A Hybrid Classification Method using Artificial Neural Network Based Decision Tree for Automatic Sleep Scoring
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.
[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
M├╝ller, "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.
[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
M├╝ller, "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.
@article{"International Journal of Business, Human and Social Sciences:64932", author = "Haoyu Ma and Bin Hu and Mike Jackson and Jingzhi Yan and Wen Zhao", title = "A Hybrid Classification Method using Artificial Neural Network Based Decision Tree for Automatic Sleep Scoring", abstract = "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, Sleep stage, Automatic sleep scoring,Electroencephalography, Decision tree, Artificial neural network", volume = "5", number = "7", pages = "922-6", }
