Printer Friendly

An observational study exploring amplitude-integrated electroencephalogram and Spectral Edge Frequency during paediatric anaesthesia.

In adults, the use of processed electroencephalogram (EEG) depth monitors, such as the Bispectral Index[R] (Covidien Inc., Boulder, CO, USA) and Entropy[R] (GE Healthcare, Buckinghamshire, UK), have been shown in some studies to be associated with a reduction in anaesthetic consumption, recovery times and postoperative nausea and vomiting (1,2). However, the effect of their use on the incidence of intraoperative awareness remains controversial (3-5). It is less clear if the currently available depth of anaesthesia monitors offer benefits in the paediatric population (6). Studies attempting to validate these devices in children have shown that in older children they usually have performance characteristics similar to adults, but in children younger than two years of age there is increasing evidence that the monitors do not behave as they do in adults or older children (6-11). These findings are not surprising considering the developmental changes that occur in the EEG with increasing age (12,13), such as the gradual increase in dominant awake frequency with age (14). This change in dominant frequency would be expected to be particularly relevant to processed EEG parameters that use the frequency domain.

The amplitude-integrated electroencephalogram (aEEG) is a digitised version of the Cerebral Function Monitor developed by Maynard and colleagues (15). As a time domain parameter, aEEG analysis involves EEG information being subjected to an envelope detection algorithm. This time compression method permits assessment of EEG amplitude changes (16). Initially used to monitor critically ill adults (15), the Cerebral Function Monitor has also been shown to detect anaesthetic-induced changes in adults (17). More recently, the aEEG has been adopted into neonatal practice (16) for seizure detection (18) and long-term monitoring after hypoxic ischaemic events (19,20).

Unlike the aEEG, which uses the time domain of the EEG, the Spectral Edge Frequency (SEF) assesses EEG in the frequency domain. SEF is the frequency in an epoch of EEG data below which a set percentage of the EEG power spectrum is contained. Investigations of SEF during anaesthesia in the adult population have demonstrated consistent changes (21).

A previous pilot study in infants (22), found that while there were no changes with emergence from anaesthesia in the SEF 90% (SEF90), there was a suggestion that changes did occur in the aEEG. This study was limited, however, by its small sample size, only short periods of artefact-free recording and very little recording of participants in the completely awake state. Hence, further examination of the aEEG and SEF90 parameters was required. We decided that more evidence that aEEG does respond to anaesthesia in an observational setting is required before performing the more technically and ethically challenging studies to determine dose responses in children's aEEG and SEF90 under controlled anaesthetic conditions. Thus, the aim of this prospective observational study was to investigate, in a number of different age groups, the changes in aEEG between awake and anaesthetised states and compare these to changes in SEF90. A secondary aim was to examine potential EEG differences according to hemispheric location.


The study was approved by the Royal Children's Hospital Human Research Ethics Committee (Reference 28116A). Due to the exploratory nature of this observational study no power calculation was undertaken prior to recruitment. Pragmatically, the number of children chosen was the number that was expected to be enrolled within one year. As the younger brain develops at a more rapid rate, recruitment of children was stratified into six age groups and skewed toward younger children. The six age groups were: one day to six months, six to 12 months, one to two years, two to four years, four to eight years and eight to 14 years. Inclusion criteria were all children scheduled for general anaesthesia. However, children were excluded if they had a known neurological condition or if the sensors would interfere with procedures such as facial surgery or magnetic resonance imaging.

Informed, written consent was obtained from a parent of the child. Then if time and the child would allow, EEG sensors were placed prior to induction of anaesthesia. If this caused distress to the child, the sensors were placed once the child was anaesthetised. Sensor sites were [C.sub.3] [C.sub.4,] [P.sub.3] [P.sub.4], and [F.sub.p1] and [F.sub.p2] according to the international 10-20 system (23), and a reference electrode behind an ear. BrainZ[TM] Hydrogel Sensors (Natus Medical Incorporated, San Carlos, CA, USA) were applied to the scalp after exfoliation of the site and application of a small amount of conductive paste. Sensors remained in situ until the child was awake and about to leave the post anaesthesia care unit.

During this observational study, the choice of anaesthetic was at the discretion of the treating anaesthetist. Signal quality data and raw EEG were visible only to research staff for the duration of the anaesthetic. (Anaesthetic staff did not have access to the EEG information as the monitor was turned away from their line of view.) Processed EEG parameters were not available until later analysis. Collected demographic and perioperative data included age of the child, procedure, any preexisting medical illness, any regular medication and use of sedative premedication. Once EEG recording had commenced, the EEG tracings were directly annotated. Relevant marked points included onset of anaesthesia, insertion of airway and awakening.

SEF90 is obtained by subjecting raw EEG to a spectral analysis using a Fast Fourier Transform. In a given epoch the SEF90 is the highest frequency (Hz) at which 90% of the power spectrum is contained (24). Generating aEEG involves raw EEG being passed through an asymmetric filter (25). The filtered signal is then rectified to convert the negative portion of the EEG signal to positive (26). Utilising an envelope detection method, the rectified signal is passed through a circuit that follows the input peaks as they rise in amplitude (27). This provides the maximum aEEG parameter. Once at the top of the peak, the trace exponentially decays until there is another peak in the rectified EEG. The decay rate and the distance between EEG peaks determine the minimum aEEG (26). Within the frequency range 2 to 20 Hz the ReBrim[TM] monitor (BrainZ Instruments Limited, Auckland, New Zealand) calculates the aEEG and SEF90 from four second epochs (28).

EEG data were analysed offline following conversion to BrainZ Rescue Monitor[TM] format using File Converter version 1.03 (BrainZ Instruments Limited, Auckland, New Zealand). Once in BrainZ Rescue Monitor format, files were reviewed using AnalyzeResearch 1.7 (BrainZ Instruments Limited, Auckland, New Zealand). SEF90, maximum and minimum aEEG, Mains Hum (HUM), Impedance (IMP) and electromyography (EMG) values along with event marks contained in one minute epochs were then exported to Microsoft[R] Excel 2002 (Microsoft Corporation, Washington, USA). Combined data was then imported into Matlab[R] Version 7.7.0 (R2008b) (The MathWorks[TM], Inc. Natick, MA, USA) for filtering and analysis. Filtering consisted of removing periods of high IMP, HUM and EMG artefact. A bespoke Matlab program was used to identify EEG data with periods of IMP >10 kOhm, HUM >50 [micro]Vpp and EMG >10 [micro][V.sup.2]. Once a period of excessive artefact was identified this line of EEG data was removed. In addition, to reduce the impact of interference on adjacent lines of data, the program also removed the minute before and after. This filtering removed contamination with artefact such as electrocautery and movement.

EEG parameters were analysed when children were 'awake' and 'during anaesthesia'. To obtain the 'during anaesthesia' EEG data, a Matlab program was written that examined the availability of individual patient's EEG data 10 minutes after the establishment of the artificial airway. If this portion of data had been removed, due to excessive artefact, the program then searched, at increasing minutes, for usable data until the time of airway removal. The presence of anaesthetic stability or surgical stimuli was not ascertained.

To identify suitable 'awake' EEG data, the filtered EEG parameters were plotted against time for individual patient files. These revealed periods of post-filtered EEG available for analysis. The raw EEG was then visually inspected, around the identified timepoint, to identify one minute of suitable data. The data was rejected if it had excessive artefact, events marks indicating atypical activity such as the patient touching the sensors, or indicating the patient was asleep.


Analysis of children's data was undertaken overall and within their six predefined age groups. Statistical software package STATA[R], Release 11.0 (STATA Corporation, College station, Texas, USA) was utilised for the statistical analysis. Assessment of awake and anaesthetised EEG data was made on one minute epochs.

Assessment of the influence of montage on aEEG and SEF90 recordings comparisons of [F.sub.p1]-[F.sub.p2] (forehead), [C.sub.3]-[P.sub.3] (left) and [C.sub.4]-[P.sub.4] (right) montages were made using a non-paired Student's t-test. A Student's t-test was also conducted on the agegrouped data.

To identify the correlation coefficient between EEG parameters and age, a linear regression analysis was used. This analysis was applied on values obtained while awake and during anaesthesia. Initially regression analysis was conducted on all age groups then post hoc on children under and over two years of age.

EEG parameters obtained during awake and anaesthesia for the different ages were compared with a non-paired Student's t-test. The influence of age and timepoint was analysed using a two-way analysis of variance. Throughout the analyses a P value of <0.05 was considered statistically significant.


A total of 213 families were approached to participate in the study. Of those approached 33 (15%) declined to take part. Post-consent, two children were not included in the analysis. The first of these was excluded as the patient's procedure was cancelled due to an emerging viral illness with associated pyrexia. The second child, once the EEG sensors were applied, was noted to have a marked reduction in the amplitude from the right hemisphere. This child was subsequently investigated and found to have had a previously undiagnosed intra-uterine stroke.

The remaining 178 children ranged in age from 24 days to 14 years and further demographic details are contained in Table 1.

Across the groups there were a larger proportion of males (67%) than females (33%). The types of procedures varied according to age. There was a higher incidence of gastrointestinal endoscopies and orthopaedic procedures in older children. Younger children underwent more hernia repairs, intraabdominal surgery, genital surgery or cystoscopies.

Children received a range of medications and anaesthetic agents. Premedication varied according to clinical requirements. Across the ages premedication with midazolam was unevenly distributed. More children aged between two and eight years received midazolam premedication. There was an even distribution of inhaled anaesthetic usage across age groups, but there was a higher use of propofol in the older age groups.

With reducing artefact tolerances there was a corresponding loss of data available for analysis. The previously reported tolerances of IMP >10 kOhm, HUM >50 [micro]Vpp and EMG >10 [micro][V.sup.222] demonstrated an acceptable preservation of data.

EEG recordings obtained while the children's breathing was maintained with an artificial airway had a median duration of 33 minutes (range 6 to 153 minutes). Total anaesthetised samples used were forehead n=118, and parietal n=122. Given the varied time duration of the procedures the post filtered data available for analysis reduced with time. Utilisation of the timepoint 10 minutes after the establishment of the artificial airway to start to retrieve data for subsequent analysis corresponded with peak availability of post filtered data. Thus, a majority of the data was obtained from this 10 minute timepoint (forehead 110 samples and parietal 106 samples).

Hemispheric comparisons of EEG parameters when awake and anaesthetised

SEF90 and aEEG values obtained from left and right hemispheres during anaesthesia or awake for all age groups showed no significant difference between left and right montages (P >0.4). Subsequently, the mean of the left and right EEG data was used for analysis as a parietal value.

When aEEG values from all children were analysed, whether awake or anaesthetised, those obtained from the forehead montage were lower than those from the parietal area (P <0.0001) (anaesthetised values shown in Table 2). SEF90 showed no statistically significant difference in awake values obtained from the forehead compared to the parietal montage (mean difference = 0.9 Hz; P=0.15), but forehead values were higher than parietal during anaesthesia (mean difference= 2.0 Hz; P <0.001).

Between forehead and parietal montages, in different age groups, there was insufficient forehead awake EEG data to perform a non-paired t-test on children aged six months to one year and eight to 14 years. While anaesthetised, the difference between the parietal and forehead aEEG of children changed with increasing age, as shown in Table 2. Children younger than four years demonstrated higher aEEG values from the parietal compared to forehead montages (P <0.05). Children over four years of age showed no statistically significant difference.


During anaesthesia there was no difference in SEF90 obtained from forehead compared to parietal montages in children less than six months of age (P=0.6). In children older than six months the SEF90 obtained from the forehead montage was higher than the parietal recordings, as detailed in Table 2.

Changes in aEEG and SEF90 during anaesthesia related to age

When the aEEG of all children was examined there was a low correlation with increasing age from the parietal montage (r2=0.06, P <0.0065) whereas the forehead montage demonstrated a moderate degree of correlation with age (r2=0.3, P <0.00001), shown in Figures 1A and B. Visual inspection of the dot plots, of children's age compared to aEEG, indicated a possible age-related change at approximately two years of age, shown in Figures 1A and B. Thus a post hoc piecewise analysis was performed on children over and under two years of age. From children below two years of age there was a consistently higher degree of correlation between age and aEEG in the parietal aEEG (aEEG maximum [r.sup.2]=0.45, P <0.00001; aEEG minimum [r.sup.2]=0.44, P <0.00001) and forehead aEEG ([r.sup.2]=0.6, P <0.00001). Of children older than two years of age, there was no correlation between age and aEEG (parietal aEEG maximum [r.sup.2]=0.08, P=0.053, P=0.062; aEEG minimum [r.sup.2]=0.07, P=0.071) and all forehead aEEG ([r.sup.2] <0.03, P >0.3).

There was a low correlation between age and SEF90 from forehead (r2=0.0015, P=0.6) or parietal ([r.sup.2]=0.008, P=0.34) montages, as shown in Figure 1C and D. In children younger than two years of age the correlation between SEF90 and age for the parietal montages remained low ([r.sup.2]=0.0009, P=0.8), while there was an increased degree of correlation between SEF90 and age in the forehead montage ([r.sup.2]=0.17, P=0.0003).


Changes in aEEG and SEF90 while awake according to age

EEG sensors were applied pre-anaesthesia to 168 children. These recordings had a median duration of 48 minutes (range 4 to 330 minutes). Awake recordings showed a large amount of contamination from artefact such as electromyogram, blinking and movement. This was particularly evident in forehead regions with only 21 (13%) awake data points available for analysis. From the parietal recordings there were 93 (55%) useble awake data points.

Overall, aEEG from awake children showed a poor degree of correlation with age in the forehead ([r.sup.2] <0.02, P >0.4) and parietal ([r.sup.2] <0.03, P >0.1) montages as shown in Figure 2A and B. When the children younger than two years of age were analysed independently there was a consistently higher degree of correlation between aEEG parameter and age in the forehead ([r.sup.2] >0.3, P <0.04) and parietal ([r.sup.2] >0.28, P <0.0001) aEEG values.

SEF90 of all children showed a moderate to large degree of correlation with age in the samples taken from parietal montages ([r.sup.2]=0.5, P <0.0001), as shown in Figure 2C. Awake SEF90 median values of age grouped data also increased with age, shown in Figure 3C. There was a weak correlation with age from forehead montage ([r.sup.2]=0.2, P=0.03), as shown in Figure 2D. When the children younger than two years of age were analysed independently, similar correlations to the 'all children' analysis were seen with only a slight reduction in the degree of correlation of SEF90 with age in the parietal montages ([r.sup.2]=0.4, P <0.0001) and little change in the forehead values ([r.sup.2]=0.2, P=0.14).


Differences in aEEG and SEF90 between awake and anaesthetised

From all children parietal aEEG values were higher during anaesthesia compared to awake values. While parietal SEF90 demonstrated no significant difference between awake and anaesthetised values (mean difference -0.6 Hz, P=0.099). Once grouped into their prospectively defined six age groups, the parietal aEEG values were usually higher in anaesthetised compared to awake states, as seen in Figure 3A and B. Mean differences between the states of awake and anaesthetised aEEG recordings differed according to age group. Children younger than six months of age demonstrated the smallest difference between states. There was not a statistically significant difference between awake and anaesthetised, parietal aEEG minimum, of children aged less than six months (mean difference 3.7 [micro]V P=0.063). Between children aged younger than six months to children aged between two to four years the aEEG mean difference, between awake and anaesthetised values, increased with increasing age. Children aged between two to four years and four to eight years showed similar aEEG mean differences between awake and anaesthetised values. While children aged between eight to 12 years of age demonstrated lower aEEG mean differences between awake and anaesthetised values, these were similar to those obtained from children aged between one to two years of age.

A lack of available forehead EEG data limited the analysis between forehead awake and anaesthetised states for the age groups between 6 to 12 months and 8 to 14 years of age. In the remaining age groups, the forehead aEEG recordings in children younger than six months of age showed no evidence for a difference between awake and anaesthetised states. In the older age groups an increasing mean difference was observed.

Parietal SEF90 from children older than two years of age was lower during the anaesthetised state compared to the awake state as shown in Figure 3C. Conversely, in children younger than six months of age, parietal SEF90 was higher in the anaesthetised compared to awake state (mean difference 2.1 Hz, P=0.0002).

The two-way analysis of variance for all parameters demonstrated evidence for an interaction (P <0.001) between state (anaesthetised and awake) and age group; the effect of state depending on the age group.


This exploratory observational study investigated aEEG and SEF90 changes occurring in 178 children while receiving an anaesthetic according to the anaesthetist's discretion. The main findings were that aEEG and SEF90 parameters of children change according anaesthetic state. However, these changes with anaesthetic are age-related. In addition, aEEG and SEF90 parameters obtained from frontal montages compared to parietal locations, also demonstrated changes with age.

During anaesthesia the absolute magnitude of aEEG increases with age to those of around two years of age. For those older than two years of age, aEEG demonstrated little in the way of age-associated changes. In the awake state aEEG also increased in magnitude with age but the change was less than while anaesthetised. Consistent with this finding, the aEEG parameters showed agerelated differences between awake and anaesthetised states. In all age groups aEEG was greater in the anaesthetised state; the absolute difference was very small in infants and increased with age.

SEF90 did not change with age in the anaesthetised state while there was some modest rise in SEF90 with age in the awake state. There was no SEF90 difference found in the anaesthetised compared to the awake state for children aged between six to 12 months, and paradoxically SEF90 was greater in children younger than six months of age. A reduction in SEF90 of children older than two years of age in response to anaesthetics, is in keeping with adult studies investigating SEF during anaesthesia (29,30). Previously Davidson and colleagues (22) reported that SEF90 in children younger than two years of age could not discriminate between anaesthetised and emergence states. This current larger study provides support for this finding; as the SEF90 of children younger than two years of age, owing to their predominantly lower awake frequency, either remained the same or conversely increased in response to anaesthetics.

The lack of evidence for a difference in SEF90 between awake and anaesthetised state in infants is consistent with the poor performance, in this age group, of depth monitors such as Bispectral Index that predominantly rely on analysis of the frequency domain. Unlike SEF90, aEEG did show some evidence for a difference between anaesthesia and awake state in infants. The difference was marginal but this finding does suggest further more controlled studies should be done to explore the possibility that aEEG could be used to measure anaesthesia depth in infants. It should be noted that the full complexities of EEG changes that occur during anaesthesia (31) are not accounted for in this algorithm. At higher doses of some anaesthetics, burst suppression occurs, while others provoke higher frequency beta activity (31). These changes will not be accounted for in single dimension algorithms such as SEF90 or aEEG. Future investigations will be required to assess age-related changes to burst suppression patterns or beta activation.

For the duration of this study the choice of anaesthetic remained at the anaesthetist's discretion. It is recognised that this introduces a heterogeneity which might reduce the power of this explorative study. A more controlled anaesthetic regimen would be better suited to determine the exact relationship between anaesthesia and EEG at different stages. However, an anaesthesia protocol which prescribes the anaesthetic is more difficult to perform and raises ethical issues if the protocol is not in accordance with what the treating doctor considers is in the child's best interest. Thus, before designing such a study it was decided to perform this exploratory observational study where the anaesthetic was not prescribed, to establish directions for future investigations. There is a possibility that our comparisons between ages may be influenced by factors not controlled for in this study such as use of premedication, varying dose of agents, different agents and varying surgical stimuli. Future studies will need to control for these variables.

In addition, due to the exploratory nature of this study, no sample size calculation was undertaken prior to recruitment. The lack of available research, regarding the EEG of children during anaesthesia, meant that relevant differences in the EEG were not available to complete a power calculation. Thus, there will be uncertainty regarding the true scientific significance of the reported findings. However, this study provides preliminary information that will help to inform future investigation into the EEG of children during anaesthesia.

Another limitation of this study is the asymmetrical filter used by the ReBrim monitor prior to processing EEG processing. This filter strongly attenuates activity below 2 Hz and above 20 Hz (26). EEG activity relevant to the monitoring of EEG during anaesthesia may occur outside these ranges such as information in the delta, higher beta (32) and gamma (33) frequencies. However, examination of the EEG within the 2 to 20 Hz range reduces artefact contamination and reduces the signal-to-noise ratio (34). Future studies will need to establish the clinical relevance of paediatric EEG information outside the 2 to 20 Hz range and overcome the challenges of ensuring EEG data is not contaminated with artefact.

This study also found that during anaesthesia, children younger than four years of age have lower aEEG from forehead regions compared to the parietal recordings, and in older children the forehead SEF90 was significantly higher than parietal recordings. The findings of this study imply that choice of montage is important when choosing potential EEG parameters to measure anaesthesia depth in children. However, the use of three EEG montages is also a limitation of this study. Attempts to obtain larger montage recordings may have led to increasing distress to awake children (entertaining children, and attaching seven sensors, then keeping the sensors in situ while the child was awake was challenging). Much of the awake EEG data was contaminated with artefact and removed by the post hoc filtering. This artefact was particularly evident in the forehead region due to blinking and facial movements. To improve the quality of the awake data providing an area where the children could be quiet with eyes closed may have revealed cleaner EEG data.


This exploratory study of a heterogeneous clinical population found that in older children there are clear differences in aEEG and SEF90 between awake and anaesthetised states. In infants the difference in aEEG between awake and anaesthetised is less clear, while SEF90 is an inconsistent discriminator of anaesthetic state. These findings support further investigation of aEEG as a measure of anaesthesia depth in infants.


This project was supported by the Victorian Government's Operational Infrastructure Support Program. BrainZ instruments/Natus provided salary support for data collection, loan of ReBrim monitor and consumables. S. McKeever received Postgraduate Health Research Scholarships from the Murdoch Childrens Research Institute, Melbourne and a Trust Fund Faculty Research Scholarship from the Faculty of Medicine, Dentistry and Health Sciences at The University of Melbourne. zzz REFERENCES

(1.) Punjasawadwong Y, Boonjeungmonkol N, Phongchiewboon A. Bispectral index for improving anaesthetic delivery and postoperative recovery. Cochrane Database Syst Rev 2007; 17:CD003843.

(2.) Bein B. Entropy. Best Pract Res Clin Anaesthesiol 2006; 20:101-109.

(3.) Avidan MS, Jacobsohn E, Glick D, Burnside BA, Zhang L, Villafranca A et al. Prevention of intraoperative awareness in a high-risk surgical population. N Engl J Med 2011; 365:591-600.

(4.) Myles PS, Leslie K, McNeil J, Forbes A, Chan MTV. Bispectral index monitoring to prevent awareness during anaesthesia: the B-Aware randomised controlled trial. Lancet 2004; 363:17571763.

(5.) Avidan MS, Zhang L, Burnside BA, Finkel KJ, Searleman AC, Selvidge JA et al. Anesthesia awareness and the bispectral index. N Engl J Med 2008; 358:1097-1108.

(6.) Davidson AJ. Monitoring the anaesthetic depth in children an update. Curr Opin Anaesthesiol 2007; 20:236-243.

(7.) Edwards JJ, Soto RG, Bedford RF. Bispectral Index values are higher during halothane vs. sevoflurane anesthesia in children, but not in infants. Acta Anaesthesiol Scand 2005; 49:1084-1087.

(8.) Murat I, Constant I. Bispectral index in pediatrics: fashion or a new tool? Paediatr Anaesth 2005; 15:177-180.

(9.) Watcha MF. Investigations of the bispectral index monitor in pediatric anesthesia: first things first. Anesth Analg 2001; 92:805-807.

(10.) Shander A, Lobel G. Caveats of bispectral index monitoring in the pediatric population. Chest 2005; 128:14-16.

(11.) Rodriguez RA, Hall LE, Duggan S, Splinter WM. The bispectral index does not correlate with clinical signs of inhalational anesthesia during sevoflurane induction and arousal in children. Can J Anesth 2004; 51:472-480.

(12.) Bell MA. The Ontogeny of the EEG During Infancy and Childhood: Implications for Cognitive Development. In: Garreau B, editor. Neuroimaging in Child Neuropsychiatric Disorders. Berlin, Springer-Verlag 1998. p. 97-111.

(13.) Bell MA, Fox NA. Brain Development Over the First Year of Life: Relations Between EEG Frequency & Coherence and Cognitive & Affective Behaviors. In: Dawson G, Fischer KW, eds. Human Behavior and the Developing Brain. New York, Guilford, 1994. p. 314-345.

(14.) Matousek M, Petersen I. Frequency Analysis of the EEG in Normal Children and in Normal Adolescents. In: Kellaway P, Petersen I, eds. Automation of Clinical Electroencephalography. New York, Raven Press, 1973. p. 75-102.

(15.) Maynard D, Prior PF, Scott DF. Device for continuous monitoring of cerebral activity in resuscitated patients. Br Med J 1969; 4:545-546.

(16.) Hellstrom-Westas L, de Vries LS, Rosen I. Atlas of AmplitudeIntegrated EEGs in the Newborn, 2nd ed. London, Informa Healthcare 2006.

(17.) Prior P. Monitoring in Anaesthesia. In: Prior P, ed. Monitoring Cerebral Function: Long-term Recordings of Cerebral Electrical Activity. Amsterdam, Elsevier 1979. p. 69-111.

(18.) Shany E, Khvatskin S, Golan A, Karplus M. Amplitudeintegrated electroencephalography: a tool for monitoring silent seizures in neonates. Pediatr Neurol 2006; 34:194-199.

(19.) Hellstrom-Westas L, Rosen I, Svenningsen NW. Predictive value of early continuous amplitude integrated EEG recordings on outcome after severe birth asphyxia in full term infants. Arch Dis Child Fetal Neonatal Ed 1995; 72:F34-F38.

(20.) Wertheim D, Mercuri E, Faundez JC, Rutherford M, Acolet D, Dubowitz L. Prognostic value of continuous electroencephalographic recording in full term infants with hypoxic ischaemic encephalopathy. Arch Dis Child 1994; 71:F97-F102.

(21.) Tonner PH, Bein B. Classic electroencephalographic parameters: median frequency, spectral edge frequency etc. Best Pract Res Clin Anaesthesiol 2006; 20:147-159.

(22.) Davidson AJ, Sale SM, Wong C, McKeever S, Sheppard S, Chan Z et al. The electroencephalograph during anesthesia and emergence in infants and children. Paediatr Anaesth 2008; 18:60-70.

(23.) Jasper HH. Report of the committee on methods of clinical examination in electroencephalography: The tentwenty electrode system of the International Federation. Electroencephalography and Clinical Neurophysiology 1957; 10:370-375.

(24.) Drummond JC, Brann CA, Perkins DE, Wolfe DE. A comparison of median frequency, spectral edge frequency, a frequency band power ratio, total power, and dominance shift in the determination of depth of anesthesia. Acta Anaesthesiol Scand 1991; 35:693-699.

(25.) Quigg M, Leiner D. Engineering aspects of the quantified amplitude-integrated electroencephalogram in neonatal cerebral monitoring. J Clin Neurophysiol 2009; 26:145-149.

(26.) Prior P, Maynard DE. Technical Aspects. In: Prior P, ed. Monitoring Cerebral Function: Long-term Recordings of Cerebral Electrical Activity. Amsterdam, Elsevier 1979. p. 366.

(27.) Smith NJ, Prior PF, van Gils M. Introduction to Methods for Continuous EEG and Evoked Potential Monitoring. In: Smith NJ, Prior PF, van Gils M, eds. Neurophysiological Monitoring During Intensive Care and Surgery. London, Mosby 2006. p. 448.

(28.) BrainZ Instruments Ltd. BRM2 Brain monitor operating manual. 1.10 ed. Auckland, BrainZ Instruments Ltd 2003.

(29.) Rampil IJ, Lockhart SH, Eger EI 2nd, Yasuda N, Weiskopf RB, Cahalan MK. The electroencephalographic effects of desflurane in humans. Anesthesiology 1991; 74:434-439.

(30.) Schwender D, Daunderer M, Mulzer S, Klasing S, Finsterer U, Peter K. Spectral edge frequency of the electroencephalogram to monitor "depth" of anaesthesia with isoflurane or propofol. Br J Anaesth 1996; 77:179-184.

(31.) Mahla ME. The electroencephalogram in the operating room . Seminars in Anesthesia, Perioperative Medicine and Pain 1997; 16:3-13.

(32.) Martin JT, Faulconer A Jr, Bickford RG. Electroencephalography in anesthesiology. Anesthesiology 1959; 20:359-376.

(33.) Smith NJ, Prior PF, van Gils M. Normal and Pathological Phenomena in EEG, Evoked Potential, EMG and Nerve Conduction Studies. In: Smith NJ, Prior PF, van Gils M, eds. Neurophysiological Monitoring During Intensive Care and Surgery. London, Mosby 2006. p. 109-154.

(34.) Inder TE, Buckland L, Williams CE, Spencer C, Gunning MI, Darlow BA et al. Lowered electroencephalographic spectral edge frequency predicts the presence of cerebral white matter injury in premature infants. Pediatrics 2003; 111:27-33.

S. McKEEVER *, L. JOHNSTON ([dagger]), A. J. DAVIDSON ([double dagger])

Department of Anaesthesia and Pain Management, Royal Children's Hospital; Murdoch Children's Research Institute and Melbourne School of Health Sciences, University of Melbourne, Melbourne, Victoria, Australia

* RGN, RN (Child), DipTropNurse, BSc (Hons), PhD Candidate.

([dagger]) RN, PhD, Professor and Head of the School of Nursing & Midwifery, Queen's University, Belfast, United Kingdom.

([double dagger]) MB, BS, FANZCA, GradDipEdBiostat, MD, Group Leader.

Address for correspondence: Mr S. McKeever, Department of Anaesthesia and Pain Management, Royal Children's Hospital, Flemington Road, Parkville, Vic. 3052. Email:

Accepted for publication on November 20, 2011.
Table 1
Participant demographic data

 <6 months 6-12 months

 n=29 n=30

Age in days, mean (SD) 89 261
 ([+ or -] 43.9) ([+ or -] 48.2)
Gender, M/F 22/7 23/7
 Gastrointestinal endoscopy - 1
 Penile/genital surgery/ 6 14
 Hernia repair 9 -
 Orthopaedic 1 2
 Plastic surgery 4 7
 Ophthalmic 3
 ENT 1 2
 Intra-abdominal surgery 8 1
 Bone marrow harvest - -
 Midazolam premedication - 2
 Anaesthetic variables
 Propofol 4 2
 [N.sub.2]O 22 30
 Airway: facemask/LMA/ETT 4/7/18 0/14/16 ([dagger])
Inhaled anaesthetics
 Sevo only 12 8
 Iso only 1 -
 Sevo to Iso 14 21
 Sevo to Iso to Sevo 2 1
 Opioids 7 9
 Neuromuscular blockage 8 3
 Caudal/femoral/nerve/ 13/0/1/0 12/0/1/0

 1-2 y 2-4 y

 n=29 n=31

Age in days, mean (SD) 502 1048
 ([+ or -] 109.2) ([+ or -] 190.1)
Gender, M/F 19/10 22/9
 Gastrointestinal endoscopy 2 5
 Penile/genital surgery/ 8 10
 Hernia repair 1 6
 Orthopaedic 1 1
 Plastic surgery 7 3
 Ophthalmic 2 2
 ENT 6 3
 Intra-abdominal surgery 2 -
 Bone marrow harvest - 1
 Midazolam premedication 4 8
 Anaesthetic variables
 Propofol 2 5
 [N.sub.2]O 28 30
 Airway: facemask/LMA/ETT 2/12/15 1/25/5 ([dagger])
Inhaled anaesthetics
 Sevo only 9 9
 Iso only 1 1
 Sevo to Iso 16 21
 Sevo to Iso to Sevo 3 -
 Opioids 10 8
 Neuromuscular blockage 1 -
 Caudal/femoral/nerve/ 9/1/1/0 10/2/0/2t

 4-8 y 8-14 y

 n=30 n=29

Age in days, mean (SD) 2139 3843
 ([+ or -] 423.8) ([+ or -] 596.6)
Gender, M/F 19/11 14/15
 Gastrointestinal endoscopy 2 8
 Penile/genital surgery/ 3 6
 Hernia repair 6 1
 Orthopaedic 4 9
 Plastic surgery 2 1
 Ophthalmic 4
 ENT 6 4
 Intra-abdominal surgery 2 -
 Bone marrow harvest 1 -
 Midazolam premedication 8 1
 Anaesthetic variables
 Propofol 12 15
 [N.sub.2]O 29 27
 Airway: facemask/LMA/ETT 3/18/9 ([dagger]) 0/24/5
Inhaled anaesthetics
 Sevo only 7 9
 Iso only 2 3
 Sevo to Iso 21 15
 Sevo to Iso to Sevo - 2
 Opioids 13 13
 Neuromuscular blockage 3 0
 Caudal/femoral/nerve/ 5/2/0/0 3/5/1/0


Age in days, mean (SD) -

Gender, M/F -
 Gastrointestinal endoscopy 18
 Penile/genital surgery/ 47
 Hernia repair 23
 Orthopaedic 18
 Plastic surgery 24
 Ophthalmic 11
 ENT 22
 Intra-abdominal surgery 13
 Bone marrow harvest 2
 Midazolam premedication -
 Anaesthetic variables -
 Propofol -
 [N.sub.2]O -
 Airway: facemask/LMA/ETT -
Inhaled anaesthetics -
 Sevo only -
 Iso only -
 Sevo to Iso -
 Sevo to Iso to Sevo -
 Opioids -
 Neuromuscular blockage -
Blocks -
 Caudal/femoral/nerve/ -

SD=standard deviation, M=male, F=female, ENT=ear, nose and throat,
N2O=nitrous oxide, LMA=laryngeal mask airway, ETT=endotracheal tube,
Sevo=sevoflurane, Iso=isoflurane. f One child had an LMA inserted
then required an ETT. ([double dagger]) One child required a caudal
and a femoral block.

Table 2
Spectral Edge Frequency 90% and amplitude-integrated
electroencephalogram comparisons of forehead to parietal recordings
during anaesthesia

 aEEG maximum

Age group Mean difference, [micro]V P value

All children -16.9 <0.0001
<6 months -14.2 0.0001
6-12 months -17.5 0.001
1-2 y -19.9 0.0002
2-4 y -27.6 0.0006
4-8 y -6.6 0.5567
8-14 y -1.1 0.8868

 aEEG minimum

Age group Mean difference, [micro]V P value

All children -10.4 <0.0001
<6 months -6.9 0.0013
6-12 months -10.6 0.0003
1-2 y -11.1 0.0004
2-4 y -16.7 0.0003
4-8 y -6.5 0.2968
8-14 y -4.6 0.2098


Age group Mean difference, Hz P value

All children 2.0 <0.0001
<6 months -0.3 0.6336
6-12 months 2.5 0.0275
1-2 y 3.3 0.0001
2-4 y 2.7 0.0004
4-8 y 1.7 0.0646
8-14 y 2.8 0.0225

Students t-test differences are forehead minus parietal.
aEEG=amplitude-integrated electroencephalogram, SEF90=Spectral Edge
Frequency 90%.
COPYRIGHT 2012 Australian Society of Anaesthetists
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2012 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:McKeever, S.; Johnston, L.; Davidson, A.J.
Publication:Anaesthesia and Intensive Care
Article Type:Report
Geographic Code:4EUUK
Date:Mar 1, 2012
Previous Article:Pharmacokinetics of oxycodone after subcutaneous administration in a critically ill population compared with a healthy cohort.
Next Article:Performance of the PIRO score for predicting mortality in patients with ventilator-associated pneumonia.

Terms of use | Privacy policy | Copyright © 2018 Farlex, Inc. | Feedback | For webmasters