Journal of clinical monitoring and computing
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J Clin Monit Comput · Aug 2002
Clinical TrialA preliminary evaluation of a new derived EEG index monitor in anesthetized patients.
An electroencephalogram (EEG) monitoring device, recently developed by Nicolet Biomedical, analyzes both high and low EEG frequencies. A processed derivative is obtained and displayed graphically and numerically on a monitor and may be used during anesthesia to indicate anesthetic "depth." However, radio-frequency interference from electrical equipment typically used in the operating room has the potential to interfere with the analysis of the high frequency components of the EEG. ⋯ The derived EEG Index monitoring system evaluated in this study functioned satisfactorily in the operating room setting in patients undergoing general anesthesia. Interference from ESU, facial nerve stimulation, and movement of the electrode wires caused brief interruptions of the derived EEG Index display and did not affect its utility in monitoring brain activity during anesthesia.
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J Clin Monit Comput · Aug 2002
Clinical TrialUnconsciousness indication using time-domain parameters extracted from mid-latency auditory evoked potentials.
The mid-latency auditory evoked potential (MLAEP) has been used to indicate depth of anaesthesia, and is usually analysed in time-domain. This work compares three techniques: Wave Deformation Parameters (PDO), Auditory Evoked Potential Index (AEPidx) and an automatic Nb-wave latency estimator (Nb), in the assessment of unconsciousness onset based on EEG under auditory stimulation. ⋯ The results suggest that, at least to indicate unconsciousness, the most reliable effect of the anaesthetic drug on MLAEP would be the amplitude attenuation. Despite the high dependence on noise due to its time-domain basis, the Attenuation-PDO seems to be adequate to assess depth of anaesthesia.
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J Clin Monit Comput · Aug 2002
Temporal patient state characterization using Iterative Order and Noise (ION) estimation: applications to anesthesia patient monitoring.
As more sensors are added to increasingly technology-dependent operating rooms (OR), physicians such as anesthesiologists must sift through an ever-increasing number of patient parameters every few seconds as part of their OR duties. To the extent these many parameters are correlated and redundant, manually monitoring all of them may not be an optimal physician strategy for assessing patient state or predicting future changes to guide their actions. ⋯ This paper illustrates the use of ION to improve significantly the performance of PCA in the efficient representation of patient state and in improving the performance of linear predictors of clinically significant parameters.