NEUROPHYSIOLOGIC AND STATISTICAL ANALYSIS OF FAILURES IN AUTOMATIC SLEEP STAGE CLASSIFICATION

Teresa Sousa, Dulce Oliveira, Sirvan Khalighi, Gabriel Pires, Urbano Nunes

2012

Abstract

This paper analyses some of the challenges in automatic multiclass sleep stage classification. Six electroencephalographic (EEG) and two electrooculographic (EOG) channels were used in this study. A set of significant features are selected by a minimum-redundancy maximum-relevance (mRMR) criterion and then classified using support vector machine (SVM). The system is tested on 14 subjects suspected of having sleep apnea. The automatic sleep staging showed a 77.70% (±15.8) sensitivity and 95.49% (±2.68) specificity. From the analysis comparing EEG records with visual and automatic classification, we found that the main cause of failures are the similarities between adjacent phases of sleep, in particular in discriminating N1 and N2. Based on the variation of the values of the features it is possible to implement some thresholds and to apply some heuristic rules to improve the performance.

References

  1. AASM, 1999. Sleep-Related Breathing Disorders in Adults: Recommendations for Syndrome Definition and Measurement Techniques in Clinical Research. The Report of an American Academy of Sleep Medicine Task Force. In Sleep, 22(5).
  2. Bonnet, M., Carley, D., Carskadon, et al., 1992. EEG Arousals: Scoring Rules and Examples. Sleep disorders atlas task force of American Sleep Disorders Association and Sleep Research Society. In Sleep, 15(2):173-184.
  3. Burges, J., 1998. A Tutorial on Support Vector Machines for Pattern Recognition. In Data Mining and Knowledge Discovery, 2.
  4. Carskadon, M., 1986. Guidelines for the Multiple Sleep Latency Test (MSLT): A Standard Measure of Sleepiness. In Sleep, 9(4):519-524.
  5. Chang, C., Lin, CJ., 2011. LIBSVM: a library for support vector machines. In ACM Transactions on Intelligent Systems and Technology, 1-39. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm.
  6. Doroshenkov, L., Konyshev, V., Selishchev, S., 2007. Classification of human sleep stages based on EEG processing using hidden markov models. In Biomedical Engineering, 41(1):25-28.
  7. Helland, V., Gapelyuk, A., Suhrbier, A., et al., 2010. Investigation of an Automatic Sleep Stage Classification by Means of Multiscorer Hypnogram. In Methods Inf. Med.,4:1-6.
  8. Iber, C., Ancoli-Israel, S., Chesson, A., Quan, S., 2007. The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications. In 1th: Westchester, Illinois: American Academy of Sleep Medicine.
  9. Kim, J., Lee, J., Robinson, P., Jeong, D., 2009. Markov Analysis of Sleep Dynamics. In Physical Review Letters, 102:178104-1-4.
  10. Khalighi, S., Sousa, T., Oliveira, D., Pires, G., Nunes, U., 2011. Efficient Feature Selection for Sleep Staging Based on Maximal Overlap Discrete Wavelet Transform and SVM. In 33rd International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC11), USA.
  11. Nicolaou, N., Georgiou, J., 2011. The use of permutation entropy to characterize sleep electroencephalograms. In Clinical EEG and Neuroscience, 42(1):24-28.
  12. Peng, H., Long, F., Ding, C., 2005. Feature selection based on mutual information: criteria of maxdependency, max-relevance, and min-Redundancy. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8):1226-1238.
  13. Penzel, T., Kesper, K., Gross, V., Becker, H., Vogelmeier, C., 2003. Problems in Automatic Sleep Scoring Applied to Sleep Apnea. In 25 th Annual International Conference of the IEEE EMBS, Sept. 17-21, 358-361.
  14. Torkkola, K., 2003. Feature Extraction by Non-Parametric Mutual Information Maximization. In Journal of Machine Learning Research, 3:1415-1438.
  15. Tsara, V., Amfilochiou, A., Papagrigorakis, et al., 2009. Definition and classification of sleep related breathing disorders in adults: Different types and indications for sleep studies (Part 1). In Hippokratia, 13(3):187-191.
  16. Young, T., Palta, M., Dempsey, Y., Skatrud, J., Weber, S., Badr, S., 1993, The occurrence of sleep disorder breathing among middle aged adults. In The New England Journal of Medicine, 328(17):1230-1235.
  17. Zoubek, L., Charbonnier, S., Lesecq, et al., 2007. Feature selection for sleep/wake stages classification using data driven methods. In Biomedical Signal Processing and Control, 2(3):171-179.
Download


Paper Citation


in Harvard Style

Sousa T., Oliveira D., Khalighi S., Pires G. and Nunes U. (2012). NEUROPHYSIOLOGIC AND STATISTICAL ANALYSIS OF FAILURES IN AUTOMATIC SLEEP STAGE CLASSIFICATION . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012) ISBN 978-989-8425-89-8, pages 423-428. DOI: 10.5220/0003792304230428


in Bibtex Style

@conference{biosignals12,
author={Teresa Sousa and Dulce Oliveira and Sirvan Khalighi and Gabriel Pires and Urbano Nunes},
title={NEUROPHYSIOLOGIC AND STATISTICAL ANALYSIS OF FAILURES IN AUTOMATIC SLEEP STAGE CLASSIFICATION},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)},
year={2012},
pages={423-428},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003792304230428},
isbn={978-989-8425-89-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)
TI - NEUROPHYSIOLOGIC AND STATISTICAL ANALYSIS OF FAILURES IN AUTOMATIC SLEEP STAGE CLASSIFICATION
SN - 978-989-8425-89-8
AU - Sousa T.
AU - Oliveira D.
AU - Khalighi S.
AU - Pires G.
AU - Nunes U.
PY - 2012
SP - 423
EP - 428
DO - 10.5220/0003792304230428