Steptoe A, O’Donnell K, Marmot M, Wardle JP, Affect. Psychological Well-being, and good sleep. J Psychosom Res. 2008;64:409–15. https://doi.org/10.1016/j.jpsychores.2007.11.008.
Rundo JV, Downey R. Polysomnography. In: Handbook of clinical neurology, vol 160. 2019. pp. 381–392.
Deak M, Epstein LJ. The history of polysomnography. Sleep Med Clin. 2009;4:313–21. https://doi.org/10.1016/j.jsmc.2009.04.001.
Menghini L, Cellini N, Goldstone A, Baker FC, De Zambotti M. A standardized Framework for testing the performance of sleep-tracking technology: step-by-step guidelines and open-source code. Sleep. 2021;44. https://doi.org/10.1093/sleep/zsaa170.
Carskadon MA, Dement WC. Normal human sleep: an overview. In: Principles and practice of sleep medicine. 2005. pp. 13–23. (ISBN 9781416066453)
Boostani R, Karimzadeh F, Nami MA. Comparative review on sleep stage classification methods in patients and healthy individuals. Comput Methods Programs Biomed. 2017;140:77–91. https://doi.org/10.1016/j.cmpb.2016.12.004.
Berry RB, Brooks R, Gamaldo C, Harding SM, Lloyd RM, Quan SF, Troester MT, Vaughn BV. AASM Scoring manual updates for 2017 (version 2.4). J Clin Sleep Med. 2017;13:665–6. https://doi.org/10.5664/jcsm.6576.
Park J, An J, Choi SH. Sleep stage classification using deep learning techniques: a review. IEIE Trans Smart Process Comput. 2023;12:30–7. https://doi.org/10.5573/IEIESPC.2023.12.1.30.
Baek J, Baek S, Yu HS, Lee JH, Park C. End-to-end automatic sleep staging Algorithm using convolution neural network and bidirectional LSTM. IEIE Trans Smart Process Comput. 2021;10:464–8. https://doi.org/10.5573/IEIESPC.2021.10.6.464.
Baek S, Baek J, Yu H, Lee C, Park C. Explainable sleep staging Algorithm using a single-Channel Electroencephalogram. IEIE Trans Smart Process Comput. 2022;11:8–13. https://doi.org/10.5573/IEIESPC.2021.11.1.8.
Dijk DJ. Regulation and functional correlates of slow Wave Sleep. J Clin Sleep Med. 2009;5. https://doi.org/10.5664/jcsm.5.2s.s6.
Hussain I, Hossain MA, Jany R, Bari MA, Uddin M, Kamal ARM, Ku Y, Kim JS. Quantitative evaluation of EEG-Biomarkers for prediction of sleep stages. Sensors. 2022;22. https://doi.org/10.3390/s22083079.
Welch PD. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans Audio Electroacoust. 1967;15:70–3. https://doi.org/10.1109/TAU.1967.1161901.
Fernandez LMJ, Lüthi A. Sleep spindles: mechanisms and functions. Physiol Rev. 2020;100:805–68. https://doi.org/10.1152/physrev.00042.2018.
Kleifges K, Bigdely-Shamlo N, Kerick SE, Robbins KA, BLINKER. Automated extraction of ocular indices from EEG enabling large-scale analysis. Front Neurosci. 2017;11. https://doi.org/10.3389/fnins.2017.00012.
Silva H, Scherer R, Sousa J, Londral A. Towards improving the usability of electromyographic interfaces. Biosyst Biorobotics. 2013;1:437–41. https://doi.org/10.1007/978-3-642-34546-3_71.
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–57. https://doi.org/10.1613/jair.953.
Torelli GM. Dan Training and assessing classification rules with Imbalanced Data. Data Min Knowl Discov. 2012;28:92–122.
Bharitkar S, Kyriakakis CA. Cluster centroid method for room response equalization at multiple locations. IEEE ASSP Work Appl Signal Process Audio Acoust. 2001;55–8. https://doi.org/10.1109/aspaa.2001.969541.
Byrd J, Lipton ZC. What is the effect of importance weighting in deep learning? In: 36th Int. Conf. Mach. Learn. ICML 2019 2019, 2019-June. pp. 1405–1419.
Vallat R, Walker MP. An open-source, high-performance tool for automated sleep staging. Elife. 2021;10. https://doi.org/10.7554/eLife.70092.
Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees. Classif Regres Trees. 2017;1–358. https://doi.org/10.1201/9781315139470.
Breiman L, Random. Forests. Random Forests, 1–122. Mach Learn. 2001;45:5–32.
Geurts P, Ernst D, Wehenkel L. Extremely randomized trees. Mach Learn. 2006;63:3–42. https://doi.org/10.1007/s10994-006-6226-1.
J Pandya V. Comparing handwritten character recognition by AdaBoostClassifier and KNeighborsClassifier. Proc - 2016 8th Int Conf Comput Intell Commun Networks CICN 2016. 2017;271–274. https://doi.org/10.1109/CICN.2016.59.
Platt J. Others probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv Large Margin Classif. 1999;10:61–74.
Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001;29:1189–232. https://doi.org/10.1214/aos/1013203451.
Article MathSciNet MATH Google Scholar
Chen T, Guestrin C. XGBoost: a scalable tree boosting system. Proc ACM SIGKDD Int Conf Knowl Discov Data Min. 2016;13:785–794. https://doi.org/10.1145/2939672.2939785.
Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY. LightGBM: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst. 2017;2017:3147–55.
Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A. Catboost: unbiased boosting with categorical features. Adv Neural Inf Process Syst. 2018;2018:6638–48.
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. 2018;26:324–33. https://doi.org/10.1109/TNSRE.2017.2733220.
Zhao D, Jiang R, Feng M, Yang J, Wang Y, Hou X, Wang XA. Deep learning algorithm based on 1D CNN-LSTM for automatic sleep staging. Technol Heal Care. 2022;30:323–36. https://doi.org/10.3233/THC-212847.
Phan H, Mikkelsen K, Chen 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. https://doi.org/10.1109/TBME.2022.3147187.
Snoek J, Larochelle H, Adams RP. Practical bayesian optimization of machine learning algorithms. Adv Neural Inf Process Syst. 2012;4:2951–9.
Rodríguez JD, Pérez A, Lozano JA. Sensitivity analysis of K-fold cross validation in prediction error estimation. IEEE Trans Pattern Anal Mach Intell. 2010;32:569–75. https://doi.org/10.1109/TPAMI.2009.187.
Arslan RS, Ulutaş H, Köksal AS, Bakır M, Çiftçi B. Automated sleep scoring system using multi-channel data and machine learning. Comput Biol Med. 2022;146. https://doi.org/10.1016/j.compbiomed.2022.105653.
Almutairi H, Hassan GM, Datta A. Classification of sleep stages from EEG, EOG and EMG signals by SSNet. 2023.
Supratak A, Guo Y, TinySleepNet. An efficient deep learning model for sleep stage scoring based on raw single-channel EEG. In: Proc. annu. int. conf. IEEE eng. med. biol. soc. EMBS 2020. 2020. pp. 641–644. https://doi.org/10.1109/EMBC44109.2020.9176741
Phan H, Chen 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. 2022;44:5903–15. https://doi.org/10.1109/TPAMI.2021.3070057.
Efe E, Ozsen S, CoSleepNet. Automated sleep staging using a hybrid CNN-LSTM Network on Imbalanced EEG-EOG datasets. Biomed Signal Process Control. 2023;80:104299. https://doi.org/10.1016/j.bspc.2022.104299.
Shen Q, Xin J, Liu X, Wang Z, Li C, Huang Z, Wang Z. LGSleepNet: an automatic sleep staging Model based on local and global representation learning. IEEE Trans Instrum Meas. 2023;72:1–14. https://doi.org/10.1109/TIM.2023.3298639.
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;25:1998–2008. https://doi.org/10.1109/TNSRE.2017.2721116.
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;42:107–14. https://doi.org/10.1016/j.bspc.2017.12.001.
Lal U, Mathavu Vasanthsena S, Hoblidar A. Temporal feature extraction and machine learning for classification of Sleep stages using telemetry polysomnography. Brain Sci. 2023;13. https://doi.org/10.3390/brainsci13081201.
Cash SS, Halgren E, Dehghani N, Rossetti AO, Thesen T, Wang CM, Devinsky O, Kuzniecky R, Doyle W, Madsen JR, et al. The human K-Complex represents an isolated cortical down-state. Sci (80-). 2009;324:1084–7. https://doi.org/10.1126/science.1169626.
Massimini M, Huber R, Ferrarelli F, Hill S, Tononi G. The sleep slow oscillation as a traveling Wave. J Neurosci. 2004;24:6862–70. https://doi.org/10.1523/JNEUROSCI.1318-04.2004.
Yousef M, Kumar A, Bakir-Gungor B. Application of biological domain knowledge based feature selection on gene expression data. Entropy. 2021;23:1–15. https://doi.org/10.3390/e23010002
Pretel MR, Vidal V, Kienigiel D, Forcato C, Ramele RA. Low-cost and open-hardware portable 3-electrode polysomnography device. Sleep Comput Comm Jpn Soc SLEEP. 2023. https://doi.org/10.31234/osf.io/6mjyr.
Hori T, Sugita Y, Koga E, Shirakawa S, Inoue K, Uchida S, Kuwahara H, Kousaka M, Kobayashi T, Tsuji Y, 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;55:305–10. https://doi.org/10.1046/j.1440-1819.2001.00810.x.
Rieke N, Hancox J, Li W, Milletarì F, Roth HR, Albarqouni S, Bakas S, Galtier MN, Landman BA, Maier-Hein K, et al. The future of digital health with federated learning. Npj Digit Med. 2020;3. https://doi.org/10.1038/s41746-020-00323-1.
Comments (0)