Automatic Sleep Stage Scoring with Wavelet Packets Based on Single EEG Recording
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.
[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.
[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.
@article{"International Journal of Medical, Medicine and Health Sciences:58264", author = "Luay A. Fraiwan and Natheer Y. Khaswaneh and Khaldon Y. Lweesy", title = "Automatic Sleep Stage Scoring with Wavelet Packets Based on Single EEG Recording", 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.", volume = "3", number = "6", pages = "72-4", }