Automatic determination of sleep stage through bio-neurological signals contaminated with artifacts by a conditional probability of the knowledge base

Artificial Life and Robotics

Volumn 12, Issue 1-2, 2008, Pages 270-275

  Wang, Bei ^a,c   Wang, Xingyu ^a   Zou, Junzhong ^a   Kawana, Fusae ^b   Nakamura, Masatoshi ^c  

^a EAST CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b TORANOMON HOSPITAL   (Japan)

^c SAGA UNIVERSITY   (Japan)

Author keywords

Artifact; Automatic sleep stage determination; Bioneurological signals; Conditional probability; Knowledge base of expert visual inspection

Indexed keywords

ARTIFACT DETECTION; AUTOMATIC DETERMINATION; CHARACTERISTIC PARAMETER; CLINICAL APPLICATION; CONDITIONAL PROBABILITIES; CONDITIONAL PROBABILITY; KNOWLEDGE BASE; SLEEP STAGE; VISUAL INSPECTION;

INSPECTION; KNOWLEDGE BASED SYSTEMS; PROBABILITY; SIGNAL DETECTION; SLEEP RESEARCH; VISUAL COMMUNICATION;

PROBABILITY DENSITY FUNCTION;

EID: 73449131554     PISSN: 14335298     EISSN: 16147456     Source Type: Journal    
DOI: 10.1007/s10015-007-0480-6     Document Type: Article

References (13)

1. J.R. Smith I. Karakan M. Yang 1978 Automated analysis of the human sleep EEG Waking Sleeping 2 75 82
2. N. Schaltenbrand R. Lengelle M. Toussaint, et al. 1996 Sleep stage scoring using the neural network model: comparison between visual and automatic analysis in normal subjects and patients Sleep 19 26 35
3. P. Anderer G. Gruber S. Parapatics, et al. 2005 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 51 115 133 10.1159/000085205 (Pubitemid 40663275)
4. M. Nakamura S. Goto T. Sugi 2000 Artificial realization of human on-off decision-making based on the conditional probability of a database Artif Life Robotics 4 89 95 10.1007/BF02480862
5. M. Nakamura T. Sugi 2002 Multi-valued decision making for transitional stochastic event: determination of sleep stages through EEG record Trans Control Autom Syst Eng 4 239 243
6. Nakamura M, Wang B, Sugi T, et al. (2006) Automatic decision making based on conditional probability of specific parameters in expert knowledge base: sleep-stage determination. In: Proc 2006 Int Symp Humanized Systems, Oct. 16-19, Fusan, Korea 64-69
7. P. Anderer S. Roberts A. Schlögl, et al. 1999 Artifact processing in computerized analysis of sleep EEG: a review Neuropsychobiology 40 150 157 10.1159/000026613 (Pubitemid 29435739)
8. D.P. Brunner R.C. Vasko C.S. Detka, et al. 1996 Muscle artifacts in the sleep EEG: automated detection and effect on all-night EEG power spectra J Sleep Res 5 155 164 10.1046/j.1365-2869.1996.00009.x
9. H.H. Jasper 1958 Ten-twenty electrode system of the international federation Electroenceph Clin Neurophysiol 10 371 375
10. Rechtschaffen A, Kales A (1968) A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Public Health Service, US Goverment Printing Office, Washington, DC
11. S.L. Himanen J. Hasan 2000 Limitations of Rechtschaffen and Kales Sleep Med Rev 4 149 167 10.1053/smrv.1999.0086 (Pubitemid 30249492)
12. A. Kaplan J. Röschke B. Darkhovsky, et al. 2001 Macrostructural EEG characterization based on nonparametric change point segmentation: application to sleep analysis J Neurosci Methods 106 81 90 10.1016/S0165-0270(01)00331-4 (Pubitemid 32193017)
13. S.P. Duntley A.H. Kim D.L. Silbergeld, et al. 2001 Characterization of the mu rhythm during rapid eye movement sleep Clin Neurophysiol 112 528 531 10.1016/S1388-2457(00)00559-9 (Pubitemid 32166934)