Automatic sleep stage recurrent neural classifier using energy features of EEG signals

Neurocomputing

Volumn 104, Issue , 2013, Pages 105-114

  Hsu, Yu Liang ^a   Yang, Ya Ting ^a   Wang, Jeen Shing ^a   Hsu, Chung Yao ^b  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

^b KAOHSIUNG MEDICAL UNIVERSITY HOSPITAL   (Taiwan)

Author keywords

EEG signals; Energy features; Recurrent neural classifiers; Sleep stage analysis

Indexed keywords

CLASSIFICATION PERFORMANCE; CLASSIFICATION RATES; EEG SIGNALS; ENERGY FEATURE; NEURAL CLASSIFIERS; PHYSIOBANK; PROBABILISTIC NEURAL NETWORKS; SLEEP RECORDINGS; SLEEP STAGE;

FEEDFORWARD NEURAL NETWORKS;

SLEEP RESEARCH;

ACCURACY; ADULT; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFIER; CONTROLLED STUDY; ELECTROENCEPHALOGRAM; ENERGY; FACTUAL DATABASE; FEMALE; HUMAN; HUMAN EXPERIMENT; MALE; NONREM SLEEP; NORMAL HUMAN; POSITIVE FEEDBACK; PRIORITY JOURNAL; RECURRENT NEURAL CLASSIFIER; REM SLEEP; SIGNAL PROCESSING; SLEEP STAGE; SLOW WAVE SLEEP; VALIDATION PROCESS; WAKEFULNESS; WAVEFORM;

EID: 84873736761     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.11.003     Document Type: Article

References (32)

1. Agarwal R., Gotman J. Computer-assisted sleep staging,. IEEE Trans. Biomed. Eng. 2001, 48(12):1412-1423.
2. Aydin S., Saraolu H.M., Kara S. Singular spectrum analysis of sleep EEG in insomnia. J. Med. Syst. 2011, 35(4):457-461.
3. Berthomier C., Drouot X., Herman-Stoïca M., Berthomier P., Prado J., Bpkar-Thire D., Benoit O., Mattout J., d'Ortho M. Automatic analysis of single-channel sleep EEG: validation in healthy individuals. Sleep 2007, 30(11):1587-1595.
4. Correa A.G., Laciar E., Patiño H.D., Valentinuzzi M.E. An automatic sleep-stage classifier using electroencephalographic signals. Int. J. Med. Sci. 2008, 1(1):13-21.
5. Charbonnier S., Zoubek L., Lesecq S., Chapotot F. Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging. Comput. Biol. Med. 2011, 41(6):380-389.
6. Funahashi K.I., Nakamura Y. Approximation of dynamical systems by continuous time recurrent neural networks,. Neural Networks 1993, 6(6):801-806.
7. Feldkamp L.A., Puskorius G.V. A signal processing framework based on dynamic neural networks with application to problems in adaptation, filtering, and classification. Proc. IEEE 1998, 86(11):2259-2277.
8. Flexer A., Gruber G., Dorffner G. A reliable probabilistic sleep stager based on a single EEG signal,. Artif. Intell. Med. 2005, 33(3):199-207.
9. Fraiwan L.A., Khaswaneh N.Y., Lweesy K.Y. Automatic sleep stage scoring with wavelet packets based on single EEG recording. World Acad. Sci. Eng. Technol. 2009, 54:485-488.
10. Gaillard J.M., Tissot R. Principles of automatic analysis of sleep records with a hybrid system,. Comput. Biomed. Res. 1973, 6:1-13.
11. Güler N.F., Übeyli E.D., Güler. Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst. Appl. 2005, 29(3):506-514.
12. P.V. Hese, W. Philips, J.D. Koninck, R.V. de Walle, I. Lemahieu, Automatic detection of sleep stages using EEG, in: Proceedings of the 23rd Annual International Conference of the IEEE, vol. 2, 2001, pp. 1944-1947.
13. Hanaoka M., Kobayashi M., Yamazaki H. Automatic sleep stage scoring based on waveform recognition method and decision-tree learning. Syst. Comput. Jpn. 2002, 33(11):1-13.
14. Hüsken M., Stagge P. Recurrent neural networks for time series classification. Neurocomputing 2003, 50:223-235.
15. Jin L., Nikiforuk P.N., Gupta M.M. Approximation of discrete-time state-space trajectories using dynamic recurrent neural networks. IEEE Trans. Autom. Control 1995, 40(7):1266-1270.
16. Jo H.G., Park J.Y., Lee C.K., An S.K., Yoo S.K. Genetic fuzzy classifier for sleep stage identification. Comput. Biol. Med. 2010, 40(7):629-634.
17. B. Kemp, The Sleep-EDF Database, , 2006. http://www.physionet.org/physiobank/database/sleep-edf/.
18. Norman R.G., Pal I., Stewart C., Walsleben J.A., Rapoport D.M. Interobserver agreement among sleep scorers from different centers in a large dataset. Sleep 2000, 23(7):901-908.
19. E. Oropesa, H.L. Cycon, M. Jobert, Sleep Stage Classification Using Wavelet Transform and Neural Networks, International Computer Science Institute, 1999.
20. Principe J.C., Gala S.K., Chang T.G. Sleep staging automaton based on the theory of evidence. IEEE Trans. Biomed. Eng. 1989, 36(5):503-509.
21. Petrosian A.A., Prokhorov D.V., Lajara-Nanson W., Schiffer R.B. Recurrent neural network-based approach for early recognition of Alzheimer's disease in EEG. Clin. Neurophysiol. 2001, 112(8):1378-1387.
22. Piñero P., Garcia P., Arco L., Álvarez A., García M.M., Bonal R. Sleep stage classification using fuzzy sets and machine learning techniques. Neurocomputing 2004, 58-60:1137-1143.
23. A. Rechtschaffen, A. Kales, A Manual of Standardized Terminology, Techniques, and Scoring System for Sleep Stages of Human Subjects, UCLA, Brain Research Institute/Brain Information service, Los Angeles, CA, 1968.
24. Stanus E., Lacroix B., Kerkhofs M., Mendlewicz J. Automated sleep scoring: a comparative reliability study of algorithms. Electroencephalogr. Clin. Neurophysiol. 1987, 66:448-456.
25. 1). Sleep 1990, 13(3):279-283.
26. Schaltenbrand N., Lengelle R., Toussaint M., Luthringer R., Carelli G., Jacqmin A., Lainey E., Muzet A., Macher J.P. Sleep stage scoring using the neural network model: comparison between visual and automatic analysis in normal subjects and patients,. Sleep 1996, 19(1):26-35.
27. Schäfer A.M., Zimmermann H.G. Recurrent neural networks are universal approximators. Int. J. Neural Syst. 2007, 17(4):253-263.
28. Sinha R.K. Artificial neural network and wavelet based automated detection of sleep spindles, REM sleep and wake states. J. Med. Syst. 2008, 32(4):291-299.
29. Sinha R.K. EEG power spectrum and neural network based sleep-hypnogram analysis for a model of heat stress. J. Clin. Monitor. Comput. 2008, 22(4):261-268. , 99.
30. Tagluk M.E., Sezgin N., Akin M. Estimation of sleep stage by an artificial neyral network employing EEG, EMG, and EOG. J. Med. Syst. 2010, 34(4):717-725.
31. Wang J.S., Lee C.S.G. Self-adaptive neuro-fuzzy inference systems for classification applications,. IEEE Trans. Fuzzy Syst. 2002, 10(6):790-802.
32. Zoubek L., Charbonnier S., Lesecq S., Buguet A., Chapotot F. Feature selection for sleep/wake stages classification using data driven methods,. Biomed. Signal Process. Control 2007, 2(3):171-179.