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EEG signal-based sleep stage classification facilitates an initial diagnosis of sleep disorders. The aim of this study was to compare the efficiency of three methods for feature extraction: power spectral density (PSD), discrete wavelet transform (DWT) and empirical mode decomposition (EMD) in the automatic classification of sleep stages by an artificial neural network (ANN). 13650 30-second EEG epochs from the PhysioNet database, representing five sleep stages (W, N1-N3 and REM), were transformed into feature vectors using the aforementioned methods and principal component analysis (PCA). Three feed-forward ANNs with the same optimal structure (12 input neurons, 23 + 22 neurons in two hidden layers and 5 output neurons) were trained using three sets of features, obtained with one of the compared methods each. Calculating PSD from EEG epochs in frequency sub-bands corresponding to the brain waves (81.1% accuracy for the testing set, comparing with 74.2% for DWT and 57.6% for EMD) appeared to be the most effective feature extraction method in the analysed problem.
Czasopismo
Rocznik
Tom
Strony
229--240
Opis fizyczny
Bibliogr. 49 poz., rys., tab., wykr., wzory
Twórcy
autor
- Wrocław University of Science and Technology, Faculty of Electronics, B. Prusa 53/55, Wrocław, Poland
autor
- Wrocław University of Science and Technology, Faculty of Electronics, B. Prusa 53/55, Wrocław, Poland
Bibliografia
- [1] Berry, R.B., Brooks, R., Gamaldo, C.E., Harding, S.M., Lloyd, R.M., Marcus, C.L., Vaughn, B.V. (2015). The American Academy of Sleep Medicine Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, Version 2.2. Darien, Illinois: American Academy of Sleep Medicine.
- [2] Jabłoński, I. (2013). Modern methods for description of complex couplings in neurophysiology of respiration. IEEE Sensors J., 13, 3182-3192.
- [3] Polak, A.G., Głomb, G., Guszkowski, T., Jabłoński, I., Kasprzak, B., Pękala, J., Stępień, A.F., Świerczyński, Z., Mroczka, J. (2009). Development of a telemedical system for monitoring patients with chronic respiratory diseases. In: O. Dossel and W.C. Schlegel (Eds): World Congress on Medical Physics and Biomedical Engineering, IFMBE Proceedings, Springer, 25/V, 51-54.
- [4] Loomis, A.L., Harvey, E.N., Hobart, G. (1937). Cerebral states during sleep, as studied by human brain potentials. J. Exp. Psychol., 21(2), 127-144.
- [5] Kleitman, N., Asernisky, E. (1953). Regularly occurring periods of eye motility, and concomitant phenomena, during sleep. Science, 118(3062), 273-274.
- [6] Chokroverty, S., Thomas, R., Bhatt, M. (2014). Atlas of Sleep Medicine. Philadelphia: Elsevier Saunders.
- [7] Hwang, S.H., Lee, Y.J., Jeong, D.U., Park, K.S. (2016). Apnea-hypopnea index estimation using quantitative analysis of sleep macrostructure. Physiol. Meas., 37, 554-563.
- [8] Attarian, H.P., Undevia, N.S. (2012). Atlas of Electroencephalography in Sleep Medicine. New York: Springer.
- [9] Berger, H. (1929). Uber das Elektrnkephalogramm des Menschen. Arch Psychiat Nernenkr, 87, 527-570.
- [10] Rechtschaffen, A., Kales, A. (1968). A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. Los Angeles: Brain Information Service.
- [11] Hsu, Y.L., Yang, Y.T., Wang, J.S., Hsu, Ch.Y. (2013). Automatic sleep stage recurrent neural classifier using energy features of EEG signals. Neurocomputing. 104, 105-114.
- [12] Boostani, R., Karimzadeh, F., Nami, M. (2017). A comparative rewiev on sleep stage classification methods in patients and healthy individuals. Comput. Methods Programs Biomed., 140, 77-91.
- [13] Jabłoński, I., Mroczka, J. (2009). Frequency-domain identification of the respiratory system during airflow interruption. Measurement, 42, 390-398.
- [14] Jabłoński, I., Polak A.G., Mroczka, J. (2011). A preliminary study on the accuracy of respiratory input measurement using the interrupter technique. Comput. Methods Programs Biomed., 101, 115-125.
- [15] Ebrahimi, F., Mikaeili, M., Estrada, E., Nazeran, H. (2008). Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients. Conf. Proc. IEEE Eng. Med. Biol. Soc., 1151-1154.
- [16] Sen, B., Peker, M., Cavusoglu, A., Celebi, F. (2014). A Comparative Study on Classification of Sleep Stage Based on EEG Signals Using Feature Selection and Classification Algorithms. J. Med. Syst., 38, 18.
- [17] Diykh, M., Li, Y. (2016). Complex networks approach for EEG signal sleep stages classification. Expert Syst. Appl., 63, 241-248.
- [18] Peker, M. (2016). A new approach for automatic sleep scoring: Combining Taguchi based complex-valued neural network and complex wavelet transform. Comput. Methods Programs Biomed., 129, 203-216.
- [19] Hassan, A.R., Bhuiyan, M.I.H. (2016). Computer-aided sleep staging using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and bootstrap aggregating. Biomed. Signal Process. Control., 24, 1-10.
- [20] Yucelbas, S., Ozsen, S., Yucelbas, C., Tezel, G., Kuccukturk, S., Yosunkaya, S. (2016). Effect of EEG Time Domain Features on the Classification of Sleep Stages. Indian J. Sci. Technol., 9, 1-8.
- [21] Oh, S.H., Lee, Y.R., Kim, H.N. (2014). A Novel EEG Feature Extraction Method Using Hjorth Parameter. J. Electron. Electr. Eng., 2, 106-110.
- [22] Mohammadi, S.M., Kouchaki, S., Ghavami, M., Sanei, S. (2016). Improving time-frequency domain sleep EEG classification via singular spectrum analysis. J. Neurosci. Methods, 273, 96-106.
- [23] Lee, J., Yoo, S. (2013). Electroencephalography Analysis Using Neural Network and Support Vector Machine during Sleep. Engineering, 5, 88-92.
- [24] Dong, H., Supratak, A., Pan, W., Wu, Ch., Matthews, P., Guo, Y. (2016). Mixed neural network approach for temporal sleep stage classification. arXiv preprint arXiv:1610.06421.
- [25] Ronzhina, M., Janousek, O., Kolarova, J., Novakova, M., Honzik, P., Provaznik, I. (2012). Sleep scoring using artificial neural networks. Sleep Med. Rev., 16, 251-263.
- [26] Sanders, T.H., McCurry, M., Clements, M.A. (2014). Sleep Stage Classification with Cross Frequency Coupling. Conf. Proc. IEEE Eng. Med. Biol. Soc., 2014, 4579-82.
- [27] Khan, N.A., Ali, S. (2016). Classification of EEG signal using adaptive time-frequency distributions. Metrol. Meas. Syst., 2(23), 251‒260.
- [28] Malinowska, U., Durka, P., Blinowska, K.J., Szelenberger, W., Wakarow, A. (2006). Micro- and Macrostructure of Sleep EEG. IEEE Eng. Med. Biol. Mag., 25, 26-31.
- [29] Silveira, T.L.T., Kozakevicius, A.J., Rodrigues, C.R. (2017). Single channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain. Med. Biol. Eng. Comput., 55, 343-352.
- [30] Tsinalis, O., Matthews, P.M., Guo, Y., Zafeiriou, S. (2016). Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks. Ann. Biomed. Eng., 44, 1587-1597.
- [31] Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.Ch., Tung, Ch.Ch., Liu, H.H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A, 454, 903-995.
- [32] Djemili, R., Bourouba, H., Korba, M.C.A. (2016). Application of empirical mode decomposition and artificial neural network for the classification of normal and epileptic EEG signals. Biocybern. Biomed. Eng., 36, 285-291.
- [33] Hassan, A.R., Bhuiyan, M.I.H. (2016). Automatic sleep scoring using statistical features in the EMD domain and ensemble methods. Biocybern. Biomed. Eng., 36, 248-255.
- [34] Hassan, A.R., Bhuiyan, M.I.H. (2017). Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting. Comput. Methods Programs Biomed., 140, 201-210.
- [35] Bajaj, V., Pachori, R.B. (2012). Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans. Inf. Technol. Biomed., 16, 1135-1142.
- [36] Liu, Y., Yan, L., Zeng, B., Wang, W. (2010). Automatic Sleep Stage Scoring using Hilbert-Huang Transform with BP Neural Network. Proceedings of ICBEE, 1-4.
- [37] Becq, G., Charbonnier, S., Chapotot, F., Buguet, a., Bourdon, L., Baconnier, P. (2005). Comparison Between Five Classifiers for Automatic Scoring of Human Sleep Recordings. Stud. Comput. Intell., 4, 113-127.
- [38] Wu, H.T., Talmon, R., Lo, Y.L. (2015). Assess Sleep Stage by Modern Signal Processing Techniques. IEEE Trans. Biomed. Eng., 62, 1159-1168.
- [39] Pinero, P., Garcia, P., Arco, L., Alvarez, A., Garcia, M.M., Bonal, R. (2004). Sleep stage classification using fuzzy sets and machine learning techniques. Neurocomputing, 58−60, 1137-1143.
- [40] Yulita, I.N., Fanany, M.I., Arymurthy, A.M. (2016). Sequence-based sleep stage classification using conditional neural fields. arXiv preprint arXiv:1610.01935.
- [41] Güneş, S., Polat, K., Yosunkaya, S., Dursun, M. (2009). A novel data pre-processing method on automatic determining of sleep stages: K-means clustering based feature weighting. Complex Syst. Appl. ICCSA, 112-117.
- [42] Peker, M. (2016). An efficient sleep scoring system based on EEG signal using complex-valued machine learning algorithms. Neurocomputing, 207, 165-177.
- [43] Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.Ch., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101, 215-220.
- [44] Varsavsky, A., Mareels, I., Cook, M. (2011). Epileptic Seizures and the EEG: Measurement, Models, Detection and Prediction. Boca Raton: CRC Press Taylor & Francis Group.
- [45] Welch, P.D. (1967). 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., 15, 70-73.
- [46] Adeli, H., Zhou, Z., Dadmehr, N. (2003). Analysis of EEG records in an epileptic patient using wavelet transform. J. Neurosci. Methods, 123, 69-87.
- [47] Subasi, A. (2005). Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients. Expert Syst. Appl., 28, 701-711.
- [48] Abdi, H., Williams, L.J. (2010). Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat., 2, 433-459.
- [49] Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Math. Control Signals Syst., 2, 303-314.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0d5cedea-da16-461b-b8df-8e3b32998138