A Sleep Scoring System Using EEG Combined Spectral and Detrended Fluctuation Analysis Features

Amr F. Farag; Shereen M. El-Metwally; Ahmed A. Morsy

doi:10.4236/jbise.2014.78059

Home > Journals > Biomedical & Life Sciences > JBiSE

Journal of Biomedical Science and Engine... > Vol.7 No.8, June 2014

A Sleep Scoring System Using EEG Combined Spectral and Detrended Fluctuation Analysis Features ()

Amr F. Farag, Shereen M. El-Metwally, Ahmed A. Morsy

Department of Biomedical Engineering, Cairo University, Cairo, Egypt.

DOI: 10.4236/jbise.2014.78059 PDF HTML 3,160 Downloads 4,137 Views Citations

Abstract

Most of sleep disorders are diagnosed based on the sleep scoring and assessments. The purpose of this study is to combine detrended fluctuation analysis features and spectral features of single electroencephalograph (EEG) channel for the purpose of building an automated sleep staging system based on the hybrid prediction engine model. The testing results of the model were promising as the classification accuracies were 98.85%, 92.26%, 94.4%, 95.16% and 93.68% for the wake, non-rapid eye movement S1, non-rapid eye movement S2, non-rapid eye movement S3 and rapid eye movement sleep stages, respectively. The overall classification accuracy was 85.18%. We concluded that it might be possible to employ this approach to build an industrial sleep assessment system that reduced the number of channels that affected the sleep quality and the effort excreted by sleep specialists through the process of the sleep scoring.

Keywords

Automated Sleep Staging, Detrended Fluctuation Analysis (DFA), Decision Tree, Multi-Layer Perceptron (MLP)

Share and Cite:

Farag, A. , El-Metwally, S. and Morsy, A. (2014) A Sleep Scoring System Using EEG Combined Spectral and Detrended Fluctuation Analysis Features. Journal of Biomedical Science and Engineering, 7, 584-592. doi: 10.4236/jbise.2014.78059.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Rechtschaffen, A. and Kales, A. (1968) A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. Public Health Service, US Government Printing Office, Washington DC.
[2]	Hopfe, H., Anderer, P., Zeitlohfer, J., Boeck, M., Dorn, H., Gruber, G., Heller, E., Loretz, E., Moser, D., Paraptics, S., Saletu, B., Schmidt, A. and Dorffner, G. (2009) Interraterreliablity for Sleep Scoring According to the Rechtschaffen & Kales and the New AASM Standard. Journal of Sleep Research, 18, 74-84. http://dx.doi.org/10.1111/j.1365-2869.2008.00700.x
[3]	Danker-Hopfe, H., Kunz, D., Gruber, G., Klösch, G., Lorenzo, J.L. and Himanen, S.L. (2004) Interrater Reliability between Scorers from Eight European Sleep Laboratories in Subjects with Different Sleep Disorders. Journal of Sleep Research, 13, 63-69. http://dx.doi.org/10.1046/j.1365-2869.2003.00375.x
[4]	Penzel, T. (2003) Problems in Automatic Sleep Scoring Applied to Sleep Apnea. Engineering in Medicine and Biology Society, Proceedings of the 25th Annual International Conference of the IEEE, Cancun, 17-21 September 2003, 358-361.
[5]	Liang, S.F., Kuo, C.E., Huo, Y.H. and Cheng, Y.C. (2012) A Rule-Based Automatic Sleep Staging Method. Journal of Neuroscience Methods, 205, 169-176.
[6]	Vivaldi, E.A. and Bassi, A. (2006) Frequency Domain Analysis of Sleep EEG for Visualization and Automated State Detection. Proceedings of the 28th IEEE EMBS Annual International Conference, 1, 3740-3743.
[7]	Charbonnier, S., Zoubek, L., Lesecq, S. and Chapotot, F. (2011) Self-Evaluating Automatic Classifier as a Decision. Computers and Biology in Medicine, 41, 380-389. http://dx.doi.org/10.1016/j.compbiomed.2011.04.001
[8]	Estrada, E., Nazeran, H., Nava, P., Behbehani, K., Burk, J. and Lucas, E. (2004) EEG Feature Extraction for Classification of Sleep Stages. Proceedings of the 26th Annual International Conference of the IEEE EMBS, 1, 196-199.
[9]	Pardey, J., Roberts, S. and Tarassenko, L. (1994) Application of Artificial Neural Networks to Medical Signal Processing. IEEE Savoy Place, London.
[10]	Takajyol, A., Katayama, M., Inoue, K., Kumamaru, K. and Matsuoka, S. (2006) Time-Frequency Analysis of Human Sleep EEG. SICEICASE International Joint Conference, Busan, 18-21 October 2006, 3303-3307.
[11]	Li, J., Du, Y. and Zhao, L. (2005) Sleep Stage Study with Wavelet Time-Frequency Analysis. International Conference Neural Networks and Brain, Beijing, 13-15 October 2005, 872-875.
[12]	Glavinovitch, A., Swamy, M.N.S. and Plotkin, E.I. (2007) Wavelet-Based Segmentation Techniques in the Detection of Microarousals in the Sleep EEG. 50th Midwest Symposium on Circuits and Systems, 2, 1302-1305.
[13]	Hjorth, B (1970) EEG Analysis Based on Time Domain Properties. Electroencephalogr. Clinical Neurophysiology, 29, 306-310. http://dx.doi.org/10.1016/0013-4694(70)90143-4
[14]	Gunes, S., Polat, K. and Yosunkaya, S. (2010) Efficient Sleep Stage Recognition System Based on EEG Signal Using k-Means Clustering Based Feature Weighting. Expert Systems with Applications, 37, 7922-7928. http://dx.doi.org/10.1016/j.eswa.2010.04.043
[15]	Koley, B. and Dey, D. (2012) An Ensemble System for Automatic Sleep Stage Classification Using Single Channel EEG Signal. Computers in Biology and Medicine, 42, 1186-1195. http://dx.doi.org/10.1016/j.eswa.2010.04.043
[16]	Adanen, M., Jiang, Z. and Yan, Z. (2012) Sleep-Wake Stages Classification and Sleep Efficiency Estimation Using Single-Lead Electrocardiogram. Expert Systems with Applications, 39, 1401-1413. http://dx.doi.org/10.1016/j.eswa.2011.08.022
[17]	Ping, C.K., Havlin, S., Stanley, H.E. and Goldberger, A.L. (1995) Quantification of Scaling Exponents and Cross over Phenomena in Non-Stationary Heartbeat Time Series. Chaos, 5, 82-87.
[18]	Lee, J.M., Kim, D.J., Kim, I.Y., Park, K.S. and Kim, S.I. (2002) Detrended Fluctuation Analysis of EEG in Sleep Apnea Using MIT/BIH Polysomnography Data. Computers in Biology and Medicine, 32, 37-47. http://dx.doi.org/10.1016/S0010-4825(01)00031-2
[19]	Farag, A.F. and EL-Metwally, S.M. (2012) Detreneded Fluctuation Analysis Features for Automated Sleep Staging of Sleep EEG. International Journal of Biology and Biomedical Technology, 4, 48-60.
[20]	Sun, M., Ryan, N.D., Dahl, R.E., Hsin, H.C., lyengar, S. and Sclabassi, R.J. (1993) A Neural Network System for Automatic Classification of Sleep Stages. Proceedings of the 12th Southern Biomedical Engineering Conference, 137-139.
[21]	Jo, H.G., Park, J.Y., Lee, C.K., An, S.K. and Yoo, S.K. (2010) Genetic Fuzzy Classifier for Sleep Stage Identification. Computers in Biology and Medicine, 40, 629-634. http://dx.doi.org/10.1016/j.compbiomed.2010.04.007
[22]	Hanaoka, M., Ashi, M.K. and Yamazaki, Y. (2001) Automated Sleep Scoring by Decision Tree Learning. Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2, 1751-1754.
[23]	Fraiwan, L., Lweesy, K., Khasawneh, N., Wenz, H. and Dickhaus, H. (2012) Automated Sleep Stage Identification System Based on Time-Frequency Analysis of a Single EEG Channel and Random Forest Classifier. Computer Methods and Programs in Biomedicine, 108, 10-19. http://dx.doi.org/10.1016/j.cmpb.2011.11.005
[24]	Alpaydin, E. (2010) Introduction to Machine Learning. 2nd Edition, The MIT Press, Cambridge, 187-200.
[25]	Kantelhardt, J.W., Bunde, E.K., Rego, H.H.A., Havlin, S. and Bunde, A. (2001) Detecting Long-Range Correlations with Detrended Fluctuation Analysis. Physica A, 295, 441-454. http://dx.doi.org/10.1016/S0378-4371(01)00144-3
[26]	Quinlan, J.R. (1992) C4.5 Programs for Machine Learning. Morgan Koufmann, San Mateo.
[27]	Quinlan, J.R. (1986) Induction of Decision Trees. Machine Learning, 1, 81-106. http://dx.doi.org/10.1007/BF00116251
[28]	Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986) Learning Representations by Back-Propagating Errors. Nature, 323, 533-536. http://dx.doi.org/10.1038/323533a0