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Biocybernetics and Biomedical Engineering

Tytuł artykułu

Automatic sleep scoring using statistical features in the EMD domain and ensemble methods

Autorzy Hassan, A. R.  Bhuiyan, M. I. H. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
EN An automatic sleep scoring method based on single channel electroencephalogram (EEG) is essential not only for alleviating the burden of the clinicians of analyzing a high volume of data but also for making a low-power wearable sleep monitoring system feasible. However, most of the existing works are either multichannel or multiple physiological signal based or yield poor algorithmic performance. In this study, we propound a data-driven and robust automatic sleep staging scheme that uses single channel EEG signal. Decomposing the EEG signal segments using Empirical Mode Decomposition (EMD), we extract various statistical moment based features. The effectiveness of statistical features in the EMD domain is inspected. Statistical analysis is performed for feature selection. We then employ Adaptive Boosting and decision trees to perform classification. The performance of our feature extraction scheme is studied for various choices of classification models. Experimental outcomes manifest that the performance of the proposed sleep staging algorithm is better than that of the state-of-the-art ones. Furthermore, the proposed method's non-REM 1 stage detection accuracy is better than most of the existing works.
Słowa kluczowe
PL EEG   AdaBoost   uczenie zespołowe   empiryczna dekompozycja sygnału  
EN EEG   AdaBoost   ensemble learning   sleep scoring   empirical mode decomposition  
Wydawca Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Czasopismo Biocybernetics and Biomedical Engineering
Rocznik 2016
Tom Vol. 36, no. 1
Strony 248--255
Opis fizyczny Bibliogr. 34 poz., tab., wykr.
autor Hassan, A. R.
  • Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, 508/3, South Goran, Dhaka 1219, Bangladesh,
autor Bhuiyan, M. I. H.
  • Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
[1] Siegel JM. Clues to the functions of mammalian sleep. Nature 2005;437(7063):1264–71.
[2] Mahowald MW, Schenck CH. Insights from studying human sleep disorders. Nature 2005;437(7063):1279–85.
[3] Rechtschaffen A, Kales A. Manual of standardized terminology, techniques and scoring systems for sleep stages of human subjects. U. G. P. Office, Washington DC Public Health Service; 1968.
[4] Iber ALCC, Ancoli-Israel S, Quan SF. The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specification. Westchester, USA: American Academy of Sleep Medicine; 2005.
[5] Imtiaz S, Rodriguez-Villegas E. A low computational cost algorithm for REM sleep detection using single channel EEG. Ann Biomed Eng 2014;42(11):2344–59.
[6] Liang S-F, Kuo C-E, Hu Y-H, Pan Y-H, Wang Y-H. Automatic stage scoring of single-channel sleep EEG by using multiscale entropy and autoregressive models. IEEE Trans Instrum Meas 2012;61(6):1649–57.
[7] Zhu G, Li Y, Wen P. Analysis and classification of sleep stages based on difference visibility graphs from a single channel EEG signal. IEEE J Biomed Health Inform 2014;99:1.
[8] Lin C-T, Ko L-W, Chiou J-C, Duann J-R, Huang R-S, Liang S-F, et al. Noninvasive neural prostheses using mobile and wireless EEG. Proc IEEE 2008;96(7):1167–83.
[9] Agarwal R, Gotman J. Computer-assisted sleep staging. IEEE Trans Biomed Eng 2001;48(12):1412–23.
[10] Held C, Heiss J, Estevez P, Perez C, Garrido M, Algarin C, et al. Extracting fuzzy rules from polysomnographic recordings for infant sleep classification. IEEE Trans Biomed Eng 2006;53(10):1954–62.
[11] Berthomier C, Drouot X, Herman-Stoca M, Berthomier P, Prado J, Bokar-Thire D, et al. Automatic analysis of single-channel sleep EEG: validation in healthy individuals. Sleep 2007;30(11):1587–95.
[12] Krakovsk A, Mezeiov K. Automatic sleep scoring: a search for an optimal combination of measures. Artif Intell Med 2011;53(1):25–33.
[13] 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):380–9.
[14] Ronzhina M, Janouek O, Kolrov J, Novkov M, Honzk P, Provaznk I. Sleep scoring using artificial neural networks. Sleep Med Rev 2012;16(3):251–63.
[15] 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 Prog Biomed 2012;108(1):10–9.
[16] Hsu Y-L, Yang Y-T, Wang J-S, Hsu C-Y. Automatic sleep stage recurrent neural classifier using energy features of EEG signals. Neurocomputing 2013;104:105–14.
[17] Koch H, Christensen JA, Frandsen R, Zoetmulder M, Arvastson L, Christensen SR, et al. Automatic sleep classification using a data-driven topic model reveals latent sleep states. J Neurosci Methods 2014;235(0):130–7.
[18] Bajaj V, Pachori R. Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans Inf Technol Biomed 2012;16(6):1135–42.
[19] Fraz M, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka A, Owen C, et al. An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans Biomed Eng 2012;59(9):2538–48.
[20] Kruskal WH, Wallis WA. Use of ranks in one-criterion variance analysis. J Am Stat Assoc 1952;47(260):583–621.
[21] Goldberger AL, Amaral LAN, 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 June 13;101 (23):e215–20.
[22] Kemp B. The Sleep-EDF database online.
[23] Kemp B, Zwinderman A, Tuk B, Kamphuisen H, Oberye J. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Trans Biomed Eng 2000;47(9):1185–94.
[24] Hassan AR, Bashar SK, Bhuiyan MIH. On the classification of sleep states by means of statistical and spectral features from single channel electroencephalogram. 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). 2015. pp. 2238–43.
[25] Hassan AR, Bhuiyan MIH. Computer-aided sleep staging using complete ensemble empirical mode decomposition with adaptive noise and bootstrap aggregating. Biomed Signal Process Control 2016;24:1–10.
[26] Hassan AR, Haque MA. Computer-aided gastrointestinal hemorrhage detection in wireless capsule endoscopy videos. Comput Methods Prog Biomed 2015.
[27] Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 1997;55(1):119–39.
[28] Polikar R. Ensemble based systems in decision making. Circ Syst Mag IEEE 2006;6(3):21–45.
[29] Wolpert DH. The lack of a priori distinctions between learning algorithms. Neural Comput 1996;8(7):1341–90.
[30] Murphy KP. Machine learning: a probabilistic perspective. The MIT Press; 2012.
[31] Song I, Ji Y, Cho B, Ku J, Chee Y, Lee J, et al. Multifractal analysis of sleep EEG dynamics in humans. 3rd International IEEE/EMBS Conference on Neural Engineering, CNE '07. 2007. pp. 546–9.
[32] Khalighi S, Sousa T, Nunes U. Adaptive automatic sleep stage classification under covariate shift. Engineering in Medicine and Biology Society (EMBC); 2012. pp. 2259–62.
[33] Corsi-Cabrera M, Muoz-Torres Z, del Ro-Portilla Y, Guevara M. Power and coherent oscillations distinguish REM sleep, stage 1 and wakefulness. Int J Psychophysiol 2006;60(1):59– 66.
[34] Virkkala J, Hasan J, Vrri A, Himanen S-L, Mller K. Automatic sleep stage classification using two-channel electro- oculography. J Neurosci Methods 2007;166(1):109–15.
PL Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-5470060e-d346-4931-b62a-71feba8afe0a
DOI 10.1016/j.bbe.2015.11.001