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REFERENCES

1 
W. M. A. van Leeuwen et al., “Sleep restriction increases the risk of developing cardiovascular diseases by augmenting proinflammatory responses through IL-17 and CRP,” PLoS One, Feb. 2009.DOI
2 
B. Faraut, K. Z. Boudjeltia, L. Vanhamme, and M. Kerkhofs, “Immune, inflammatory and cardiovascular consequences of sleep restriction and recovery,” Sleep Med Rev, Apr. 2011.DOI
3 
J. E. Gangwisch, D. Malaspina, B. Boden-Albala, and S. B. Heymsfield, “Inadequate sleep as a risk factor for obesity: analyses of the NHANES I,” Sleep, Oct. 1992.DOI
4 
R. B. Berry et al., “The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications, Version 2.0,” American Academy of Sleep Medicine, 2012.URL
5 
M. Adnane, Z. Jiang, and Z. Yan, “Sleep-wake stages classification and sleep efficiency estimation using single-lead electrocardiogram,” Expert Systems with Applications, Aug. 2011.DOI
6 
L. Fraiwan, K. Lweesy, N. Khasawneh, H. Wenz, and H. Dickhaus, “Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier,” Computer methods and programs in biomedicine, Sep. 2009.DOI
7 
W. Pei, Y. Li, S. Siuly, and P. Wen, “A hybrid deep learning scheme for multi-channel sleep stage classification,” Computers, Materials and Continua, Feb. 2022.DOI
8 
P. Y. Simard, D. Steinkraus, and J. C. Platt, “Best practices for convolutional neural networks applied to visual document analysis,” Icdar, Aug. 2003.DOI
9 
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun ACM, Jun. 2017.DOI
10 
B. Zhao, H. Lu, S. Chen, J. Liu, and D. Wu, “Convolutional neural networks for time series classification,” Journal of Systems Engineering and Electronics, Feb. 2017.DOI
11 
W. Yin, K. Kann, M. Yu, and H. Schütze, “Comparative Study of CNN and RNN for Natural Language Processing,” 2017.DOI
12 
O. Vinyals, A. Toshev, S. Bengio, and D. Erhan, “Show and tell: A neural image caption generator,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June. 2015.URL
13 
S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural computation, Nov. 1997.DOI
14 
A. Vilamala, K. H. Madsen, and L. K. Hansen, “Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring,” Computers, Materials and Continua, Sep. 2021.DOI
15 
K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint arXiv:1409.1556, Sep. 2014.DOI
16 
F. Ebrahimi, M. Mikaeili, E. Estrada, and H. Nazeran, “Automatic Sleep Stage Classification Based on EEG Signals by Using Neural Networks and Wavelet Packet Coefficients,” IEEE Engineering in Medicine and Biology Society, Aug. 2008.DOI
17 
N. Michielli, U. R. Acharya, and F. Molinari, “Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals,” ScienceDirect Computers in Biology and Medicine, Mar. 2019.DOI
18 
O. Yildirim, U. B. Baloglu, and U. R. Acharya, “A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals,”, International journal of environmental research and public health, Feb. 2019.DOI
19 
S. Mousavi, F. Afghah, and U. R. Acharya, “SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach,” PLoS ONE, May. 2019.DOI
20 
A. Supratak, H. Dong, C. Wu, and Y. Guo, “DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Jun. 2017.DOI
21 
A. Supratak and Y. Guo, “TinySleepNet: An efficient deep learning model for sleep stage scoring based on raw single-channel EEG,” 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Jul. 2020.DOI
22 
I. Hussain et al., “Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages,” Sensors, Apr. 2022.DOI
23 
R. Zhao, Y. Xia, and Q. Wang, “Dual-modal and multi-scale deep neural networks for sleep staging using EEG and ECG signals,” Biomedical Signal Processing and Control, Apr. 2021.DOI
24 
C. W. Whitney et al., “Reliability of scoring respiratory disturbance indices and sleep staging,” Sleep, 1998.DOI
25 
T. Penzel et al., “Modulations of heart rate, ECG, and cardio-respiratory coupling observed in polysomnography,” Frontiers in Physiology, Oct. 2016.DOI
26 
R. P. Bartsch et al., “Phase transitions in physiologic coupling,” Proc Natl Acad Sci USA, Jun. 2012.DOI
27 
P. A. Lanfranchi and V. K.Somers, “Cardiovascular Physiology: Autonomic Control in Health and in Sleep Disorders,” Principles and Practice of Sleep Medicine: Fifth Edition, 2010.DOI
28 
E. Urtnasan, J. U. Park, E. Y. Joo, and K. J. Lee, “Deep Convolutional Recurrent Model for Automatic Scoring Sleep Stages Based on Single-Lead ECG Signal,” Diagnostics, May. 2022.DOI
29 
M. Radha et al., “A deep transfer learning approach for wearable sleep stage classification with photoplethysmography,” NPJ digital medicine, Sep. 2021.DOI
30 
M. Radha et al., “Sleep stage classification from heart-rate variability using long short-term memory neural networks,” Scientific Reports, Oct. 2019.DOI
31 
Q. Li et al., “Deep learning in the cross-time frequency domain for sleep staging from a single-lead electrocardiogram,” Physiological measurement, Dec. 2018.DOI
32 
N. Sridhar et al., “Deep learning for automated sleep staging using instantaneous heart rate,” NPJ digital medicine, Oct. 2020.DOI
33 
Y. Wei al., “A multi-class automatic sleep staging method based on long short-term memory network using single-lead electrocardiogram signals,” IEEE Access 7, Jun. 2019.DOI
34 
M. Tang et al., “Deep adaptation network for subject-specific sleep stage classification based on a single-lead ECG,” Biomedical Signal Processing and Control 75, Feb. 2022.DOI
35 
R. K. Tripathy and U. R. Acharya, “Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework,” Advances in Artificial Neural Systems, May. 2012.DOI
36 
M. Längkvist, L. Karlsson, and A. Loutfi, “Sleep Stage Classification Using Unsupervised Feature Learning,” Advances in Artificial Neural Systems, Jul. 2012.DOI
37 
M. Kebe et al., “Human Vital Signs Detection Methods and Potential Using Radars: A Review,” Sensors, Mar. 2020.DOI
38 
L. Zhang et al., “Sleep stages classification by CW Doppler radar using bagged trees algorithm,”2017 IEEE Radar Conference, May. 2017.DOI
39 
H. Hong et al., “Noncontact Sleep Stage Estimation Using a CW Doppler Radar,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, June. 2018.DOI
40 
A. Tataraidze et al., “Bioradiolocation-based sleep stage classification,” 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC), Aug. 2016.DOI
41 
S. Toften et al., “Validation of sleep stage classification using non-contact radar technology and machine learning(Somnofy),” Sleep Medicine, Feb. 2020.DOI
42 
H. B. Kwon et al., “Attention-Based LSTM for Non-Contact Sleep Stage Classification Using IR-UWB Radar,” IEEE Journal of Biomedical and Health Informatics, Oct. 2021.DOI
43 
B. Yu et al., “WiFi-Sleep: Sleep Stage Monitoring Using Commodity Wi-Fi Devices,” IEEE Internet of Things Journal, Mar. 2021.DOI
44 
M. Zhao et al., “Learning sleep stages from radio signals: A conditional adversarial architecture,” arXiv:1706.03762, 2017.URL