Analysis of temporal non-stationarities in EEG signals by means of parametric modelling

G. Tognola, P. Ravazzani, F. Minicucci, T. Locatelli, F. Grandori, J. Ruohonen, G. Comi

Research output: Contribution to journalArticlepeer-review


A method for the analysis of variability of EEG signals is described. We examined simulated signals and real EEGs obtained from a normal subject and two epileptic patients. The first step of the method is based on autoregressive (AR) modelling of short EEC epochs. Prediction coefficients of the AR model were computed as a function of time from partially-overlapping moving windows of 2 s duration. The temporal behaviour of these coefficients was analysed to detect variability: quasi-stationary activity causes only smooth changes in the coefficients while variations in the amplitude and/or the frequency content of the signal are shown to produce sharp changes in the coefficients. A segmentation algorithm was developed to detect and quantify with a numerical value (Difference Measure, DM) the AR coefficients variations.

Original languageEnglish
Pages (from-to)169-185
Number of pages17
JournalTechnology and Health Care
Issue number2
Publication statusPublished - Aug 1996


  • autoregressive modelling
  • clinical EEG
  • non-adaptive segmentation
  • temporal variability

ASJC Scopus subject areas

  • Biophysics


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