Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31100
A Hybrid Classification Method using Artificial Neural Network Based Decision Tree for Automatic Sleep Scoring

Authors: Haoyu Ma, Bin Hu, Mike Jackson, Jingzhi Yan, Wen Zhao


In this paper we propose a new classification method for automatic sleep scoring using an artificial neural network based decision tree. It attempts to treat sleep scoring progress as a series of two-class problems and solves them with a decision tree made up of a group of neural network classifiers, each of which uses a special feature set and is aimed at only one specific sleep stage in order to maximize the classification effect. A single electroencephalogram (EEG) signal is used for our analysis rather than depending on multiple biological signals, which makes greatly simplifies the data acquisition process. Experimental results demonstrate that the average epoch by epoch agreement between the visual and the proposed method in separating 30s wakefulness+S1, REM, S2 and SWS epochs was 88.83%. This study shows that the proposed method performed well in all the four stages, and can effectively limit error propagation at the same time. It could, therefore, be an efficient method for automatic sleep scoring. Additionally, since it requires only a small volume of data it could be suited to pervasive applications.

Keywords: Sleep, Electroencephalography, Artificial Neural Network, Decision Tree, Sleep stage, Automatic sleep scoring

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1749


[1] A.Rechtschaffen, A.Kales (Eds.), "A Manual of Standardized Terminology, Techniques, and Scoring System for Sleep Stages of Human Sleep," Brain Information Service/Brain Research Institute, UCLA, LosAngeles, 1968.
[2] H. Schulz. "Rethinking sleep analysis: Comment on the AASM Manual for the Scoring of Sleep and Associated Events," Journal of Clinical Sleep Medicine, vol. 4, pp. 99-103, 2008.
[3] Irena Koprinska, Gert Pfurtscheller and Doris Flotzinger, "Sleep classification in infants by decision tree-based neural networks," Artificial Intelligence in Medicine, vol. 8, pp. 387-401, 1996.
[4] Pedro Piñero, Pavel Garcia, Leticia Arco, Alfredo Álvarez, M. Matilde García and Rolando Bonal, "Sleep stage classification using fuzzy sets and machine learning techniques," Neurocomputing, vol. 58-60, pp. 1137-1143, 2004.
[5] Arthur Flexer, Georg Gruber and Georg Dorffner, "A reliable probabilistic sleep stager based on a single EEG signal," Artificial Intelligence in Medicine, vol. 33, pp. 199-207, 2005.
[6] Hilbert R and Naitoh P, "EOG and delta rhythmicity in human sleep EEG," Psychophysiology, vol. 9, pp. 533-538, 1972.
[7] Dyson RJ, Thorton C and Dore CJ, "EOG electrode positions outside the hairline to monitor sleep in man," Sleep, vol. 7, pp. 180-188, 1984.
[8] Lapinlampi A-M and Himanen S-L, "Sleep staging with frontopolar EEG derivation," Sleep and Hypnosis, vol. 6, pp. 48-53, 2004.
[9] Werth E and Borbely AA, "Recording the sleep EEG with periorbital skin electrodes," Electroencephalography and Clinical Neurophysiology, vol. 94, pp. 406-413, 1995.
[10] Adnane Mourad and Jiang Zhongwei, "Automatic sleep-wake stages classifier based on ECG," 2009 ICROS-SICE International Joint Conference, pp. 493-498, 2009.
[11] Mendez M.O., Matteucci M., Cerutti S. and Aletti F., Bianchi A.M., "Sleep staging classification based on HRV: Time-variant analysis," The 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 9-12, 2009.
[12] Jussi Virkkala, Joel Hasan, Alpo Värri, Sari-Leena Himanen and Kiti Müller, "Automatic sleep stage classification using two-channel electrooculography," Journal of Neuroscience Methods, vol. 166, pp. 109-115, 2007.
[13] H.W. Agnew, W.B. Webb and R.L. Williams, "Sleep patterns in late middle age males: an EEG study," Electroencephalography and Clinical Neurophysiology, vol. 23, pp. 168-171, 1967.
[14] I. Feinberg, R.L. Koresko and N. Heller, "EEG sleep patterns as a function of normal and pathological aging in man," Journal of Psychiatric Research, vol. 5, pp. 107-144, 1967.
[15] Tian J.Y. and Liu J.Q., "Automated sleep staging by a hybrid system comprising neural network and fuzzy rule-based reasoning," The 27th Annual International Conference IEEE Engineering in Medicine and Biology Society, pp. 4115-4118, 2005.
[16] Salih G├╝nes, Kemal Polat and ┼×ebnem Yosunkaya, "Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting," Expert Systems with Applications, vol. 37, pp. 7922-7928, 2010.
[17] Han G. Jo, Jin Y. Park, Chung K. Lee and Suk K. An, Sun K. Yoo, "Genetic fuzzy classifier for sleep stage identification," Computers in Biology and Medicine, vol. 40, pp. 629-634, 2010.
[18] Claude Robert, Christian Guilpin and Aymé Limoge, "Review of neural network applications in sleep research," Journal of Neuroscience Methods, vol. 79, pp. 187-193, 1998.
[19] Wen Zhao, Jingzhi Yan, Bin Hu, Haoyu Ma, Li Liu, "Advanced measure selection in automatic NREM discrimination based on EEG," The 5th International Conference on Pervasive Computing and Applications, pp. 26-31, 2010.
[20] Jack R. Smith and Ismet Karacan, "EEG Sleep Stage Scoring By An Automatic Hybrid System," Electroencephalography and Clinical Neurophysiology, vol. 31, pp. 231-237, 1971.
[21] Hae-Jeong Park, Jung-Su Oh, Do-Un Jeong and Kwang-Suk Park, "Automated Sleep Stage Scoring Using Hybrid Rule- and Case-Based Reasoning," Computers and Biomedical Research, vol. 33, pp. 330-349, 2000.