A computer method for identifying patterns in electroencephalogram signals.
A computer method is developed for identifying patterns in electroencephalogram (EEG) signals. An EEG numerical signal is transformed into a symbolic series. The simple transformation used here studies the variations between two successive values of the signal. Then, this series is analysed with a symbolic correlation function based on probabilities without bias. The use of large windows, e.g. 1 hour, allows the identification of weak signals hidden by the specific ones. An application of this method to the sleep analysis of a healthy adult shows a periodicity modulo 10 in all derivations. A possible neurophysiological meaning is presented in the discussion.
Michel
C J
CJ
Equipe de Bioinformatique Théorique, Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection (LSIIT), UMR CNRS-ULP 7005, Université Louis Pasteur Strasbourg, Pôle API, Illkirch, France. michel@dpt-info.u-strasbg.fr
D000465:Algorithms; D001300:Audiometry, Evoked Response / Q000379:methods*; D001921:Brain / Q000502:physiology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D010507:Periodicity; D012890:Sleep / Q000502:physiology*