China

autor

Chen Fuming

• Medical Security Center, The 940th Hospital of Joint Logistics Support Force of Chinese People’s Liberation Army, Lanzhou, Gansu 730050, China

Bibliografia

• [1] Czeisler CA. Duration, timing and quality of sleep are each vital for health, performance, and safety. Sleep Health 2015;1(1):5-8.
• [2] Ge L, Guyatt G, Tian J, Pan B, Chang Y, Chen Y, et al. Insomnia and risk of mortality from all-cause, cardiovascular disease, and cancer: Systematic review and meta-analysis of prospective cohort studies. Sleep Med Rev 2019;48:101215.
• [3] You Y, Zhong X, Liu G, Yang Z. Automatic sleep stage classification: A light and efficient deep neural network model based on time, frequency and fractional Fourier transform domain features. Artif Intell Med 2022;127:102279.
• [4] Chapotot F, Becq G. Automated sleep-wake staging combining robust feature extraction, artificial neural network classification, and flexible decision rules. Int J Adapt Control Signal Process 2010;24(5):409423.
• [5] Ebrahim K, Babak AM. Automatic sleep stage classification using temporal convolutional neural network and new data augmentation technique from raw single-channel EEG. Comput Methods Programs Biomed 2021:204106063.
• [6] Huang Z, Chen Y, Fu XZJ, Tang X. Study on the risk and influencing factors of obstructive sleep apnea syndrome in patients with different stages of chronic kidney disease. Western Med 2019;31(11):1700-3.
• [7] Zhang R, Wang HH, Yu LL, Liu L, Sun JJ, Li JR, et al. Comparison of clinical application of polysomnography and portable sleep monitoring. J Otorhinolaryngol Shandong Univ 2016;30(5):65-7.
• [8] Hui L, Da Tian Y, Cheng P. Development and Design of portable sleep electroencephalogram monitoring system. J Biomed Eng 2015;32(3). pp. 548-552+557.
• [9] Widmann A, Schröger E, Maess B. Digital filter design for electrophysiological data - a practical approach. J Neurosci Methods 2015:25034-46.
• [10] Sharma M, Goyal D, Achuth PV, Acharya UR. An accurate sleep stages classification system using a new class of optimally time-frequency localized three-band wavelet filter bank. Comput Biol Med 2018;98:58-75.
• [11] Mahajan R, Morshed BI. Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy, kurtosis, and wavelet-ICA. IEEE J Biomed Health Inform 2015;19(1):158-65.
• [12] Zhang H, Zhao M, Wei C, Mantini D, Li Z, Liu Q. EEGdenoiseNet: a benchmark dataset for deep learning solutions of EEG denoising. J Neural Eng 2021;18(5): 056057.
• [13] Tao YJ, Yang Y. Efficient automatic sleep staging method based on original single-channel electroencephalogram. J Zhengzhou Univ (Nat Sci Ed) 2022;54(3):5.
• [14] Xu L, Wu YX, Xiao B, Xu Z, Zhang Y. One-dimensional convolutional neural network for children’s sleep staging. Chinese J Eng 2021;43(9):1253-60.
• [15] Liu JB, Wang B, Gu JF. Sleep staging method based on data generation. Comput Eng Des 2022;43(2):406-12.
• [16] Luo SL, Hao JW, Pan LM. Automatic sleep staging method based on CNN-BiLSTM. J Beijing Univ Technol 2020;40(07):746-52.
• [17] Zhang HM, Wang Z, Liao XJ, Li WD. A dual-layer sleep classification model based on multiple features. J Huazhong Univ Sci Technol (Nat Sci Edit) 2024;52(05): 64-9.
• [18] Zhou JJ, Wang GS, Liu JB, Wu DP, Xu WF. Automatic Sleep Stage Classification With Single Channel EEG Signal Based on Two-Layer Stacked Ensemble Model. IEEE Access 2020:857283-897.
• [19] Feng LY, Yao L, Zhao XJ. Research on sleep EEG data enhancement model with Huber loss function. J Beijing Normal University (Natural Science,)2021,57(06): 875-882.
• [20] Ling H, Luyuan Y, Xinxin L, Bingliang D. Staging study of single-channel sleep EEG signals based on data augmentation#13. Front Public Health 2022: 101038742.
• [21] Gong Y, Wang F, Lv Y, Liu C, Li TX. Automatic sleep staging using BiRNN with data augmentation and label redirection. Electronics 2023;12(11).
• [22] Yin HR. EEG enhancement based on MHA-GAN improves the class imbalance problem in sleep staging. Network New Media Technology 2023;12(06):28-35.
• [23] Cheng X, Huang K, Zou Y, Shujie M. SleepEGAN: A GAN-enhanced ensemble deep learning model for imbalanced classification of sleep stages. Biomed Signal Process Control 2024.
• [24] Jain R, G RA. Modality-Specific Feature Selection, Data Augmentation and Temporal Context for Improved Performance in Sleep Staging. IEEE J Biomed Health Inform. 2024,28(2).
• [25] Hori T, Sugita Y, Koga E et al. Proposed supplements and amendments to ‘A Manual of Standardized Terminology, Techniques, and Scoring System for Sleep Stages of Human Subjects,’ the Rechtschaffen & Kales (1968) standard. Psychiatry Clin Neurosci 2001;55(3): 305-310.
• [26] Berry RB, Budhiraja R, Gottlieb DJ, Gozal D, Iber C, Kapur VK, et al. The American Academy of Sleep Medicine (AASM) manual for the scoring of sleep and associated events: rules, terminology and technical specifications. J Clin Sleep Med 2012;8(5):597-619.
• [27] Ebrahimi F, Setarehdan S, Ayala-Moyeda J, Nazeran H. Automatic sleep staging using empirical mode decomposition, discrete wavelet transform, time-domain, and nonlinear dynamics features of heart rate variability signals. Comput Methods Programs Biomed 2013;112(1):47-57.
• [28] Seifpour S, Niknazar H, Mikaeili M, Nasrabadi A. A new automatic sleep staging system based on statistical behavior of local extrema using single channel EEG signal[J]. Expert Syst Appl 2018:104277-10493.
• [29] Jeon Y, Kim S, Choi H, Chung YG, Ah CS, Kim H, et al. Pediatric sleep stage classification using multi-domain hybrid neural networks. IEEE Access 2019: 796495-7505.
• [30] Panteleimon C, Frantzidis CA, Gkivogkli PT, Bamidis PD, Kourtidou-Papadeli C. Automatic sleep staging employing convolutional neural networks and cortical connectivity images. IEEE Trans Neural Networks Learn Syst 2020;31(1):113-23.
• [31] Sun C, Chen C, Li W, et al. A hierarchical neural network for sleep stage classification based on comprehensive feature learning and multi-flow sequence learning. IEEE J Biomed Health Inform 2020;24(5):1351-66.
• [32] Li LT, Miao MM, Hu WJ. Sleep staging method based on multi-feature fusion and cascade forest. Inform Technolo Informatization 2023;04:4-9.
• [33] Chen T. Research on automatic sleep staging based on frequency domain analysis and statistical calculation. Electron Pract 2021;12:91-4.
• [34] Satapathy SK, Brahma B, Panda B, Barsocchi P, Bhoi AK. Machine learning-empowered sleep staging classification using multi-modality signals. BMC Med Inf Decis Making 2024;24(1):119.
• [35] Gao QX, Wu K. Automatic sleep staging based on power spectral density and random forest. J Biomed Eng 2023;40(2). pp. 280-285+294.
• [36] Liu G, Liu HY, Shi JL, Wang GJ, Hu MX, Wang WD. Sleep staging method based on feature extraction of EEG signal. Chinese Med Equip J 2021;42(1):1-8.
• [37] Yu X, Liu CC, Dai JF, Li J, Wang J, Hou FZ. Epilepsy electroencephalogram signal analysis based on improved K-nearest neighbor network. J Biomed Eng 2016;33(6):7.
• [38] Zhang Z, Zhou XC, Zhao HH, Zhang XH. Automatic sleep staging based on EEG signal feature extraction and SVM algorithm. Comput Digit Eng 2022;50(05): 936-41.
• [39] L S A, M Z L B, Kaspar S, Athina. Neural complexity and the spectral slope characterize auditory processing in wakefulness and sleep. Eur J Neurosci 2023; 59(5):822-841.
• [40] Jagannathan RS, Jeans T, Poll D V N M, Swinderen B V. Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies. Sci Adv 2024;10(8):eadj4399-eadj4399.
• [41] Hassan RA, Bhuiyan HIM. Automatic sleep scoring using statistical features in the EMD domain and ensemble methods. Biocybernet Biomed Eng 2016;36(1): 248-55.
• [42] Tian P, Hu J, Qi J, Ye X, Che DT, Ding Y, et al. A hierarchical classification method for automatic sleep scoring using multiscale entropy features and proportion information of sleep architecture. Biocybernet Biomed Eng 2017;37(2):263-71.
• [43] Tripathy R, Acharya RU. Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework. Biocybernet Biomed Eng 2018;38(4):890-902.
• [44] KGG, Prabhu SS, Sinha N. Sleep EEG analysis utilizing inter-channel covariance matrices. Biocybernet Biomed Eng 2020;40(1):527-545.
• [45] Geng D, Yang D, Cai M, Zheng LX. A novel microwave treatment for sleep disorders and classification of sleep stages using multi-scale entropy. Entropy 2020;22(3):347.
• [46] Dakhale BJ, Sharma M, Arif M, Kushagra A, Ankit AB, Ashwin G, et al. An automatic sleep-scoring system in elderly women with osteoporosis fractures using frequency localized finite orthogonal quadrature Fejer Korovkin kernels. Med Eng Phys 2023;112:103956.
• [47] Eris VT, Hiemstra FW, Cramer ABG, Verbruggen SCAT, Tax David MJ, Joosten K, et al. An EEG-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children. J Clin Sleep Med: JCSM: Official Publication of the Am Acad Sleep Med 2024;20(3):389-97.
• [48] Sharma M, Yadav A, Tiwari J, Karabatak M, Yildirim O, Acharya UR. An automated wavelet-based sleep scoring model using EEG, EMG, and EOG signals with more than 8000 subjects. Int J Environ Res Public Health 2022;19:7176.
• [49] Lal U, Vasanthsena MS, Hoblidar A. Temporal feature extraction and machine learning for classification of sleep stages using telemetry polysomnography. Brain Sci 2023;13(8):1201.
• [50] Ting C, Zhiwei L, Heng D, Jingyun S, Yilun F, Junmeng L. A sleep staging model for the sleep environment control based on machine learning. Build Simul 2023; 16(8):1409-23.
• [51] Hyewon H, Jae MS, Janghun H, Eunyeon J, Junhyoung O. Classification and automatic scoring of arousal intensity during sleep stages using machine learning. Sci Rep 2024;14(1):5983.
• [52] Hamouda BG, Rejeb L, Said BL. Ensemble learning for multi-channel sleep stage classification. Biomed Signal Process Control 2024;93106184.
• [53] Radhakrishnan BL, Ezra K, Jebadurai IJ, Selvakumar I, Karthikeyan P. An autonomous sleep-stage detection technique in disruptive technology environment. Sensors (Basel) 2024;24(4):1197.
• [54] Yuan W, Xiang W, Si K, Yang C, Zhao L, Li JQ, et al. Multi-channel EEG-based sleep staging using brain functional connectivity and domain adaptation. Physiol Meas 2023;44(10).
• [55] Adrián M, Pablo A, Eduardo G, Leila K, Daniel Á, Jesús L, et al. Pediatric sleep apnea: Characterization of apneic events and sleep stages using heart rate variability. Comput Biol Med 2023:154106549.
• [56] Chaewon K, Sora A, Jin HK, Devi M, Cho A, Hwang S, et al. Age-integrated artificial intelligence framework for sleep stage classification and obstructive sleep apnea screening. Front Neurosci 2023:171059186.
• [57] Rashidi S, Asl BM. Strength of ensemble learning in automatic sleep stages classification using single-channel EEG and ECG signals. Med Biol Eng Compu 2024;62(4):997-1015.
• [58] Ma N, Wu Z, Cheung YM, Guo Y, Gao Y, Li J, et al. A survey of human action recognition and posture prediction. Tsinghua Sci Technol 2022;27(6):973-1001.
• [59] Shankar V, Parsana S. An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing. J Acad Mark Sci 2022;50(6):1324-50.
• [60] Soleimani R, Barahona J, Chen YH, Bozkurt A, Daniele M, Pozdin V, et al. Advances in modeling and interpretability of deep neural sleep staging: a systematic review. Physiologia 2023;1:1-42.
• [61] Ji XY. Research on single-channel EEG automatic sleep staging method based on deep migration learning M.S., Dalian University of Technology; 2022.
• [62] Shi WJ, Wang J, Ning XJ, Lin YF. Meta-transfer learning sleep staging model in small sample scene. Comput Appl 2023:1-8.
• [63] Tsinalis O, Matthews PM, Guo Y. Automatic sleep stage scoring using time-frequency analysis and stacked sparse autoencoders. Ann Biomed Eng 2016;44(5):1587-97.
• [64] Humayun AI, Sushmit AS, Hasan T, Bhuiyan MIH. End-to-end sleep staging with raw single channel EEG using deep residual convents. CoRR 2019. abs/1904.10255.
• [65] Zhu T, Luo W, Yu F. Convolution- and attention-based neural network for automated sleep stage classification. Int J Environ Res Public Health 2020;17(11): 4152.
• [66] Sun S, Li C, Lv N, Zhang X, Yu Z, Wang H. Attention-based convolutional network for automatic sleep stage classification. Biomed Tech (Berl) 2021;66(4):335-43.
• [67] Zhang X, Xu M, Li Y, Su M, Xu Z, Wang C, et al. Automated multi-model deep neural network for sleep stage scoring with unfiltered clinical data. Sleep Breath 2020;24(2):581-90.
• [68] Perslev M, Jensen MH, Darkner S, Jennum PJ, Igel C. U-Time: a fully convolutional network for time series segmentation applied to sleep staging. CoRR 2019.
• [69] Sors A, Bonnet S, Mirek S, Vercueil L, Payen JF. A convolutional neural network for sleep stage scoring from raw single-channel EEG. Biomed Signal Process Control 2018;42(APR.):107-14.
• [70] Zhang J, Wu Y. Complex-valued unsupervised convolutional neural networks for sleep stage classification. Comput Methods Programs Biomed 2018:164181-91.
• [71] Phan H, Andreotti F, Cooray N, Chen OY, De Vos M. Joint classification and prediction CNN framework for automatic sleep stage classification. IEEE Trans Biomed Eng 2019;66(5):1285-96.
• [72] Jadhav P, Rajguru G, Datta D, Mukhopadhyay S. Automatic sleep stage classification using time-frequency images of CWT and transfer learning using convolution neural network. Biocybernet Biomed Eng 2020;40(1):494-504.
• [73] Horie K, Ota L, Miyamoto R, Abe T, Suzuki Y, Kawana F, et al. Automated sleep stage scoring employing a reasoning mechanism and evaluation of its explainability. Sci Rep 2022;12:12799.
• [74] Masad IS, Alqudah A, Qazan S. Automatic classification of sleep stages using EEG signals and convolutional neural networks. PLoS One 2024;19(1):e0297582.
• [75] Satapathy SK, Loganathan D. Automated classification of multi-class sleep stages classification using polysomnography signals: a nine-layer 1D-convolution neural network approach. Multimed Tools Appl 2022:1-43.
• [76] Dongdong Z, Jian W, Guoqiang H, Zhang JC, Li F, Yan R, et al. SingleChannelNet: A model for automatic sleep stage classification with raw single-channel EEG. Biomed Signal Process Control 2022;75:103592.
• [77] Haghayegh S, Hu K, Stone K, Redline S, Schernhammer E. Automated sleep stages classification using convolutional neural network from raw and time-frequency electroencephalogram signals: systematic evaluation study. J Med Internet Res 2023;25:e40211.
• [78] Yang X, Wu ZN, Qian SR. Study on sleep staging based on bidirectional recurrent neural network in single channe EEG. Microcomputer Application 2017;33(1): 42-5.
• [79] Phan H, Andreotti F, Cooray N, Chén O Y, Vos M D. Automatic Sleep Stage Classification Using Single-Channel EEG: Learning Sequential Features with Attention-Based Recurrent Neural Networks. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference,2018,20181452-1455.
• [80] Choi SH, Kwon HB, Jin HW, Yoon H, Lee MH, Lee YJ, et al. Long short-term memory networks for unconstrained sleep stage classification using polyvinylidene fluoride film sensor. IEEE J Biomed Health Inform 2020;24(12): 3606-15.
• [81] Li R, Wang B, Zhang T, Sugi T. A developed LSTM-ladder-network-based model for sleep stage classification. IEEE Trans Neural Syst Rehabil Eng 2023;31: 1418-28.
• [82] Fu M, Wang Y, Chen Z, Li J, Xu F, Liu X, et al. Deep learning in automatic sleep staging with a single channel electroencephalography. Front Physiol 2021;12: 628502.
• [83] Jha Pawan K, Valekunja Utham K, Reddy AB. SlumberNet: deep learning classification of sleep stages using residual neural networks. Sci Rep 2024;14(1): 4797.
• [84] Li Q, Wang B, Jin J, Wang X. Comparison of CNN-Uni-LSTM and CNN-Bi-LSTM based on single-channel EEG for sleep staging. Bioinformatics, Medical Imaging and Neuroscience,2020,18-20.
• [85] Dong H, Supratak A, Pan W, Wu C, Matthews PM, Guo Y. Mixed neural network approach for temporal sleep stage classification. IEEE Trans Neural Syst Rehabil Eng 2017;26(2):324333.
• [86] Zhang KL. Sleep staging model based on deep convolution neural network and recurrent neural network. Information & Computer 2023;35(3):68-70.
• [87] Zhang J, Yao R, Ge W, Gao JF. Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG. Comput Methods Programs Biomed 2020:183105089.
• [88] Ziyang F, Chen H, Li Z, Wang SH, Zhang Y. Deep learning model of sleep EEG signal by using bidirectional recurrent neural network encoding and decoding. Electronics 2022;11(17):2644.
• [89] Ji X, Liu R, Liang H, Li H. Automatic sleep staging using CNN-HMM based on raw single-channel EEG. Int J Psychophysiol 2021;168:S168.
• [90] Zhuang L, Dai M, Zhou Y, Sun L. Intelligent automatic sleep staging model based on CNN and LSTM. Front Public Health 2022;10:946833.
• [91] Qu W, Wang Z, Hong H, Chi Z, Feng DD, Grunstein R, et al. A Residual based attention model for EEG based sleep staging. IEEE J Biomed Health Inform 2020; 24(10):2833-43.
• [92] Li C, Qi Y, Ding X, Zhao J, Sang T, Lee M. A deep learning method approach for sleep stage classification with EEG spectrogram. Int J Environ Res Publ Health 2022;19(10):6322.
• [93] Hisham E, Mohammad E, Mutaz R, Thomas P. A CNN-BiLSTM deep learning model for automatic scoring of EEG signals. Curr Direct Biomed Eng 2023;9(1): 642-5.
• [94] Li QY, Wang B, Jin J, Zhang T, Wang XY. Automatic sleep staging model based on the BiLSTM convolutional network and attention mechanism. CAAI Trans Intell Syst 2022;003:017.
• [95] Sundar GN, Narmadha D, Anton Jone AA, Sagayam KM, Dang H, Pomplun M. Automated sleep stage classification in sleep apnoea using convolutional neural networks. Inf Med Unlocked 2021;26:100724.
• [96] Tang J, Wen YM. Automatic sleep staging based on 3CNN-BiGRU. Comput Modernization 2022;002:000.
• [97] Hu KL, Chen JX, Zhang PW, Xue W, Xie J. Multi-modal physiological time-frequency feature extraction network for accurate sleep stage classification. J Biomed Eng 2024;41(01):26-33.
• [98] Li W, Gao J. Automatic sleep staging by a hybrid model based on deep 1DResNetSE and LSTM with single-channel raw EEG signals. PeerJ Comput Sci 2023;9:e1561.
• [99] Liu C, Yin Y, Sun Y, Ersoy OK. Multi-scale ResNet and BiGRU automatic sleep staging is based on an attention mechanism. PLoS One 2022;17(6):e0269500.
• [100] Wang Q, Tong S. EEG sleep staging algorithm based on hybrid neural network. Radio Engineering 2023;53(4):925-35.
• [101] Liu XB, Zhou Q. Automatic sleep staging method based on multi-layer convolution fusion network. J Sens Technol 2023;36(3):427-33.
• [102] Hu Y, Shi W, Yeh C. Spatiotemporal convolution sleep network based on graph attention mechanism with automatic feature extraction. Comput Methods Programs Biomed 2024:244107930.
• [103] Zhang W, Li C, Peng H, Qiao H Y, Chen X. CTCNet: A CNN Transformer capsule network for sleep stage classification. Measurement,2024,226114157.
• [104] Dongrae C, Boreom L. Automatic sleep-stage classification based on residual unit and attention networks using directed transfer function of electroencephalogram signals. Biomed Signal Process Control 2024;88:105679.
• [105] Zhao YJ, Zhou Q, Liu X, Li W, Tian YZ. Single channel EEG signal automatic sleep staging algorithm based on deep reinforcement learninög. Appl Res Comput 2024: 1-7.
• [106] She Y, Zhang D, Sun J, Yang XJ, Zeng X, Wei Q. CBLSNet: A concise feature context fusion network for sleep staging. Biomed Signal Process Control 2024.
• [107] Shao Y, Huang B, Du L, Wang P, Li ZF, Zhe L, et al. Reliable automatic sleep stage classification based on hybrid intelligence. Comput Biol Med 2024;173108314.
• [108] Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit and PhysioNet are components of a new research resource for complex physiologic signals. Circulation 2000;101(23):215-20.
• [109] Sateia MJ. International classification of sleep disorders. Chest 2014;146(5): 1387-94.
• [110] Arslan RS, Ulutas H, Köksal AS, Bakir M, Çiftçi B. End-to-end decision support system for sleep apnea detection and Apnea-Hypopnea Index calculation using hybrid feature vector and Machine learning. Biocybernet Biomed Eng 2023;43(4): 684-99.
• [111] Keong AN, Cuntai G. Impact of obstructive sleep apnea on sleep-wake stage ratio. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference; 2012, 20124660-3.
• [112] Md B, AS F, Frances S, Agu C, Bhattarai B, Oke V, Enriquez D et al. Relationship of symptoms with sleep-stage abnormalities in obstructive sleep apnea-hypopnea syndrome. J Commun Hospital Int Med Perspect 2016;6(4):32170.
• [113] Zhang FZ, Wang GX, Xu ZF, Zhen L, Zhang YM, Zhao J, et al. Analysis of sleep structure and related factors in children with severe OSAHS. J Otolaryngol Head Neck Surg 2019:441-6.
• [114] Yung-Ming C, Shyh-Liang L, Peng-Zhe T, Ming-Chen W, Liang-Wen H. A comparison of each sleep stage autonomic nervous system activity in different sleep Apnea severity levels. J Med Imag Health Inform 2020;10(6):1274-80.
• [115] Wu BW, Cai JY, Yao Y, Pan Y, Pan LQ, Zhang LS, et al. The relationship between obstructive sleep apnea and sleep structure is of different severity. J Zhejiang Univ (Medical Edition) 2020;49(04):455-61.
• [116] Hao HT, Wei SH, Rung SH, Hwang JH. Sleep apnea plays a more important role in the sleep N3 stage than chronic tinnitus in adults. Medicine 2022;101(34): e30089.
• [117] Zhang YT, Li QX, Shi ZH, Shang L, Yuan YQ, Cao ZN, et al. Study on sleep structure and establishment of sleep structure interpretation model in children with obstructive sleep apnea. Otolaryngol J Shand Univ 2023;37(06):126-32.
• [118] Don MD, Osterbauer B, Gowthaman D, Laurel F, Emily SG. Polysomnographic characteristics of sleep architecture in children with obstructive sleep Apnea. Ann Otol Rhinol Laryngol 2024;133(6). 34894241232477-34894241232477.
• [119] Schlemmer A, Parlitz U, Luther S, Wessel N, Penzel T. Changes of sleep-stage transitions due to aging and sleep disorder. Philos Trans. Series A, Math, Phys Eng Sci 2015;373(2034).
• [120] Wächter M, Kantelhardt JW, Bonsignore MR, Bouloukaki I, Escourrou P, Fietze I, et al. Unique sleep-stage transitions are determined by obstructive sleep apnea severity, age, and gender. J Sleep Res 2020;29(2):e12895.
• [121] Kohzoh Y, Saki I, Akinori I, Sakoda S. Dynamic sleep stage transition process analysis in patients with Parkinson’s disease having sleep apnea syndrome. Inf Med Unlocked 2021;25.
• [122] Zhao JC, Liu M, Jiang XQ, Shi WY, Lou YT, Leng JC, et al. Brain network analysis and classification based on sub-period apnea and sleep stages. Biomed Eng Res 2024;43(01):40-5.
• [123] Lee T-D, Pei-Lin H, Yi H L, Po C C, Yu An H, Jen W L, Hsiang W H, et al. Automatic sleep staging in patients with obstructive sleep Apnea using single-channel frontal EEG. J Clin Sleep Med: JCSM: Off Publ Am Acad Sleep Med 2019,15(10):1411-1420.
• [124] Lv XF, Ma J, Li JB, Ren QQ. Ssleepnet: a structured sleep network for sleep staging based on sleep apnea severity. Complex Intelligent Systems 2023;10(2): 2689-701.
• [125] Henri K, Timo L, Juhani A, Sami N, Samu K, Akseli L, et al. Accurate deep learning-based sleep staging in a clinical population with suspected obstructive sleep Apnea. IEEE J Biomed Health Inform 2019;24(7). 1-1.
• [126] Hanrui Z, Xueqing W, Hongyang L, Soham, Guan YF. Auto-annotating sleep stages based on polysomnographic data.Patterns,2021,3(1):100371.
• [127] Yamei L, Shengqiong L, Haibo Z, Yinkai Z, Zhang Y, Lo B. MtCLSS: multi-task contrastive learning for semi-supervised pediatric sleep staging. IEEE J Biomed Health Inform 2023;27(6):2647-55.
• [128] Fernando V, Daniel A, GutierrezTobal GC, Del CF, Gozal D, Kheirandish-Gozal L, et al. A deep learning model based on the combination of convolutional and recurrent neural networks to enhance pulse oximetry ability to classify sleep stages in children with sleep apnea. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-4.
• [129] Yuyang Y, Shuohua C, Zhihong Y, Qihang S. PSNSleep: a self-supervised learning method for sleep staging based on Siamese networks with only positive sample pairs. Front Neurosci 2023. 171167723-1167723.
• [130] Liu Y, He CL, Yuan CM, Zhang HW, Ji CJ. A study on sleep stage staging method of polysomnography based on attentional mechanisms and bi-directional gated cyclic units. J Biomed Eng 2023;40(1):35-43.
• [131] Hyun JL, Hyunwoo N, Hyun DK, Lim KD, Won CJ, SeungNo H, et al. Developing a deep learning model for sleep stage prediction in obstructive sleep apnea cohort using 60 GHz frequency-modulated continuous-wave radar. J Sleep Res 2023;33(1):e14050.
• [132] Fernando V, Gutiérrez-Tobal GC, Eva C, Álvarez D, Kheirandish-Gozal L, Félix del C, et al. An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea. Comput Biol Med 2023. 165107419-107419.
• [133] Xiaopeng J, Yan L, Peng W .3DSleepNet: A Multi-Channel Bio-Signal Based Sleep Stages Classification Method Using Deep Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering: a publication of the IEEE Engineering in Medicine and Biology Society,2023,31:3513-3523.
• [134] Minji L, Gyu HK, Jin HK, Ok WD, Whan LS. SeriesSleepNet: an EEG time series model with partial data augmentation for automatic sleep stage scoring. Front Physiol 2023. 141188678-1188678.
• [135] Tai CH, Liao TY, Chen SP, Chung MH. Sleep stage classification using light gradient boost machine: exploring feature impact in depressive and healthy participants. Biomed Signal Process Control 2024;88:105647.
• [136] Aar DVFJ, Ende DVAD, Fonseca P, Van Meulen FB, Sebastiaan O, Merel MVG, et al. Deep transfer learning for automated single-lead EEG sleep staging with channel and population mismatches. Front Physiol 2024. 141287342-1287342.
• [137] Pei W, Li Y, Wen P, Yang FW, Ji XP. An automatic method using MFCC features for sleep stage classification. Brain Inform 2024;11(1). 6-6.
• [138] Vaswani A, Shazeer N, Parmar N, Uszkorei J, Jones, L, Gomez A N, et al. Attention is all you need,2017,30.
• [139] Phan H, Mikkelsen K, Chén OY, Koch P, Mertins A, De Vos M. Sleeptransformer: Automatic sleep staging with interpretability and uncertainty quantification. IEEE Trans Biomed Eng 2022;69:2456-67.
• [140] Li P, Yanzhen R, Zhiheng L, Chen X, Yang X Q, Tu WP. SleepViTransformer: Patch-based sleep spectrogram transformer for automatic sleep staging. Biomed Signal Process Control 2023;86.
• [141] Yang D, Xiuli L, Shanshan L, Wang L K, Duan QT, Yang H, et al. MultiChannelSleepNet: A Transformer-based Model for Automatic Sleep Stage Classification with PSG. IEEE J Biomed Health Inform 2023;27(9):4204-4215.
• [142] Hu J, Shen L, Albanie S, Sun G, Wu E. Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell 2020;42(8):2011-23.
• [143] Li T, Gong Y, Lv Y, Wang F, Hu M, Wen Y. GAC-SleepNet: A dual-structured sleep staging method based on graph structure and Euclidean structure. Comput Biol Med 2023;165:107477.
• [144] Qu W, Zhiyong W, Hong H, Zheru C, Dagan FD, Ron G, et al. A residual based attention model for EEG based sleep staging. IEEE J Biomed Health Inform 2020.
• [145] Zhiqiang C, Xue P, Zhifei X, Li K, Yudan L, Zhang Y et al. A semi-supervised multiscale arbitrary dilated convolution neural network for pediatric sleep staging. IEEE J Biomed Health Informat 2023;109.
• [146] Zhu H, Xu Y, Wu Y, Shen N, Wang L, Chen C, et al. A sequential end-to-end neonatal sleep staging model with squeeze and excitation blocks and sequential multi-scale convolution neural networks. Int J Neural Syst 2024;34(3):2450013.
• [147] Sajad M, Fatemeh A, Rajendra UA. SleepEEGNet: automated sleep stage scoring with sequence-to-sequence deep learning approach. PLoS One 2019;14(5): e0216456.
• [148] Zhu T, Luo W, Yu F. Convolution-and attention-based neural network for automated sleep stage classification. Int J Environ Res Public Health 2020;17: 4152.
• [149] Huang G, Ma F. TrustSleepNet: A Trustable Deep Multimodal Network for Sleep Stage Classification. Proceedings of the 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece; 2022: 27-30.
• [150] Li LT, Miao MM. Sleep EEG staging based on multi-view and attention mechanism. Foreign Electronic Measur Technique 2024;43(01):30-37.
• [151] Yu W, Yongpeng Z, Yihan Z, Yu XK, Luo YX. Automatic sleep staging based on contextual scalograms and attention convolution neural network using single-channel EEG. IEEE J Biomed Health Inform 2024;28(2):801-11.
• [152] Yang B, Wu W, Liu Y, Liu H. A novel sleep stage contextual refinement algorithm leveraging conditional random fields. IEEE Trans Instrum Meas 2022;71:1-13.
• [153] Xu X, Chen C, Meng K, Lu LB, Cheng XR, Fan HC. NAMRTNet: Automatic classification of sleep stages based on improved ResNet-TCN network and attention mechanism. Appl Sci 2023;13(11).
• [154] Nam B, Bark B, Lee J, Kim IY. InsightSleepNet: the interpretable and uncertainty-aware deep learning network for sleep staging using continuous photoplethysmography. BMC Med Inf Decis Making 2024;24(1). 50-50.
• [155] Caihong Z, Jinbao L, Yahong G. BTCRSleep: a boundary temporal context refinement based fully convolutional network for sleep staging with single-channel EEG. Physiol Meas 2023;44(7).
• [156] Xu ZL, Yang XJ, Sun JB, Liu P, Qin W. Sleep stage classification using time-frequency spectra from consecutive multi-time points. Front Neurosci 2020.
• [157] Zhou J, Cui G Q, Hu S D, Zhang Z Y, Yang C. Graph neural networks: a review of methods and applications Open 2020;157-81.
• [158] Jia Z, Lin Y, Wang J, Zhou R, Ning X, He Y, Zhao Y. GraphSleepNet: Adaptive spatial-temporal graph convolutional networks for sleep stage classification. Int J Conf Artificial Intelligence 2020:1324-1330.
• [159] Jia Z, Lin Y, Wang J, Ning X, He Y, Zhou R, et al. Multiview spatial-temporal graph convolutional networks with domain generalization for sleep stage classification. IEEE Trans Neural Syst Rehabilitation Eng 2021;29:1977-86.
• [160] Ji X, Li Y, Wen P. Jumping knowledge based spatial-temporal graph convolutional networks for automatic sleep stage classification. In: IEEE Transactions on Neural Systems and Rehabilitation Engineering 2022;30:1464-1472.
• [161] Yuan Z, Xianghong L, Zequn Z, Wang X W, He XR, Yang LL. STDP-based adaptive graph convolutional networks for automatic sleep staging. Front Neurosci 2023; 171158246-1158246.
• [162] Einizade A, Nasiri S, Sardouie SH, Clifford GD. ProductGraphSleepNet: Sleep staging using product spatiotemporal graph learning with attentive temporal aggregation. Neural Netw 2023;164:667-80.
• [163] Jiang D, Ma Y, Wang Y. Sleep stage classification using covariance features of multi-channel physiological signals on Riemannian manifolds. Comput Methods Programs Biomed 2019;178:19-30.
• [164] Xueling Z, Bingo LW, Waqar A, Yang Z, Yuxin L, Hongtao Z. Multivariate phase space reconstruction and Riemannian manifold for sleep stage classification. Biomed Signal Process Control 2024;88.
• [165] Supratak A, Dong H, Wu C, Guo Y. DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Trans Neural Syst Rehabil Eng 2017:(99).
• [166] Phan H, Andreotti F, Cooray N, Chen OY, De Vos M. SeqSleepNet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging. IEEE Trans Neural Syst Rehabil Eng 2019;27(3):400-10.
• [167] Supratak A, Guo Y. TinySleepNet: an efficient deep learning model for sleep stage scoring based on raw single-channel EEG. Annu Int Conf IEEE Eng Med Biol Soc 2020;2020:641-4.
• [168] Jia Z, Lin Y, Wang J, Wang X, Xie P, Zhang Y. SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging. ArXiv; 2021.
• [169] Sanghavi S, Vaid P, Rathod P, Srivastava K. SpectroTemporalNet: Automated Sleep Stage Scoring with Stacked Generalization. 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India; 2021:1256-1263.
• [170] Phan H, Ch´en OY, Tran MC, Koch P, Mertins A, De Vos M. XSleepNet: multi-view sequential model for automatic sleep staging. IEEE Trans Pattern Anal Mach Intell 2021;44:5903-15.
• [171] Neng W, Lu J, Xu L. CCRRSleepNet: a hybrid relational inductive biases network for automatic sleep stage classification on raw single-channel EEG. Brain Sci 2021;11(4):456.
• [172] Eldele E, Chen Z, Liu C, Wu M, Kwoh CK, Li X, et al. An attention-based deep learning approach for sleep stage classification with single-channel EEG. IEEE Trans Neural Syst Rehabil Eng 2021;29:809-18.
• [173] Yu R, Zhou Z, Wu S, Gao X, Bin G. MRASleepNet: a multi-resolution attention network for sleep stage classification using single-channel EEG. Neural Eng 2022; 19(6).
• [174] Tingting L, Bofeng Z, Hehe L, Hu SX, Xu ZK. Tuergong Y.CAttSleepNet: automatic end-to-end sleep staging using attention-based deep neural networks on single-channel EEG. Int J Environ Res Public Health 2022;19(9). 5199-5199.
• [175] Zhao C, Li J, Guo Y. SleepContextNet: A temporal context network for automatic sleep staging based single-channel EEG. Comput Methods Programs Biomed 2022; 220:106806.
• [176] Jin Z, Jia K. SAGSleepNet: a deep learning model for sleep staging based on the self-attention graph of polysomnography. Biomed Signal Process Control 2023; 86:105062.
• [177] Hangyu Z, Wei Z, Cong F, Y L, Shen N, et al. MaskSleepNet: A Cross-modality Adaptation Neural Network for Heterogeneous Signals Processing in Sleep Staging. IEEE J Biomed Health Inform 2023;27(5):2353-2364.
• [178] Hui CL, Ji HK, Tak YK, Hakseung K, Bin KJ, Joo KD. SleepExpertNet: high-performance and class-balanced deep learning approach inspired from the expert neurologists for sleep stage classification. J Ambient Intell Hum Comput 2022;14(6):8067-83.
• [179] Zhi L, Meiqiao Q, Yunhua L, Sixin L, Qinhan Z. DenSleepNet: DenseNet-based model for sleep staging with two-frequency feature fusion and coordinate attention. Biomed Eng Lett 2023;13(4):751-61.
• [180] Zhu HY, Guo Y, Wu YL, Zhang YY, Shen N, Xu Y et al. MAGSleepNet: Adaptively multi-scale temporal focused sleep staging model for multi-age groups.Expert Systems With Applications,2024,240.
• [181] Ji X, Li Y, Wen P, Barua P, Acharya UR. MixSleepNet: a multi-type convolution combined sleep stage classification model. Comput Methods Programs Biomed 2024.
• [182] Zhu H, Wu Y, Guo Y, Fu C, Shu F, et al. Towards real-time sleep stage prediction and online calibration based on architecturally switchable deep learning models. IEEE J Biomed Health Inform 2024;28(1):470-81.
• [183] Fei K, Wang J, Pan L, Wang X, Chen BH. A sleep staging model on wavelet-based adaptive spectrogram reconstruction and lightweight CNN. Comput Biol Med 2024;173108300.
• [184] Zhou ZH. Machine learning. Beijing: Tsinghua University Press; 2016. p. 31-5.
• [185] Olsen M, Zeitzer MJ, Richardson NR, Valerie HM, Helge BDS, Emmanuel M, et al. A deep transfer learning approach for sleep stage classification and sleep apnea detection using wrist-worn consumer sleep technologies. IEEE Trans Biomed Eng 2024.
• [186] Vera B, Mohamed E, Olivier L, Carlo M. Evaluating reliability in wearable devices for sleep staging. NPJ Digital Med 2024;7(1). 74-74.
• [187] Xue MJ, Wang FQ, Huang FY, Yang C, Zhao MY. Research on sleep monitoring system based on human physiological signals. Internet Things Technol 2024;14(04):23-5.
• [188] Ma S, Zhang Y, Chen Y, Tao X, Song SC, Jia ZY. Exploring structure incentive domain adversarial learning for generalizable sleep stage classification[J]. ACM Trans Intell Syst Technol 2024;15(1):1-30.
• [189] Waters SH, Clifford GD. Physics-informed transfer learning to enhance sleep staging. IEEE Trans Biomed Eng 2024;71(5):1599-606.
• [190] Shao HY, Zheng YB, Luo YY, Li L, Zhang L. Sleep staging algorithm based on class rebalancing unsupervised domain adaptation. J Beijing Univ Posts Telecommun 2023;46(06):8-14.
• [191] Zhang Y, Yang Q. An overview of multi-task learning. Natl Sci Rev 2018;5:30-43.
• [192] Nie H, Tu S, Xu L. RecSleepNet: An Automatic Sleep Staging Model Based on Feature Reconstruction. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021:1458-1461.
• [193] Caihong Z, Jinbao L, Yahong G. Sequence signal reconstruction based multi-task deep learning for sleep staging on single-channel EEG. Biomed Signal Process Control 2024;88.
• [194] Liu Y, Chu HR, Zhang HW. Automatic sleep staging based on deep learning: a review. J Data Acquisit Process 2023;38(04):759-76.
• [195] Yazdi M, Samaee M, Massicotte D. A review on automated sleep study. Ann Biomed Eng 2024;52(6):1463-91.
• [196] Ying SF, Qin DY, Xie JX, Gao DR, Qin Y, Liu TJ. Automatic sleep staging based on heart rate variability and its EEG verification. Chin J Biomed Eng 2024;43(01): 18-26.
• [197] Kawada T. Automatic sleep staging system utilizing photoplethysmography: a validation study. Journal of Clinical Sleep Medicine: JCSM: official publication of the American Academy of. Sleep Med 2024.
• [198] Hyun JL, Hyunwoo N, Hyun DK, Lim KD, Won CJ, Hong SN, et al. Developing a deep learning model for sleep stage prediction in obstructive sleep apnea cohort using 60 GHz frequency-modulated continuous-wave radar. J Sleep Res 2023;33(1):e14050.
• [199] Mcchugh ML. Interrater reliability: The kappa statistic. Biochemia Medica 2012; 22(3):276-82.
• [200] Irfan A, Cassie S. Mitchell. SERF: Interpretable Sleep Staging using Embeddings, Rules, and Features. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management; 2022.
• [201] Niknazar H, Mednick SC. A multi-level interpretable sleep stage scoring system by infusing experts’ knowledge into a deep network architecture. IEEE Trans Pattern Anal Mach Intell 2024;46(7):5044-61.
• [202] Don DM, Osterbauer B, Gowthaman D, Fisher L, Gillett ES. Polysomnographic characteristics of sleep architecture in children with obstructive sleep apnea. Ann Otol Rhinol Laryngol 2024;133(6):590-7.
• [203] Huang MF, Huang DS, Fu LL. Characteristics of sleep staging in elderly patients with obstructive sleep apnea-hypopnea syndrome. In: Chinese Sleep Research Association. The 8th Annual Conference of China Sleep Research Association and 20th Anniversary Celebration Paper Collection; 2014.
• [204] Tan X, Ljunggren M, Kilander L, Benedict C, Lindberg E. Obstructive sleep apnea during rapid eye movement sleep and cognitive performance in adults. Sleep Med 2024;113:34-40.
• [205] Liang YL, Zhao QP. Comparative analysis of sleep structure between menopausal women and adult women with moderate and severe obstructive apnea-hypopnea syndrome. World J Sleep Med 2022;9(06):1068-70.
• [206] Wulterkens MB, Teuling DGN, Hermans WL, Asin J, Duis N, Overeem S, et al. Multi-night home assessment of sleep structure in OSA with and without insomnia. Sleep Med 2024;117:152-61.
• [207] Yue H, Chen Z, Guo W, Sun L, Dai YD, Wang YM, et al. Research and application of deep learning-based sleep staging: data, modeling, validation, and clinical practice. Sleep Med Rev 2024. 74101897-101897.
• [208] Terzano MG, Parrino L, Sherieri A, Chervin R, Chokroverty S, Guilleminault C, et al. Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep. Sleep Med 2001 Nov;2(6):537-53. https://physionet.org/content/capslpdb/1.0.0/.
• [209] Kemp B, Zwinderman AH, Tuk B, Kamphuisen HAC, Obery´e JJL. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE-BME 2000;47(9):1185-94. https://physionet.org/content/sleep-edfx/1.0.0/.
• [210] [dataset] Khalighi Sirvan, Teresa Sousa, José Moutinho Santos, and Urbano Nunes. “ISRUC-Sleep: A comprehensive public dataset for sleep researchers.“Computer methods and programs in biomedicine 124 (2016): 180-192. https://sleeptight.isr.uc.pt/?page_id=48.
• [211] Ichimaru Y, Moody GB. Development of the polysomnographic database on CDROM. Psychiatry Clin Neurosci April 1999;53:175-7. https://physionet.org/content/slpdb/1.0.0/.
• [212] [dataset] O’Reilly C et al. (2014) J Sleep Res, 23(6):628-635. https://borealisdata.ca/dataverse/MASS.
• [213] Zhang GQ, Cui L, Mueller R, Tao S, Kim M, Rueschman M, et al. The National Sleep Research Resource: towards a sleep data common. J Am Med Inform Assoc 2018 Oct 1;25(10):1351-8. https://sleepdata.org/datasets/shhs/.
• [214] Alvarez-Estevez D, Rijsman R M. Inter-database validation of a deep learning approach for automatic sleep scoring. PLoS One, 2021, 16(8): e0256111. https://physionet. org/content/hmc-sleep-staging/1.1/.
• [215] Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000;101(23):E215-20. https://physionet.org/cont ent/ucddb/1.0.0/.
• [216] Zhou W, Zhu H, Shen N, Chen H, Fu C, Yu H, et al. A lightweight segmented attention network for sleep staging by fusing local characteristics and adjacent information. IEEE Trans Neural Syst Rehabil Eng 2023;31:238-47.
• [217] Zaman A, Kumar S, Shatabdi S, Dehzangi I, Sharma A. SleepBoost: a multi-level tree-based ensemble model for automatic sleep stage classification. Med Biol Eng Comput; 2024.
• [218] Li Y, Chen J, Ma W, Zhao G, Fan X. MVF-SleepNet: multi-view fusion network for sleep stage classification. IEEE J Biomed Health Inform 2024;28(5):2485-95.
• [219] Pranavan S, Timo L, I P T, Sigridur S, Sif A E, A Ó K, et al.Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls.Front Neurol 2023; 141162998-1162998.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).