Research Article | Open Access
Yanjun Zhang, Xiangmin Zhang, Wenhui Liu, Yuxi Luo, Enjia Yu, Keju Zou, Xiaoliang Liu, "Automatic Sleep Staging using Multi-dimensional Feature Extraction and Multi-kernel Fuzzy Support Vector Machine", Journal of Healthcare Engineering, vol. 5, Article ID 410705, 16 pages, 2014. https://doi.org/10.1260/2040-2218.104.22.1685
Automatic Sleep Staging using Multi-dimensional Feature Extraction and Multi-kernel Fuzzy Support Vector Machine
This paper employed the clinical Polysomnographic (PSG) data, mainly including all-night Electroencephalogram (EEG), Electrooculogram (EOG) and Electromyogram (EMG) signals of subjects, and adopted the American Academy of Sleep Medicine (AASM) clinical staging manual as standards to realize automatic sleep staging. Authors extracted eighteen different features of EEG, EOG and EMG in time domains and frequency domains to construct the vectors according to the existing literatures as well as clinical experience. By adopting sleep samples self-learning, the linear combination of weights and parameters of multiple kernels of the fuzzy support vector machine (FSVM) were learned and the multi-kernel FSVM (MK-FSVM) was constructed. The overall agreement between the experts' scores and the results presented was 82.53%. Compared with previous results, the accuracy of N1 was improved to some extent while the accuracies of other stages were approximate, which well reflected the sleep structure. The staging algorithm proposed in this paper is transparent, and worth further investigation.
- M. M. Ohayon, “Epidemiology of insomnia: what we know and what we still need to learn,” Sleep Med Rev, vol. 6, pp. 97–111, 2002.
- M. W. Mahowald and C. H. Schenck, “Insights from studying human sleep disorders,” Nature, vol. 437, pp. 1279–1285, 2005.
- A. Rechtschaffen and A. Kales, A Manual of Standardized Terminology, Techniques and Scoring System For Sleep Stages of Human SubjectsBrain InForm Service/Brain Res. Inst., Univ, CaliFornia, Los Angeles, 1968.
- C. Iber, S. Ancoli-Israel, A. L. Chesson, and S. F. Quan, The AASM manual for the Scoring of Sleep and Associated Events, American Academy of Sleep Medicine, 2007.
- D. Moser, P. Anderer, G. Gruber et al., “Sleep classification according to AASM and Rechtschaffen & Kales: effects on sleep scoring parameters,” Sleep, vol. 32, no. 2, pp. 139–149, 2009.
- L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classication and regression trees, Chapman & Hall, Boca Raton, 1993.
- M. Kubat, G. Pfurtscheller, and D. Flotzinger, “AI-based approach to automatic sleep classification,” Biol Cybern, vol. 70, pp. 443–448, 1994.
- H. J. Park, K. S. Park, and U. U. Jeong, “Hybrid neural-network and rule-based expert system for automatic sleep stage scoring. in,” in Proc. 22th Annual EMBS Int. Conf, pp. 1316–1319, 2000.
- F. Ebrahimi, S. K. Setarehdan, Ayala-Moyeda Jose, and H. Nazeran, “Automatic sleep staging using empirical mode decomposition, discrete wavelet transform, time-domain, and nonlinear dynamics features of heart rate variability signals,” Computer Methods and Programs in Biomedicine, vol. 112, no. 1, pp. 47–57, 2013.
- E. Estrada and H. Nazeran, “EEG and HRV signal features for automatic sleep staging and apnea detection,” in 20th International Conference on Electronics, Communications and Computers, pp. 142–147, Cholula, Mexico, 2010.
- Migliorini Matteo, M. Bianchi Anna, and Nistico Domenico, “Automatic sleep staging based on ballistocardiographic signals recorded through bed sensors,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3273–3276, IEEE Engineering in Medicine and Biology Society, 2010.
- N. Schaltenbrand, R. Lengelle, M. Toussaint et al., “Sleep stages scoring using the neural network model: Comparison between visual and automatic analysis in normal subjects and patients,” Sleep, vol. 19, pp. 26–35, 1996.
- R. Agarwal and J. Gotman, “Computer-assisted sleep staging,” IEEE Trans Biomed Eng, vol. 48, pp. 1412–1423, 2001.
- T. Penzel and R. Conradt, “Computer based sleep recording and analysis,” Sleep Med Rev, vol. 4, pp. 131–148, 2000.
- J. Malmivuo and R. Plonsey, Principles and Applications of Bioelectric and Biomagnetic Fields. Bioelectromagnetism, Press: Oxford University, New York, 1995.
- H. Kuwahara, H. Higashi, Y. Mizuki, S. M. M. Tanaka, and K. Inanaga, “Automatic real-time analysis of human sleep stages by an interval histogram method,” Electroencephalogr Clin Neurophysiol, vol. 70, pp. 220–229, 1988.
- F. Duman, A. Erdamar, O. Erogul, Z. Telatar, and S. Yetkin, “Efficient sleep spindle detection algorithm with decision tree,” Expert Syst Appl, vol. 36, pp. 9980–9985, 2009.
- S. F. Liang, C. E. Kuo, H. u. YH, and Y. S. Cheng, “A rule-based automatic sleep staging method,” J Neurosci Methods, vol. 205, pp. 169–176, 2012.
- S. T. Pan, C. E. Kuo, J. H. Zeng, and S. F. Liang, “A transition-constrained discrete hidden Markov model for automatic sleep staging,” BioMedical Engineering Online, vol. 11, no. 52, 2012.
- C. Berthomier, J. Prado, and O. Benoit, “Automatic sleep EEG analysis using filter banks,” Biomed SciInstrum, vol. 35, pp. 241–246, 1999.
- G. H. Jansen and B. M. Dawant, “Knowledge-based approach to sleep EEG analysis - a feasibility study,” IEEE Trans Biomed Eng, vol. 36, pp. 510–518, 1989.
- C. Robert, C. Guilpin, and A. Limoge, “Review of neural network application in sleep research,” Journal of Neuroscience Methods, vol. 79, pp. 187–193, 1998.
- N. Schaltenbrand, R. Lengelle, M. Toussaint et al., “Sleep stage scoring using the neural network model: Comparison between visual and automatic analysis in normal subjects and patients,” Sleep, vol. 19, pp. 26–35, 1996.
- M. E. Tagluk, N. Sezgin, and M. Akin, “Estimation of sleep stages by an artificial neural network employing EEG, EMG and EOG,” J Med Syst, vol. 34, pp. 717–725, 2010.
- Y. L. Hsu, Y. T. Yang, and J. S. Wang, “Automatic sleep stage recurrent neural classifier using energy features of EEG signals,” Neurocomputing, vol. 104, pp. 105–114, 2013.
- X. Y. Fu, B. Wang, and X. Y. Wang, “Feature extraction and classification for short-time Sleep,” Journal of East China University of Science and Technology (Natural Science Edition), vol. 37, no. 1, pp. 84–89, 2011.
- C. C. Sady, U. S. Freitas, A. Portmann, J. F. Muir, C. Letellier, and L. A. Aguirre, “Automatic sleep staging from ventilator signals in non-invasive ventilation,” Computers in Biology and Medicine, vol. 43, no. 7, pp. 833–839, 2013.
- S. Gudmundsson, T. P. Runarsson, and S. Sigurdsson, “Automatic sleep staging using support vector machines with posterior probability estimates,” in International Conference on Computational Intelligence for Modelling, Control and Automation/International Conference on Intelligent Agents Web Technologies and International Commerce, Vienna, Austria, 2005.
- A. Flexer, G. Dorffner, P. Sykacek, and I. Rezek, “An automatic, continuous and probabilistic sleep stager based on a hidden Markov model,” Appl Artif Intell, vol. 16, pp. 199–207, 2002.
- A. Flexer, G. Gruber, and G. Dorffner, “A reliable probabilistic sleep stager based on a single EEG signal,” Artif Intell Med, vol. 33, pp. 199–207, 2005.
- L. G. Doroshenkov, V. A. Konyshev, and S. V. Selishchev, “Classification of human sleep stages based on EEG processing using hidden Markov models,” Biomed Eng, vol. 41, pp. 25–28, 2007.
- E. Malaekah and D. Cvetkovic, “Automatic detection of the wake and stage 1 sleep stages using the EEG sub-epoch approach,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6401–6404, IEEE Engineering in Medicine and Biology Society, 2013.
- S. F. Liang, C. E. Kuo, and Y. H. Hu, “Automatic stage scoring of single-channel sleep EEG by using multiscale entropy and autoregressive models,” IEEE Transactions on Instrumentation, vol. 61, no. 6, pp. 1649–1657, 2012.
- F. Ebrahimi, SK. Setarehdan, and H. Nazeran, “DFA and DWT based features of HRV signal for automatic sleep staging,” in 19TH Iranian Conference of Biomedical Engineering (ICBME), pp. 262–265, Tehran, Iran, 2012.
- S. Charbonnier, L. Zoubek, and S. Lesecq, “Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging,” Computers in Biology and Medicine, vol. 41, no. 6, pp. 380–389, 2011.
- G. Becq, S. Charbonnier, F. Chapotot, A. Buguet, L. Bourdon, and P. Baconnier, “Comparison between five classifiers for automatic scoring of human sleep recordings,” in Proceedings of the 1st International Conference on Fuzzy Systems and Knowledge Discovery: Computational Intelligence for the E-Age, L. Wang, S. Halgamuge, and X. Yao, Eds., vol. 1, pp. 616–620, Orchid Country Club, Singapore, 2002.
- C. F. Lin and S. D. Wang, “Fuzzy support vector machine,” IEEE Ransacion on Neural Networks, vol. 13, pp. 464–471, 2002.
- C. F. Lin and S. D. Wang, “Fuzzy support vector machines with automatic membership setting,” Studies in Fuzziness and Soft Computing, vol. 177, pp. 233–254, 2005.
- M. J. Li, M. K. Ng, Y. M. Cheung, and J. Z. Huang, “Agglomerative fuzzy K-means clustering algorithm with selection of number of clusters,” IEEE Trans Knowledge and Data Engineering, vol. 20, pp. 1519–1534, 2008.
- J. F. Gao, Y. Yang, P. Lin, P. Wang, and C. X. Zheng, “Automatic removal of eye-movement and blink artifacts from EEG signals,” Brain Topogr, vol. 23, no. 1, pp. 105–114, 2010.
- X. U. GL and Y. WEI, “Learning algorithm based on fuzzy support vector machine of multi-core functions,” Journal of Chongqing Normal University (Natural Science), vol. 29, pp. 50–53, 2012.
- H. C. Huang, Y. Y. Chuang, and C. S. Chen, “Multiple kernel fuzzy clustering,” IEEE Transactions on Fuzzy Systems, vol. 20, no. 1, pp. 120–134, 2012.
- J. Cohen, “A coefficient of agreement for nominal scales,” Educ Psychol Meas, vol. 20, pp. 37–46, 1960.
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