Title :
A parametric method for the analysis of temporal and spatial variability in the interictal EEG signal
Author :
Tognola, G. ; Ravazzani, P. ; Locatelli, T. ; Minicucci, F. ; Grandori, F. ; Comi, G.
Author_Institution :
Dept. of Biomed. Eng., Politecnico di Milano, Italy
Abstract :
The analysis of variability in the EEG signal is a relatively new field of investigation. This is mainly due to the objective difficulty to develop quantitative methods of analysis. Autoregressive modeling of the EEG signal is proposed to quantify its variability. Model coefficients were computed from adjacent epochs and their temporal behavior was analyzed: background activity produced only very slow temporal changes, while a variability in the EEG provoked sharp changes in the AR sequences. To quantify the variability with a numerical value (Difference Measure, DM), the AR sequences were processed by means of a segmentation algorithm. DMs were derived for all EEG leads and analyzed under visual inspection. Preliminary results show that this approach could be of some help in the study of temporal and spatial characteristics of interictal epileptiform discharges
Keywords :
electroencephalography; adjacent epochs; autoregressive modeling; background activity; difference measure; interictal EEG signal analysis; interictal epileptiform discharges; model coefficients; parametric method; segmentation algorithm; spatial variability; temporal variability; variability analysis; Biological materials; Biomedical measurements; Brain modeling; Delta modulation; Electroencephalography; Epilepsy; Inspection; Neurophysiology; Power measurement; Signal analysis;
Conference_Titel :
Engineering in Medicine and Biology Society, 1994. Engineering Advances: New Opportunities for Biomedical Engineers. Proceedings of the 16th Annual International Conference of the IEEE
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-2050-6
DOI :
10.1109/IEMBS.1994.415409
