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.
| Original language | English |
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| Title of host publication | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
| Publisher | IEEE |
| Pages | 1234-1235 |
| Number of pages | 2 |
| Volume | 16 |
| Edition | pt 2 |
| Publication status | Published - 1994 |
| Event | Proceedings of the 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 1 (of 2) - Baltimore, MD, USA Duration: Nov 3 1994 → Nov 6 1994 |
Other
| Other | Proceedings of the 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 1 (of 2) |
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| City | Baltimore, MD, USA |
| Period | 11/3/94 → 11/6/94 |
ASJC Scopus subject areas
- Bioengineering