A computer method for identifying patterns in electroencephalogram signals

References

• BERGER, H., 1929, Uber das Elektrenkephalogramm des Menschen. Archiv für Psychiatrie und Nervenkrankheiten, 87, 527 —570.
   Google Scholar
• NIEDERMEYER, E., 1993, The normal EEG of the waking adult. In Electroencephalography: basic principles, clinical applications and related fields, third edition, edited by E. Niedermeyer and F. H. Lopes da Silva, (Baltimore: Williams and Wilkins), p. 131.
   Google Scholar
• QUIAN QuutoGA, R., and ScHfamANN, M., 1999, Functions and sources of event-related EEG alpha oscillations studied with the Wavelet Transform. Clinical Neurophysiology, 110, 643 —654.
   Google Scholar
• PETROSIAN, A., 1995, Kolmogorov complexity of finite sequences and recognition of different preictal EEG patterns. Proceedings of the 8th JEFF Symposium on Computer-Based Medical Systems, June 9-10,212 — 217, Lubbock, Texas: IEEE Computer Society Press).
   Google Scholar
• KLATCHKO, A., RAVIV, G., WEBBER, W. R. S., and LESSER, R. P., 1998, Enhancing the detection of seizures with a clustering algorithm. Electroencephalography and Clinical Neurophysiology, 106, 52–63.
   PubMedGoogle Scholar
• BEUN, A. M., VAN EMDE BOAS, W., and DEKKER, E., 1998, Sharp transients in the sleep EEG of healthy adults: a possible pitfall in the diagnostic assessment of seizure disorders. Electroencephalography and Clinical Neurophysiology, 106, 44–51.
   PubMedGoogle Scholar
• SARTOREITO, F., and ERMANI, M., 1999, Automatic detection of epileptiform activity by single-level wavelet analysis. Clinical Neurophysiology, 110, 239–249.
   PubMedGoogle Scholar
• SCHNEIDER-HELMERT, D., and KUMAR, A., 1995, Sleep, its subjective perception, and daytime performance in insom-niacs with a pattern of alpha sleep. Biological Psychiatry, 37, 99–105.
   PubMed Web of Science ®Google Scholar
• RECHTSCHAFFEN, A., and KALEs, A., 1968, A manual standardized terminology, techniques and scoring system for sleep stages of human subjects. Public Health Service, NIH Publication N° 204, US Government Printing Office, Washington, DC.
   Google Scholar
• HIRSHKOWITZ, M., and MOORE, C. A., 1994, Issues in computerized polysomnography. Sleep, 17, 105 — 112.
   Google Scholar
• HASAN, J., 1996, Past and future of computer-assisted sleep analysis and drowsiness assessment. Journal of Clinical Neurophysiology, 13, 295 — 313.
   PubMedGoogle Scholar
• KAPLAN, A., ROSCHKE, J., DARKHOVSKY, B., and FELL, J., 2001, Macrostructural EEG characterization based on nonpara-metric change point segmentation: application to sleep analysis. Journal of Neuroscience Methods, 106, 81–90.
   PubMed Web of Science ®Google Scholar
• BROUGHTON, R. J., 1987, Polysomnography: Principles and applications in sleep and arousal disorders. In Electro-encephalography, second edition, edited by E. Niedermeyer and F. Lopes da Silva, (Baltimore: Urban and Schwarzen-berg).
   Google Scholar
• CARSKADON, M. A., and RECHTSCHAFFEN, A., 1989, Monitor-ing and staging human sleep. In Principles and practice of sleep medicine, edited by M. H. Kryger, T. Roth and W. C. Dement, (Philadelphia: Saunders).
   Google Scholar
• GEVINS, A. S., 1987, Correlation analysis. In Methods of Analysis of Brain Electrical and Magnetic Signals (Handbook of Electro-encephalography and Clinical Neurophysiology, revised series, vol. 1), edited by A. S. Gevins and A. Remond, (Amsterdam: Elsevier).
   Google Scholar
• ARQuts, D. G., and MICHEL, C. J., 1987, A purine-pyrimidine motif verifying an identical presence in almost all gene taxonomic groups. Journal of Theoretical Biology, 128, 457 — 461.
   Google Scholar
• SADASIVAN, P. K., and DUTT, D. N., 1994, Minimization of EOG artefacts from corrupted EEG signals using a neural network approach. Computers in Biology and Medicine, 24, 441 — 449.
   Google Scholar
• SAASTAMOINEN, A., PIETILA, T., VARRI, A., LEHTOKANGAS, M., and SAARINEN, J., 1998, Waveform detection with RBF network — application to automated EEG analysis. Neuro-computing, 20, 1 — 13.
   Google Scholar
• ROBERT, C., GUILPIN, C., and LimoGE, A., 1998, Review of neural network applications in sleep research. Journal of Neuroscience Methods, 79, 187–193.
   PubMedGoogle Scholar
• NUWER, M. R., 1988, Quantitative EEG: I. Techniques and problems of frequency analysis and topographic mapping. Clinical Neurophysiology, 5, 1 —43.
   Google Scholar
• ARQuts, D. G., and MICHEL, C. J., 1990, A model of DNA sequence evolution. Part 1: Statistical features and classifi-cation of gene populations. Part 2: Simulation model. Part 3: Return of the model to the reality. Bulletin of Mathematical Biology, 52, 741 — 772.
   Google Scholar
• KToms, P. Y., and GOSALIA, A. P., 1981, Spectral analysis vs. period-amplitude analysis of narrow band EEG activity: a comparison based on the sleep delta-frequency band. Sleep, 4, 193–206.
   PubMedGoogle Scholar
• UCHIDA, S., MATSUURA, M., OGATA, S., YAMAMOTO, T., and AIKAWA, N., 1996, Computerization of Fujimori's method of waveform recognition. A review and methodological considerations for its application to all-night sleep EEG. Journal of Neuroscience Methods, 64, 1–12.
   PubMedGoogle Scholar
• HAURI, P., and HAWKINS, D. R., 1973, Alpha-delta sleep. Electroencephalography and Clinical Neurophysiology, 34, 233 — 237.
   Google Scholar
• MOLDOFSKY, H., SCARISBRICK, P., ENGLAND, R., and SMYTHE, H., 1975, Muskuloskeletal symptoms and non-REM sleep disturbance in patients with "fibrositis syndrome" and healthy subjects. Psychosomatic Medicine, 37, 341 — 351.
   Google Scholar
• SCHEULER, W., STINSHOFF, D., and Kusicm, S., 1983, The alpha-sleep pattern. Differentiation from other sleep patterns and effect of hypnotics. Neuropsychobiology, 10, 183 — 189.
   Google Scholar
• DUMERMUTH, G., LANGE, B., LEHMANN, D., MEIER, C. A., DINKELMANN, R., and MOLINARI, L., 1983, Spectral analysis of all-night sleep EEG in healthy adults. European neurology, 22, 322 — 339.
   Google Scholar
• CROCHEMORE, M., and RYrrER, W., 1994, Text algorithms. (New York: Oxford University Press).
   Google Scholar