Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
Automatic Sleep Stage Scoring with Wavelet Packets Based on Single EEG Recording

Authors: Luay A. Fraiwan, Natheer Y. Khaswaneh, Khaldon Y. Lweesy

Abstract:

Sleep stage scoring is the process of classifying the stage of the sleep in which the subject is in. Sleep is classified into two states based on the constellation of physiological parameters. The two states are the non-rapid eye movement (NREM) and the rapid eye movement (REM). The NREM sleep is also classified into four stages (1-4). These states and the state wakefulness are distinguished from each other based on the brain activity. In this work, a classification method for automated sleep stage scoring based on a single EEG recording using wavelet packet decomposition was implemented. Thirty two ploysomnographic recording from the MIT-BIH database were used for training and validation of the proposed method. A single EEG recording was extracted and smoothed using Savitzky-Golay filter. Wavelet packets decomposition up to the fourth level based on 20th order Daubechies filter was used to extract features from the EEG signal. A features vector of 54 features was formed. It was reduced to a size of 25 using the gain ratio method and fed into a classifier of regression trees. The regression trees were trained using 67% of the records available. The records for training were selected based on cross validation of the records. The remaining of the records was used for testing the classifier. The overall correct rate of the proposed method was found to be around 75%, which is acceptable compared to the techniques in the literature.

Keywords: Features selection, regression trees, sleep stagescoring, wavelet packets.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1072303

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1578

References:


[1] Rechtschaffen A, Kales A. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Public Health Service, U.S. Government Printing Office, Washington DC, 1968.
[2] K. Šušmáková. Human Sleep. Measurement Science Review, Volume 4, Section 2, 59-74, 2004.
[3] Shimada T, Shiina T, Saito Y. Sleep stage diagnosis system with neural network analysis. Engineering in Medicine and Biology Society. 4 (29):2074-2047, 1998.
[4] Penzel T., Hirshkowitz M., Harsh J., Chervin R., Butkov N., Kryger M, Malow B., Vitiello M., Silber M., Kushida C., and Chesson A. (2007) Digital Analysis and Technical Specifications. Journal of Clinical Sleep Medicine, 3 (2), 109-120.
[5] Wang B., Xingyu W., Junzhong Z., Fusae K., and Masatoshi N. Automatic determination of sleep stage through bio-neurological signals contaminated with artifacts by conditional probability of a knowledge base. Artif Life Robotics, 12, 270-275, 2008.
[6] Kubat M, Pfurtscheller G, Flotzinger D. AI-based approach to automatic sleep classification. Biological Cybernetics. 70(5):443-8, 1994.
[7] Hae-Jeong P, Jung-Su O, Do-Un J, and Kwang-Suk P. Automated Sleep Satge Scoring Using Hybrid Rule and Case-Based Reasoning. Computers and Biomedical Research, 33, 330-349, 2000.
[8] Breiman, L., et al. Classification and Regression Trees, Chapman & Hall, Boca Raton, 1993.
[9] PhysioNet (2009), Research Resource for Complex Physiologic Signals. Available online at: http://www.physionet.org/
[10] Orfanidis, S. Introduction to Signal Processing, Prentice-Hall, Englewood Cliffs, NJ, 1996.
[11] Wei-Yen H., Chou-Ching Lin, Min-Shaung Ju, and Yung-Nein S. Wavelet-based fractal features with active segment selection: Application to single -trial EEG data. Journal of Neuroscience Methods 163, 145-160, 2007.
[12] Bang-hua Y., Guo-zheng Y., Ting W., and Rong-guo Y. Subject-based feature extraction using fuzzy wavelet packet in brain-computer interfaces. Signal Processing, 87, 1569-1574, 2007.
[13] Bang-hua Y., Guo-zheng Y., Rong-guo Y., and Ting W. Feature extraction for EEG-based brain-computer interfaces by wavelet packet best basis decomposition. Journal of Neural Engineering, 3, 251-256, 2006.
[14] Hall M., and Smith L. Practical feature subset selection for machine learning. Proceedings of the 21st Australian Computer Science Conference, pp. 181-191, 1998.
[15] Hastie, T. Tibshirani, R, and Friedman, J. The Elements of Statistical Learning, Springer, 2001.
[16] Krzanowski, W. Principles of Multivariate Analysis, Oxford University Press, 1988.