T74. Factors that reduce the accuracy of seizure identification using quantitative EEG displays

Abstract

Introduction Quantitative electroencephalography (QEEG) trending displays are being used increasingly commonly to facilitate seizure identification during critical care EEG monitoring. Our previous work showed that the accuracy of seizure identification using QEEG is lower in some recordings, even for EEG experts. In this study, we sought to determine which seizure characteristics make identification on QEEG more difficult. Methods Three neurophysiologists were independently asked to mark all suspected seizures in 27 continuous EEG recordings from critically ill children that had been processed using two QEEG techniques: amplitude-integrated EEG (aEEG) and color density spectral array (CDSA). The raw EEG was concealed. We analyzed the following factors for their influence on the probability of correct seizure identification: spectral characteristics of individual seizures such as power and frequency (both absolute during the seizure and relative to the immediate pre-ictal background); number of seizures per recording, rate of seizures per hour and age of the child. We then performed a multivariable regression analysis to identify independent predictors of poor seizure detectability on CDSA and aEEG. Results Seizure detectability declined with decreasing seizure duration. Using CDSA, seizure identification rates were 65% for seizure durations of >180 s, 62% for 60–179 s, 44% for 30–59 s and 16% for less than 30 s. Using aEEG, seizure identification rates were 81% for seizures lasting >180 s, 67% for 60–179 s, 38% for 30–59 s and 13% for less than 30s. Focal seizures were more difficult to detect on CDSA/aEEG (26%/33%) compared to both hemispheric seizures (44%/53%) and generalized seizures (69%/64%). With CDSA, additional independent predictors of poor seizure detectability were: younger age, lower number of seizures during the recording, lower ictal/pre-ictal spectral edge frequency at 50% power (Ratio-SEF50), lower ictal/pre-ictal spectral edge frequency at 95% power (Ratio-SEF95), and lower ictal/pre-ictal average total power (Ratio-TTLPower). With aEEG, other independent predictors of poor seizure detectability were: lower number of seizures during the recording, lower Ratio-SEF50, and lower Ratio-TTLPower. Conclusion The ability of neurophysiologists to identify seizures on both CDSA and aEEG declines significantly as seizures become shorter and more focal, with fewer seizures during the recording, and with lower ictal/pre-ictal power and spectral edge frequency. Also, for CDSA but not aEEG, seizure detection was more difficult in younger children. These insights into the characteristics that reduce seizure detectability using QEEG trending displays will help to inform the optimal use of these displays, and the design of new QEEG trends that may permit more accurate seizure identification.