Abstract
The diagnosis of sleep related disorders like sleep apnea, insomnia, restless legs syndrome, begins with grading sleep into stages to analyze the problem. The R&K rules recommend dividing the polysomnographic record of sleep consisting of EEG, EOG and EMG into 30 s epochs and classifying them as Stage 1, 2, 3, 4, Rapid Eye Movement (REM) and Wake state. In this paper, data from a single EEG electrode are decomposed into its wavelet coefficients (Daubechies wavelet from 2 to 6). Instead of using statistical parameters like entropy, energy, etc. of the coefficients as features, the coefficients are directly applied as input to a neural network for classification. Prior to training the neural network, the high dimensional input data are reduced to its principal components. The proposed method helps in isolating Stage 3 and 4, rather than identifying them as a combined Slow Wave Stage (SWS). Best results were obtained using Daubechies 2 wavelet, with an overall accuracy of 86%.
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References
Khandpur, R.S.: Handbook of Biomedical Instrumentation. Tata McGraw-Hill Education, New York City (1992)
Ebrahimi, F., Mikaeili, M., Estrada, E., Nazeran, H.: Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients. In: 30th Annual International IEEE EMBS Conference Canada (2008)
Rechtschaffen, A., Kales, A.: A Manual of Standardized Terminology, Techniques, and Scoring System for Sleep Stages of Human Subjects. UCLA, Brain Research Institute/Brain Information Service, Los Angeles (1968)
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. 108, 10–19 (2012)
Koley, B., Dey, D.: An ensemble system for automatic sleep stage classification using single channel EEG signal. Comput. Biol. Med. 42(12), 1186–1195 (2012)
Tagluk, M.E., Sezgin, N., Akin, M.: Estimation of sleep stages by an artificial neural network employing EEG, EMG and EOG. J. Med. Syst. 34(4), 717–725 (2010)
Sinha, R.K.: Artificial neural network and wavelet based automated detection of sleep spindles, REM sleep and Wake States. J. Med. Syst. 32(4), 291–299 (2008)
Prochazka, A., Mudrova, M., Vysata, O., Hava, R., Araujo, C.P.S.: Multi-channel EEG signal segmentation and feature extraction. In: 14th International Conference on Intelligent Engineering Systems (INES), pp. 317–320 (2010)
Gandhi, T., Panigrahi, B.K., Anand, S.: A comparative study of wavelet families for EEG signal classification. Neurocomputing 74(17), 3051–3057 (2011)
Ronzhina, M., Janousek, O., Kolarova, J., Novakova, M., Honzik, P., Provaznik, I.: Sleep scoring using artificial neural networks. Sleep Med. Rev. 16(3), 251–263 (2012)
Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), 215–220 (2000)
Kemp, B., Zwinderman, A.H., Tuk, B., Kamphuisen, H.A.C., Oberye, J.J.L.: Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Trans. Biomed. Eng. 47(9), 1185–1194 (2000)
Ghodsi, A.: Dimensionality reduction: a short tutorial. Technical report, University of Waterloo (2006)
Navon, I.M., Legler, D.M.: Conjugate-gradient methods for large-scale minimization in meteorology. Mon. Weather Rev. 115, 1479–1502 (1987)
Susmskova, K.: Human sleep and sleep EEG. Measur. Sci. Rev. 4(2), 59–74 (2004)
Sen, B., Peker, M., Cavusoglu, A., Celebi, F.: A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms. J. Med. Syst. 38, 18 (2014)
Radha, M., Garcia-Molina, G., Poel, M., Tononi, G.: Comparison of feature and classifier algorithms for online automatic sleep staging based on a single EEG Signal. In: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1876–1880 (2014)
Leskovec, J., Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, Cambridge (2014)
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 104, 105–114 (2013)
Schalkoff, R.J.: Artificial Neural Network. McGraw-Hill, New York City (2013)
Rojas, R.: Neural Networks. Springer, Heidelberg (1996)
Mallat, S.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)
Addision, P.S.: The Illustrated Wavelet Transfor Handbook Introductor Theory and Appication in Science, Engineering, Medicine and Finance. IOP Publishing Ltd., Bristol (2002)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Vigon, L., Saatchi, M.R., Mayhew, J.E.W., Fernandes, R.: Quantitative evaluation of techniques for ocular artefact filtering of EEG waveforms. IEEE Proc. Sci. Measur. Technol. 147(5), 219–228 (2000)
Hyvrinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural Netw. 13(4–5), 411–430 (2000)
Vigario, R., Sarela, J., Jousmiki, V., Hamalainen, M., Oja, E.: Independent component approach to the analysis of EEG and MEG recordings. IEEE Trans. Biomed. Eng. 47(5), 589–593 (2000)
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Crasto, N., Upadhyay, R. (2017). Wavelet Decomposition Based Automatic Sleep Stage Classification Using EEG. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10208. Springer, Cham. https://doi.org/10.1007/978-3-319-56148-6_45
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