A Novel Continuous Sleep State Artificial Neural Network Model Based on Multi-Feature Fusion of Polysomnographic Data

References

• Mellinger Glen D. Insomnia and its treatment. Arch Gen Psychiatry. 1985;42(3):225. doi:10.1001/archpsyc.1985.01790260019002
   PubMed Web of Science ®Google Scholar
• Charbonnier S, Zoubek L, Lesecq S, Chapotot F. Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging. Comput Biol Med. 2011;41(6):380–389. doi:10.1016/j.compbiomed.2011.04.001
   PubMed Web of Science ®Google Scholar
• Geng D, Zhao J, Dong J, Jiang X, Gómez C, Schwarzacher SP. Comparison of support vector machines based on particle swarm optimization and genetic algorithm in sleep staging. Technol Health Care. 2019;27(S1):143–151. doi:10.3233/THC-199014
   PubMedGoogle Scholar
• Walthert L, Bauerfeind A, Kumar S. An automated random forest algorithm for sleep staging using advanced cardiorespiratory and movement features. Sleep Med. 2019;64(S1):S206–S206.
   Google Scholar
• Huang W, Guo B, Shen Y, et al. Sleep staging algorithm based on multichannel data adding and multifeature screening. Comput Methods Programs Biomed. 2020;187:105253. doi:10.1016/j.cmpb.2019.105253
   PubMed Web of Science ®Google Scholar
• Hassan AR, Bhuiyan MIH. Automatic sleep stage classification. Paper presented at: International Conference on Electrical Information & Communication Technology; 2016.
   Google Scholar
• Jia-Yi GE, Peng Z, Xin Z, Hai-Ying L. Multiscale entropy analysis of EEG signal. Com Eng App. 2009;45(10):13–15.
   Google Scholar
• Larsen L, Walter D. Classification of sleep stages by EEG spectra; 1969.
   Google Scholar
• Vincent LC-F, Jaeseung J. Quantification of brain macrostates using dynamical nonstationarity of physiological time series. IEEE Trans Bio-Med Eng. 2011;58(4):1084–1093. doi:10.1109/TBME.2009.2034840
   PubMed Web of Science ®Google Scholar
• Fraiwan L, Lweesy K, Khasawneh N, Wenz H, Dickhaus H. Automated sleep stage identification system based on time–frequency analysis of a single EEG channel and random forest classifier. Comput Methods Programs Biomed. 2012;108(1):10–19. doi:10.1016/j.cmpb.2011.11.005
   PubMed Web of Science ®Google Scholar
• Weiss B, Clemens Z, Bódizs R, Halász P. Comparison of fractal and power spectral EEG features: effects of topography and sleep stages. Brain Res Bull. 2010;84(6):359–375. doi:10.1016/j.brainresbull.2010.12.005
   PubMed Web of Science ®Google Scholar
• K D-K, K D, L J-G, W Y, J J. Deep learning application to clinical decision support system in sleep stage classification. Sleep Med. 2022;100(S1):136.
   Google Scholar
• Fernando -V-V, G-tg C, Eva 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;165:107419. doi:10.1016/j.compbiomed.2023.107419
   PubMed Web of Science ®Google Scholar
• R M, H R, K H, et al. Generalizable deep learning-based sleep staging approach for ambulatory textile electrode headband recordings. IEEE journal of biomedical and health informatics; 2023.
   Google Scholar
• Wei L, Lin Y, Wang J, Ma Y. Time-frequency convolutional neural network for automatic sleep stage classification based on single-channel EEG. In: 2017 IEEE 29th International Conference on Tools with Artificial Intelligence. IEEE; 2017.
   Google Scholar
• Emin TM, Necmettin S, Mehmet A. Estimation of sleep stages by an artificial neural network employing EEG, EMG and EOG. J Med Syst. 2010;34(4):717–725. doi:10.1007/s10916-009-9286-5
   PubMed Web of Science ®Google Scholar
• Sors A, Bonnet S, Mirek S, Vercueil L, Payen J-F. A convolutional neural network for sleep stage scoring from raw single-channel EEG. Biomed Signal Process Control. 2018;42:107–114. doi:10.1016/j.bspc.2017.12.001
   Web of Science ®Google Scholar
• Hsu Y-L, Yang Y-TC, Wang J-S, Hsu C-Y. Automatic sleep stage recurrent neural classifier using energy features of EEG signals. Neurocomputing. 2013;104:105–114. doi:10.1016/j.neucom.2012.11.003
   Web of Science ®Google Scholar
• Akara S, Hao D, Chao W, Yike G. DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Trans Neural Syst Rehabilit Eng. 2017;25(11):1998–2008. doi:10.1109/TNSRE.2017.2721116
   PubMed Web of Science ®Google Scholar
• Jaemin J, Wonhyuck Y, JeongGun L, et al. Standardized image-based polysomnography database and deep learning algorithm for sleep stage classification. Sleep. 2023;46(12):zsad242.
   PubMed Web of Science ®Google Scholar
• Dongrae C, Boreom L. Automatic sleep-stage classification based on residual unit and attention networks using directed transfer function of electroencephalogram signals. Biomed Sig Process Control. 2024;88(PB):105679.
   Google Scholar
• Helli M, Fortune RD. The neuronal transition probability (NTP) model for the dynamic progression of non-REM sleep EEG: the role of the suprachiasmatic nucleus. PLoS One. 2011;6(8):e23593.
   PubMed Web of Science ®Google Scholar
• Uchida S, Maloney T, Feinberg I. Beta (20–28 Hz) and delta (0.3–3 Hz) EEGs oscillate reciprocally across NREM and REM sleep. Sleep. 1992;15(4):352–358. doi:10.1093/sleep/15.4.352
   PubMed Web of Science ®Google Scholar
• Uchida S, Maloney T, March JD, Azari R, Feinberg I. Sigma (12–15 Hz) and delta (0.3–3 Hz) EEG oscillate reciprocally within NREM sleep. Brain Res Bull. 1991;27(1):93–96. doi:10.1016/0361-9230(91)90286-S
   PubMed Web of Science ®Google Scholar
• Pardey J, Roberts S, Tarassenko L, Stradling J. A new approach to the analysis of the human sleep/wakefulness continuum. J Sleep Res. 2010;5(4):201–210. doi:10.1111/j.1365-2869.1996.00201.x
   Google Scholar
• Asyali MH, Berry RB, Khoo MCK, Altinok A. Determining a continuous marker for sleep depth. Comput Biol Med. 2007;37(11):1600–1609. doi:10.1016/j.compbiomed.2007.03.001
   PubMed Web of Science ®Google Scholar
• Magdy Y, Michele O, Marc S, et al. Odds ratio product of sleep EEG as a continuous measure of sleep state. Sleep. 2015;38(4):641–654. doi:10.5665/sleep.4588
   PubMed Web of Science ®Google Scholar
• Magdy Y, Ali A, Michelle R, Md R, Susan R. Characteristics and reproducibility of novel sleep EEG biomarkers and their variation with sleep apnea and insomnia in a large community-based cohort. Sleep. 2021;44(10):zsab145.
   PubMed Web of Science ®Google Scholar
• Magdy Y, Bethany G, Pack AI, Kuna ST, Cecilia C, Susan R. Sleep architecture based on sleep depth and propensity: patterns in different demographics and sleep disorders and association with health outcomes. Sleep. 2022;45(6):zsac059.
   PubMed Web of Science ®Google Scholar
• Magdy Y, Bethany G, Eleni G, et al. Contribution of obstructive sleep apnea to disrupted sleep in a large clinical cohort of patients with suspected OSA. Sleep. 2023;46(7):zsac321.
   PubMed Web of Science ®Google Scholar
• Bethany G, Ks T, Allan P, et al. An approach for determining the reliability of manual and digital scoring of sleep stages. Sleep. 2023;46(11):zsad248.
   PubMed Web of Science ®Google Scholar
• Sultan Q, Hani M, Faris A, et al. The prevalence of rapid eye movement-related obstructive sleep apnea in a sample of Saudi population. Ann Thorac Med. 2023;18(2):90–97. doi:10.4103/atm.atm_388_22
   PubMed Web of Science ®Google Scholar
• Anna R, Cs L, Fan H, et al. Association of a novel EEG metric of sleep depth/intensity with attention-deficit/hyperactivity, learning and internalizing disorders and their pharmacotherapy in adolescence. Sleep. 2021;45(3):zsab287.
   Web of Science ®Google Scholar
• Julio FM, Anna R, Fan H, et al. 0254 association of slow wave activity and odds ratio product with internalizing and externalizing problems in children and adolescents. Sleep. 2022(Supplement_1):A114.
   Google Scholar