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 EEG 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.
Analysis of temporal non-stationarities in EEG signals by means of parametric modelling
Tognola G;Ravazzani P;Grandori F;
1996
Abstract
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 EEG 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
