1 Department of Clinical Neurophysiology and 2 Department of Anaesthesiology and Intensive Care, Kuopio University Hospital, Finland. 3 VTT Information Technology, Tampere, Finland. 4 Departments of Anaesthesiology and Intensive Care Medicine, Helsinki University Hospital, Jorvi Hospital, Espoo, Finland
* Corresponding author. E-mail: susanna.westeren-punnonen{at}kuh.fi
Accepted for publication December 15, 2004.
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Abstract |
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Keywords: heart, ventricular fibrillation ; monitoring, electroencephalography ; monitoring, event related potentials ; monitoring, intensive care
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Introduction |
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Case report |
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The background EEG, recorded during the auditory stimulation, was analysed in 5-s epochs with a 50% overlap (300 s from the beginning of the recording). Serious artifacts were excluded by checking the maximum amplitude for each epoch; if the amplitude was >75 µV, the epoch was excluded. First, the RMS power was calculated from the EEG epochs. Then, the power spectral density (PSD) for each epoch was estimated using Welsh's averaged periodogram method. Spectral entropy, spectral edge frequency (SEF95) and median power frequency (MPF) were computed from the PSD using a frequency range of 0.532 Hz. Relative powers for total (0.532 Hz), delta (0.54 Hz), theta (48 Hz), alpha (813 Hz), beta-1 (1320 Hz) and beta-2 (2032 Hz) bands were also computed. Finally, the median of the accepted epochs was computed for all the EEG parameters.
For ERP analysis the data were transformed to epochs of 100 to 900 ms relative to the onset of each stimulus. After artifact rejection (rejection levels +75 µV and 75 µV), the responses to standard and deviant tones were averaged separately. The averaged data were filtered digitally with low-pass cut-off frequency at 15 Hz. The N100 component was defined as a maximum negative deflection between 80 and 150 ms after onset of the stimulus.
A CS3 monitor with an EEG module (Datex-Ohmeda, Helsinki, Finland) was used for additional EEG monitoring during postoperative recovery. The EEG signal was recorded from the mastoidmidline (A1Fz and A2Cz) and centralparietal (C3P3 and C4P4) derivations. A Datex-Ohmeda recording system digitized the EEG signal at 100 Hz, which was then stored and analysed offline. The spectral power was calculated for 5-s epochs with a 50% overlap in the frequency band 0.532 Hz. The median values were computed before the beginning of the VF (440 s determined from the ECG signal) and after the EEG pattern had changed as a result of the VF (290 s).
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Discussion |
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Auditory evoked potentials reflect the conduction of an auditory stimulus in the cochlear nerve and brainstem (brainstem auditory evoked potentials [BAEP]), and further to the auditory cortex (middle-latency auditory evoked potentials [MLAEP]). Thus auditory stimulus processing in the brain is best reflected in the ERPs. The ERPs are affected by both the physical properties of the auditory stimulus and the psychological state of the subject. Therefore sleep, sedation and coma modify the amplitude, latency and shape of the ERP peaks. The most prominent ERP peak is the N100 component, which appears 100 ms after the onset of the stimulus and has been associated with early discrimination of incoming stimuli.7 The loss of the N100 component during propofol sedation has been proposed as a marker of the transition from consciousness to unconsciousness,8 but other reports suggest that this component remains visible during general anaesthesia911 and deep sedation postoperatively.2 In addition, the visible ERP components have been shown to correlate with regaining of consciousness and a generally good prognosis in comatose patients.12 13
Cardiopulmonary bypass may cause cerebral hypoperfusion and EEG changes. Furthermore, anaesthetic drugs and sedation level alter the EEG waveform. Vasoactive drugs given after the resuscitation may also contribute to the EEG waveform. Therefore the effect of these factors on various parameters of the EEG cannot be completely excluded in this case. During sedation, the patient had considerably reduced values in some EEG parameters (MPF and SEF95) after VF compared with patients who recovered from CABG without complication.3 In our case the delta power was increased when compared with that of patients recovering normally. This may imply that the slowing of the EEG was mostly caused by the cardiac arrest.
The N100 component was not identifiable when the patient was deeply sedated. This might have been due to the pharmacological effect of sedation. We have reported recently that six of 26 patients recovering normally from CABG did not have a detectable N100 component during deep sedation.2 In the present case, the N100 component had recovered to baseline level during moderate sedation, even though the background EEG was still very slow. This may suggest that the N100 component recovers from propofol sedation more quickly than the EEG. However, VF initially caused a considerable suppression and slowing of the EEG, and during moderate sedation the background EEG was still very slow for that sedation level. Therefore it can also be speculated that VF had a lesser effect on the N100 component than on the background EEG. Thus our findings might also suggest that the recording of evoked potentials may improve the evaluation of the brain function after cardiac arrest.
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Acknowledgments |
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References |
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