Main Article Content
Abstract
The sleep stages determination is important for the identification and diagnosis of different diseases. An efficient algorithm of wavelet decomposition is used for feature extraction of single channel EEG. The Chi-Square method is applied for the selection of the best attributes from the extracted features. The classification of different staged techniques is applied with the help AdaBoost.M1 algorithm. The accuracy of 89.82% achieved in the six stage classification. The weighted sensitivity of all stages is 89.8% and kappa coefficient of 77.93% is obtained in the six stage classification.
Keywords
EEG
Sleep stages
Machine learning
Accuracy
Article Details
License
Authors retain the copyright without restrictions for their published content in this journal. IJSRTM is a SHERPA ROMEO Journal.
Publishing License
This is an open-access article distributed under the terms of 
How to Cite
Roy, V., Prakash, A., & Shukla, S. (2017). WAVELET FEATURES BASED SLEEP STAGES DETECTION USING SINGLE CHANNEL EEG. International Journal of Students’ Research in Technology & Management, 5(4), 99–102. https://doi.org/10.18510/ijsrtm.2017.5414
References
-
[1] A. Rechtschaffen and A. Kales. Manual of Standardized Terminology, Techniques and Scoring Systems for Sleep Stages of Human Subjects. U. G. P. Office, Washington DC Public Health Service. 1968.
[2] A.L.C.C. Iber, S. Ancoli-Israel and S.F. Quan. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specification. American Academy of Sleep Medicine. Westchester. USA. 2005.
[3] P. Anderer, G. Gruber, S. Parapatics, M. Woertz, T. Miazhynskaia, G. Kl¨osch, B. Saletu, J. Zeitlhofer, M. J. Barbanoj, and H. Danker-Hopfe. An E-health Solution for Automatic Sleep Classification According to Rechtschaffen and Kales: Validation Study of the Somnolyzer 24 × 7 Utilizing the Siesta Database. Neuropsychobiology. 2015, volume 51: (115–133).
[4] F. Chapotot and G. Becq. Automated Sleep–Wake Staging Combining Robust Feature Extraction, Artificial Neural Network Classification, and Flexible Decision Rules. Adaptive Control Signal Process. 2010, volume 24: (409–423).
[5] A. R. Hassan, S. K. Bashar, and M. I. H. Bhuiyan. On the Classification of Sleep States by Means of Statistical and Spectral Features from Single Channel Electroencephalogram. International Conference of Advance Computing Communication Informatics. 2015, (2238–2243).
[6] L. Fraiwan, K. Lweesy, N. Khasawneh, H. Wenz, and H. Dickhaus. Automated Sleep Stage Identification System Based on Time-Frequency Analysis of a Single EEG Channel and Random Forest Classifier. Computer Methods Programs Biomedicine. 2012, volume 108, issue 1: (10–19).
[7] B. Kemp, A. H. Zwinderman, B. Tuk, H. A. C. Kamphuisen and J. J. L. Oberyé. Analysis of a Sleep-Dependent Neuronal Feedback Loop: The Slow-Wave Microcontinuity of the EEG. IEEE Transaction in Biomedical Engineering. 2000, volume 47, issue 9: (1185–1194).
[8] B. Kemp. The Sleep-EDF Database [Onlne]. Available: http://www.physionet.org/physiobank/database/sle- ep- edf/
[9] A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.K. Peng, and H. E. Stanley. PhysioBank, Physiotoolkit, and Physionet: Components of a New Research Resource for Complex Physiologic Signals. Circulation. 2000, volume 101: (215–220).
[10] Y. Freund and R.E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer System and Science. 1997, volume 55, issue 1: (119-139).
References
[1] A. Rechtschaffen and A. Kales. Manual of Standardized Terminology, Techniques and Scoring Systems for Sleep Stages of Human Subjects. U. G. P. Office, Washington DC Public Health Service. 1968.
[2] A.L.C.C. Iber, S. Ancoli-Israel and S.F. Quan. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specification. American Academy of Sleep Medicine. Westchester. USA. 2005.
[3] P. Anderer, G. Gruber, S. Parapatics, M. Woertz, T. Miazhynskaia, G. Kl¨osch, B. Saletu, J. Zeitlhofer, M. J. Barbanoj, and H. Danker-Hopfe. An E-health Solution for Automatic Sleep Classification According to Rechtschaffen and Kales: Validation Study of the Somnolyzer 24 × 7 Utilizing the Siesta Database. Neuropsychobiology. 2015, volume 51: (115–133).
[4] F. Chapotot and G. Becq. Automated Sleep–Wake Staging Combining Robust Feature Extraction, Artificial Neural Network Classification, and Flexible Decision Rules. Adaptive Control Signal Process. 2010, volume 24: (409–423).
[5] A. R. Hassan, S. K. Bashar, and M. I. H. Bhuiyan. On the Classification of Sleep States by Means of Statistical and Spectral Features from Single Channel Electroencephalogram. International Conference of Advance Computing Communication Informatics. 2015, (2238–2243).
[6] L. Fraiwan, K. Lweesy, N. Khasawneh, H. Wenz, and H. Dickhaus. Automated Sleep Stage Identification System Based on Time-Frequency Analysis of a Single EEG Channel and Random Forest Classifier. Computer Methods Programs Biomedicine. 2012, volume 108, issue 1: (10–19).
[7] B. Kemp, A. H. Zwinderman, B. Tuk, H. A. C. Kamphuisen and J. J. L. Oberyé. Analysis of a Sleep-Dependent Neuronal Feedback Loop: The Slow-Wave Microcontinuity of the EEG. IEEE Transaction in Biomedical Engineering. 2000, volume 47, issue 9: (1185–1194).
[8] B. Kemp. The Sleep-EDF Database [Onlne]. Available: http://www.physionet.org/physiobank/database/sle- ep- edf/
[9] A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.K. Peng, and H. E. Stanley. PhysioBank, Physiotoolkit, and Physionet: Components of a New Research Resource for Complex Physiologic Signals. Circulation. 2000, volume 101: (215–220).
[10] Y. Freund and R.E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer System and Science. 1997, volume 55, issue 1: (119-139).
[2] A.L.C.C. Iber, S. Ancoli-Israel and S.F. Quan. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specification. American Academy of Sleep Medicine. Westchester. USA. 2005.
[3] P. Anderer, G. Gruber, S. Parapatics, M. Woertz, T. Miazhynskaia, G. Kl¨osch, B. Saletu, J. Zeitlhofer, M. J. Barbanoj, and H. Danker-Hopfe. An E-health Solution for Automatic Sleep Classification According to Rechtschaffen and Kales: Validation Study of the Somnolyzer 24 × 7 Utilizing the Siesta Database. Neuropsychobiology. 2015, volume 51: (115–133).
[4] F. Chapotot and G. Becq. Automated Sleep–Wake Staging Combining Robust Feature Extraction, Artificial Neural Network Classification, and Flexible Decision Rules. Adaptive Control Signal Process. 2010, volume 24: (409–423).
[5] A. R. Hassan, S. K. Bashar, and M. I. H. Bhuiyan. On the Classification of Sleep States by Means of Statistical and Spectral Features from Single Channel Electroencephalogram. International Conference of Advance Computing Communication Informatics. 2015, (2238–2243).
[6] L. Fraiwan, K. Lweesy, N. Khasawneh, H. Wenz, and H. Dickhaus. Automated Sleep Stage Identification System Based on Time-Frequency Analysis of a Single EEG Channel and Random Forest Classifier. Computer Methods Programs Biomedicine. 2012, volume 108, issue 1: (10–19).
[7] B. Kemp, A. H. Zwinderman, B. Tuk, H. A. C. Kamphuisen and J. J. L. Oberyé. Analysis of a Sleep-Dependent Neuronal Feedback Loop: The Slow-Wave Microcontinuity of the EEG. IEEE Transaction in Biomedical Engineering. 2000, volume 47, issue 9: (1185–1194).
[8] B. Kemp. The Sleep-EDF Database [Onlne]. Available: http://www.physionet.org/physiobank/database/sle- ep- edf/
[9] A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.K. Peng, and H. E. Stanley. PhysioBank, Physiotoolkit, and Physionet: Components of a New Research Resource for Complex Physiologic Signals. Circulation. 2000, volume 101: (215–220).
[10] Y. Freund and R.E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer System and Science. 1997, volume 55, issue 1: (119-139).