AUTOREGRESSIVE MODELING WITH EXOGENOUS INPUT OF MIDDLE-LATENCY AUDITORY-EVOKED POTENTIALS TO MEASURE RAPID CHANGES IN-DEPTH OF ANESTHESIA

Obtaining an adequate depth of anesthesia is a continuous challenge to the anesthetist. With the introduction of muscle-relaxing agents the traditional signs of awareness are often obscured, or difficult to int erpret. These signs include blood pressure, heart rate, pupil size, et c. However, these factors do not describe the depth of anesthesia (DA) in a cerebral activity sense. Hence, a better measure of the DA is re quired. It has been suggested that Auditory-Evoked Potentials (AEP) ca n provide additional information about the DA. The general method of e xtracting AEP is by use of a Moving Time Average (MTA). However, the M TA is time consuming because a large number of repetitions is needed t o produce an estimate of the AEP. Hence, changes occurring over a smal l number of sweeps will not be detected by the MTA average. We describ e a system-identification method, an autoregressive model with exogene ous input (ARX) model, to produce a sweep-by-sweep estimate of the AEP . The method was clinically evaluated in 10 patients anesthetized with alfentanil and propofol. The time interval between propofol induction and the time when the Na-Pa amplitude was decreased to 25% of the ini tial amplitude was measured. These measurements showed that ARX-estima ted compared to MTA-estimated AEP was significantly faster in tracing transition from consciousness to unconsciousness during propofol induc tion (p < 0.05).