EOG artifact removal using a wavelet neural network
Neurocomputing
| Hi-index | 0.00 |
We describe a model for simulating a spontaneouselectroencephalogram (EEG) and for simulating the effects ofanesthesia on the EEG, to allow anesthesiologists and EEGtechnicians to learn and practice intraoperative EEG monitoring.For this purpose, we developed a linear model to manipulate theamplitude of the activity in each of the traditional EEG frequencybands. Burst suppression patterns are simulated by manipulating anoverall gain. To demonstrate the model feasibility, modelparameters for thiopental and isoflurane were estimated guided bypublished data on the EEG effects of these anesthetic drugs. Usingthese estimates, EEG time signals were simulated for isoflurane atvarious partial pressures, and for bolus intravenous doses ofthiopental. Comparison with actual recorded EEG signals showed thatthe changes produced by isoflurane and thiopental in the simulatedsignals are very similar to the changes in the actual signals,which was confirmed by two clinicians with experience and routinepractice in intraoperative EEG monitoring.