Amplitude-Integrated EEG and Its Potential Role in Augmenting Management Within the NICU




Abstract


Electroencephalography (EEG) or amplitude-integrated EEG (aEEG) provides important information on brain function in newborns. It can detect subclinical seizures and assess the effect of antiepileptic medication, and the background activity provides information on the severity of encephalopathy or can be used to monitor brain maturation in preterm infants. Continuous EEG or aEEG monitoring is now used routinely in an increasing number of neonatal intensive care units. However, many aEEG/EEG readers are not confident in their ability to interpret the aEEG/EEG. In this chapter we provide an overview of the literature and discuss how aEEG can be used in the neonatal intensive care unit to improve care in newborns.




Keywords

aEEG, artifacts, background pattern, HIE, newborn infants, preterm infants, seizures

 





  • aEEG is easy to apply and interpret at the bedside.



  • The aEEG background pattern is a reliable marker for encephalopathy in full-term infants and for brain maturation in preterm infants and can therefore be used for prognostication.



  • Electrographic seizures detected with the aEEG should always be confirmed on the real EEG.



  • Factors influencing the aEEG such as interelectrode spacing, medication, and common artifacts should be considered when interpreting the aEEG.



Interest in the neonatal brain has increased considerably during the past decades. This is in part due to better diagnostic methods in the acute and subacute stage. The presence and extent of structural lesions of the brain is provided by imaging techniques such as ultrasound and magnetic resonance imaging (MRI). Information about cerebral metabolism can also be obtained during the same examination using MR spectroscopy. Near-infrared spectroscopy (NIRS) allows noninvasive monitoring of brain oxygenation and cerebral hemodynamics.


Electroencephalography (EEG) or amplitude-integrated EEG (aEEG) provides information about brain function. It may detect epileptic discharges and signs of hypoxic-ischemic encephalopathy (HIE). Today aEEG is used routinely in an increasing number of neonatal intensive care units (NICUs). The extent of EEG monitoring in the NICU has been evaluated by analyzing 210 surveys (124 from Europe and 54 from the United States). Ninety percent of the respondents had access to either EEG or aEEG monitoring; 51% had both. The EEG was mainly interpreted by neurophysiologists (72%), whereas aEEG was usually interpreted by the neonatologist (80%). However, as many as 31% of the respondents reported that they were not confident in their ability to interpret aEEG/EEG.




Amplitude-Integrated EEG


Maynard originally constructed the cerebral function monitor (CFM) in the late 1960s for continuous monitoring. Prior developed the clinical application, mainly for adult patients during anesthesia and intensive care, after cardiac arrest, during status epilepticus, and after heart surgery.


The term aEEG is currently preferred to denote a method for encephalographic monitoring, whereas CFM is used to refer to a specific type of equipment. The EEG signal for the single-channel aEEG is usually recorded from one pair of parietally placed electrodes (corresponding to P3 and P4 according to the international EEG 10-20 classification, ground Fz). Two-channel EEG (F3-P3 and F4-P4 or C3-P3 and C4-P4, ground Fz according to the international EEG 10-20 classification) is now predominantly used and will provide information about hemispheric asymmetry, which may be especially helpful in children with a unilateral brain lesion. In the two-channel recording the F3-P3 and F4-P4 position is preferred for assessment of the background pattern, opposed to the short electrode distance of the C3-P3 and C4-P4 position, which will affect the background pattern but is better for seizure detection.


The signal is amplified and passed through an asymmetric band-pass filter that strongly prefers higher frequencies over lower ones and suppresses activity below 2 Hz and above 15 Hz to minimize artifacts from sources such as sweating, movement, muscle activity, and electrical interference. Additional processing includes rectification (negative waves become positive), smoothing, and considerable time compression. The signal is displayed on a semilogarithmic scale at slow speed (6 cm/hr) at the bedside. A second tracing continuously displays the original EEG from either one or two channels. The electrode impedance is continuously recorded but not necessarily displayed; there will be an alarm when the impedance is high, often as the result of a loose electrode. The bandwidth (BW) in the output reflects variations in minimum and maximum EEG amplitude, both of which depend on the maturity and severity of illness of the newborn. Because the semilogarithmic scale is used to plot the output, changes in background activity of very low amplitude (<5 μV) are enhanced.




Assessment of aEEG Background Pattern


The aEEG traces are assessed visually based on pattern recognition and classified into the following five categories in full-term infants :



  • 1.

    The continuous normal voltage (CNV) pattern is a continuous trace with a voltage between 10 and 25 (–50) μV ( Fig. 14.1A )




    Fig. 14.1


    A, Continuous normal voltage pattern with sleep-wake cycling. B, Discontinuous normal voltage pattern. C, Dense burst suppression. D, Sparse burst suppression pattern. E, Continuous low-voltage pattern. aEEG, Amplitude-integrated electroencephalogram; F, flat trace pattern.


  • 2.

    The discontinuous normal voltage (DNV) pattern is a discontinuous trace, in which the lower margin is predominantly below 5 μV (no burst suppression [BS]) ( Fig. 14.1B )


  • 3.

    The discontinuous background pattern (BS); periods of low amplitude (inactivity) intermixed with bursts of higher amplitude (usually >25 μV; BS) ( Fig. 14.1C and D )


  • 4.

    The continuous background pattern of very low voltage (around or below 5 μV) sometimes has bursts of higher (but <25 μV) amplitude (continuous low voltage [CLV]) ( Fig. 14.1E )


  • 5.

    Very low voltage, mainly inactive tracing with activity below 5 μV (flat trace [FT]) ( Fig. 14.1F )



Another classification according to al Naqeeb uses absolute values for background patterns:




  • Normal: upper margin greater than 10 μV; lower margin less than 5 μV



  • Moderately abnormal: upper margin greater than 10 μV; lower margin less than 5 μV



  • Severely abnormal: upper margin less than 10 μV; lower margin less than 5 μV



We prefer the pattern recognition criteria because the background pattern may be influenced by the so-called drift of the baseline ( Fig. 14.2 ). This drift is especially common in infants with very poor background activity, where the lower margin is lifted upward by a high-frequency external signal such as the ECG signal.




Fig. 14.2


The patient was born at full term via an emergency cesarean section. Sinusoidal cardiotocography was seen. Arterial umbilical pH was 6.70, and the first arterial lactate level was 30 mmol/L. Upper panel, drift of the baseline with seizures (indicated by ∗). Lower panel, real EEG shows electrocardiogram artifact. The loading dose of lidocaine was given at point C. CFM, Cerebral function monitor; EEG, electroencephalogram.


When these two aEEG scoring systems were compared in the same dataset containing comparable normothermia and hypothermia-treated infants, it was noted that the pattern recognition method was superior for early outcome prediction in a subgroup of patients with HIE. The appearance of the aEEG trace is influenced by several factors, including interference from the electrocardiogram, muscle activity, and interelectrode distance. Interobserver agreement was slightly lower using the voltage criteria compared with the pattern recognition method. However, both methods are equally good in determining the background pattern compared with the use of standard EEG. The voltage classification system is easier to use for clinicians with little experience in reading aEEG, but one should always try to assess the underlying pattern. It has been shown that a BS pattern may be read as a normal voltage pattern when a drift of the baseline is bringing the lower margin above 5 μV. When this artifact is not recognized, the background pattern may be misclassified and as a consequence hypothermia may not be offered to eligible infants, for example (see Fig. 14.2 ).




Comparison With Standard EEG


Background Pattern


Several studies investigating simultaneous use of aEEG and standard EEG have been performed to compare the two techniques. A good correlation between the aEEG background pattern and standard EEG background activity was seen in full-term infants with moderate to severe neonatal encephalopathy.


Prognostic Value of aEEG in HIE: Noncooled Situation


The value of the background pattern in the prediction of neurodevelopmental outcome in infants with HIE was already well established with the use of the standard EEG. A poor background pattern, which persists beyond the first 12 to 24 hours after birth (BS, low voltage, and FT) are well known to carry a poor prognosis. A more recent study by Murray et al. described the evolution of EEG changes after a hypoxic insult. They recorded continuous, multichannel, video EEG from 6 hours to 72 hours after delivery, and neurologic outcome was assessed at 24 months in 44 infants. Of those, 20 (45%) had an abnormal outcome. The best predictive ability was seen at 6 hours of age (area under the receiver operator characteristic curve: 0.958 (95% confidence interval [CI] 0.88-1.04; P .001). EEG features associated with an abnormal outcome were background amplitude less than 30 μV, interburst intervals (IBIs) of more than 30 seconds, electrographic seizures, and absence of sleep-wake cycling (SWC) at 48 hours after birth.


The prognostic value of early aEEG in HIE is described in the meta-analysis of 8 studies by Spitzmiller et al. A sensitivity of 91% (CI 87–95) and a negative likelihood ratio of 0.09 for aEEG tracings were found to accurately predict poor outcome. The relationship among aEEG amplitude measures, Sarnat grades, and MRI abnormality scores has also been reported. The relationship was strongest for the minimum amplitude measures in both hemispheres. A minimum amplitude of less than 4 μV was useful in predicting severe MRI abnormalities.


Both positive and negative predictive values were slightly lower when aEEG was assessed at 3 instead of 6 hours after birth, but they were still considered sufficiently high to use this technique for early selection in hypothermia or other intervention studies. Combining a neurologic examination with aEEG performed less than 12 hours after birth further increased predictive accuracy from 75% to 85%.


Recovery of the background pattern within 24 hours after perinatal asphyxia with a poor background activity (BS, FT, and CLV) has been reported in 20% of the cases. Of these infants, 60% survived with a mild disability or were normal at follow-up. The patients who did not recover either died in the neonatal period or survived with a severe disability.


Another way of looking at recovery of the background pattern is to assess the presence, quality, and time of onset of SWC (see also Figs. 14.1A, 14.8A , and 14.9C ). The presence, time of onset, and quality of SWC reflect the severity of the hypoxic-ischemic insult to which newborns have been exposed. The time of onset of SWC was shown to predict neurodevelopmental outcome based on whether SWC returns before 36 hours (good outcome) or after 36 hours (bad outcome). Therefore we recommend continuous monitoring for at least 48 hours or until a normal SWC pattern is established.


Prognostic Value of aEEG in HIE: Cooled Situation


Del Rio and colleagues recently performed a systematic review investigating and comparing the prognostic value of aEEG in cooled and noncooled infants with HIE. Seven studies have reported on the predictive value of aEEG in cooled infants ( Table 14.1 ). All found the predictive value, especially the specificity, of aEEG to be poor at 6 to 24 hours after birth. However, from 36 hours onward both the sensitivity and specificity were above 80% and comparable to the predictive value of aEEG in the normothermic situation.



Table 14.1

Studies Investigating the Predictive Value of aEEG in Hypothermia-Treated Infants





















































































































Study (Year of Publication) Normothermia
(n)
Hypothermia (n) Follow-Up
(mo)
Sensitivity, %
(95% CI)
Specificity, %
(95% CI)
Sensitivity, %
(95% CI)
Specificity, %
(95% CI)
Sensitivity, %
(95% CI)
Specificity, %
(95% CI)
Sensitivity, %
(95% CI)
Specificity, %
(95% CI)
6 hours 24 hours 36 hours 48 hours
Hallberg et al.
(2010)
0 23 12 100
(54–100)
31
(11–59)
100
(54–100
76
(50–93)
100
(54–100)
82
(57–96)
80
(28–99)
100
(80–100)
Thoresen et al.
(2010)
31 43 18 100
(80–100)
62
(41–80)
94
(71–100)
73
(52–88)
88
(64–99)
96
(80–100)
82
(57–96)
100
(87–100)
Ancora et al. (2013) 0 12 ≥12 100
(40–100)
50
(16–84)
75
(19–99)
25
(3–65)
NR NR NR NR
Shankaran et al. (2011) 51 57 18 100
(86–100)
30
(16–49)
NR NR NR NR NR NR
Gucuyener et al. (2012) 0 10 8–24 100
(16–100)
38
(9–76)
NR NR NR NR NR NR
Cseko et al. (2013) 0 70 18–24 100
(87–100)
40
(25–56)
95
(76–100)
74
(50–93)
95
(74–100)
83
(68–93)
82
(57–96)
93
(80–98)
Azzopardi (2014) 158 156 18 97
(89–100)
31
(21–42)
NR NR NR NR NR NR

aEEG, Amplitude-integrated electroencephalogram; CI, confidence interval; HT, hypothermia; NR, not reported; NT, normothermia.


The appearance of SWC in cooled infants with HIE has been addressed in several studies. Researchers found that the onset of SWC may be markedly delayed in term neonates with moderate to severe HIE treated with hypothermia, but when SWC returns within 36 hours the majority of infants will have a normal outcome. However, when SWC is never achieved, this predicts a poor outcome with a positive predictive value of 0.73. Therefore SWC is an important additional tool for assessing recovery in term infants with moderate to severe HIE treated with hypothermia.


Care should be taken when antiepileptic drugs (AEDs) are given for seizures in infants who have not recovered their background pattern within 24 to 48 hours. High blood levels of AED as the result of altered metabolism and accumulation under hypothermia may influence the background pattern of the aEEG.


aEEG and Seizures


Seizure Detection


A multichannel video EEG study by Murray et al. showed that only one-third of neonatal EEG seizures display clinical signs on simultaneous video recordings. Two-thirds of these clinical manifestations were not recognized or were misinterpreted by experienced neonatal staff with very low interobserver agreement. These findings show that clinical diagnosis is not sufficient for the recognition and management of neonatal seizures and underline the importance of EEG monitoring in infants at risk of developing seizures.


A rapid rise of both the lower and the upper margins of the aEEG tracing is suggestive of an ictal discharge ( Fig. 14.3B ). Seizures can be recognized as single seizures, repetitive seizures, and as status epilepticus (see also Fig. 14.10C ). The latter usually resembles a sawtooth pattern. Correct interpretation is greatly improved by simultaneous reading of the raw EEG, which is now available on most digital aEEG monitors (see Fig. 14.9A ).




Fig. 14.3


The patient was born at 40 weeks’ gestation and weighed 3150 g at birth. An emergency cesarean section was performed for suspected fetal compromise with bradycardia on the cardiotocogram because of a nuchal cord. Apgar scores were 1, 0, 0 at 1, 5, and 10 minutes. The infant was resuscitated for 15 minutes with intravenous adrenalin given once. The umbilical cord pH was 7.25, and base excess was –3.5. the first arterial lactate concentration was 26.4 mmol/L. The infant was cooled for 72 hours at 33.5°C. Seizures were treated with phenobarbital, midazolam, and lidocaine. Magnetic resonance imaging on day 4 showed severe abnormalities in the basal ganglia and thalami. The infant died on day 5 after redirection of care. A, Cooling was started at 3 hours after birth. B, Amplitude-integrated electroencephalogram (aEEG) at 18 hours after birth during cooling shows a flat trace pattern with seizures (indicated by the pink bars ). 10, care; 11, x-ray; 12 + 13, midazolam is given. C, A sparse burst suppression pattern is seen 36 hours after birth (during cooling). D, A dense burst suppression is seen 48 hours after birth. E, A discontinuous normal voltage pattern is seen 72 hours after birth.


Multichannel video EEG is the gold standard for neonatal seizure detection, but it is not always readily available or feasible in the NICU. The advantages of limited channel aEEG compared with multichannel EEG are the easy application and interpretation that can be done in real time by NICU personnel. This can reduce the time to diagnosis and treatment of seizures significantly. However, owing to the nature of the aEEG technique it is not surprising that very brief seizure activity as well as focal seizure activity may be missed. This was also shown by Shellhaas et al. in a large dataset of 125 multichannel EEGs with 851 neonatal seizures. They found that 94% of the multichannel EEGs detected one or more seizures on the C3–C4 channel, which is often used for aEEG. Thus multichannel EEG remains the gold standard for quantification of seizure burden. Rakshasbhuvankar and colleagues recently reviewed 10 studies to answer the question of whether aEEG is as accurate as multichannel EEG in the detection of seizures.


Detection of individual seizures using aEEG was difficult (12%–38%) without access to the real EEG, especially when the seizures were infrequent, brief, or of low amplitude. There were no false-positive findings among control records. Infants with focal seizures, however, usually develop more widespread ictal discharges, which will be identified by limited channel aEEG. In addition, 81% of the neonatal seizures originated from central temporal or midline vertex electrodes, which can potentially be picked up by the aEEG electrodes. An important study is the one from Shah et al., showing that the combination of two-channel aEEG with the real EEG signal detected the majority (76%) of electrical seizures in at-risk newborns. Use of aEEG in combination with the real EEG signal was clearly better in seizure detection than use of aEEG alone (27%–44%). In a review by Evans and colleagues, 44 studies with aEEG and simultaneous multichannel EEG were analyzed. Sensitivity for the presence of seizures by aEEG was 80% and specificity was 50%. Several seizures were overdiagnosed by aEEG as well as by standard EEG (63% vs. 45.5%, respectively). The specificity of aEEG for seizure detection was higher in neonates undergoing EEG for suspected seizures. On the other hand, monitoring for seizures with limited channel aEEG (two channels with access to the real EEG) can be accurately interpreted, compares favorably to multichannel EEG, and is associated with a trend toward reduced seizure burden. It is important to use at least two channels, especially in infants with suspected unilateral brain lesions.


Sometimes the aEEG shows a seizure pattern, but the two-channel real EEG is not conclusive. This could be due to multifocal epileptiform activity. The only way to check this is to perform a multichannel EEG ( Fig. 14.4 ).




Fig. 14.4


Labor was induced at 42 weeks gestational age. The infant’s birth weight was 4100 g. There was shoulder dystocia during labor. Apgar scores were 2 and 5 after 1 and 5 minutes, respectively. The infant was resuscitated for 4 minutes. Umbilical pH was 6.98 and base excess was –18. The infant was not cooled but started to have seizures within 12 hours after birth. He was treated initially with phenobarbital and was transferred to the neonatal intensive care unit. He subsequently needed ventilatory support and was treated with phenobarbital, lidocaine, and midazolam. Cranial ultrasound performed within 24 hours after birth showed diffuse echogenicities in the subcortical white matter and basal ganglia. Magnetic resonance imaging showed extensive cortical gray matter and subcortical white matter abnormalities in the entire left hemisphere and in large areas of the right hemisphere together with the thalami. A, Amplitude-integrated electroencephalogram at 25 hours after birth was performed for suspected seizures, but findings from the two-channel aEEG and real electroencephalogram (B) were not conclusive. C, Multichannel electroencephalogram showed epileptic activity over the vertex (red arrows) . D, Axial T2-weighted image. E, Axial diffusion-weighted image.


Since the increased use of continuous monitoring, it has become apparent that subclinical seizures are common and occur especially following administration of the first AED. This so-called uncoupling or electroclinical dissociation has been reported by several groups and was found in 50% to 60% of the children studied. The aEEG can play an important role in the detection of these subclinical seizures.


Even status epilepticus is not uncommon and occurred in 18% of 56 full-term infants admitted with neonatal seizures recorded with aEEG. The duration of status epilepticus may influence prognosis as well. In a group of 48 infants with HIE and aEEG-detected status epilepticus, there was a significant difference in background pattern, as well as in duration of the status epilepticus between infants with a poor outcome, compared with those with a good outcome. The background pattern at the onset of status epilepticus appeared to be the main predictor of outcome in all neonates with status epilepticus. The background pattern also proved to be an independent predictor of seizures in a group of 90 infants with HIE treated with therapeutic hypothermia. The incidence of seizures did not change after the introduction of therapeutic hypothermia, but the overall seizure burden was reduced. However, status epilepticus is not uncommon in infants with HIE treated with hypothermia; status epilepticus was observed in 10% to 23% of infants. A high seizure burden and status epilepticus have been related to more severe brain injury on MRI in hypothermia-treated infants.


Should We Treat Subclinical Seizures?


There is no consensus whether clinical events without an EEG correlate should be treated or how aggressively to treat electrographic-only seizures. Although human data are scarce, several studies do suggest an adverse effect of both clinical and subclinical seizures on neurodevelopmental outcome. Neonatal seizures have been reported to predispose patients to later problems with regard to cognition, behavior, and development of postneonatal epilepsy. Two previous aEEG studies have shown that infants treated for both clinical and subclinical seizures had a lower incidence of postneonatal epilepsy (8%–9%) compared with those treated only for clinical seizures (20%–50%). It was of interest that in the study by Brunquell the infants with subtle seizures and generalized tonic seizures had a significantly higher prevalence of postneonatal epilepsy ( P = .04 and P = .01, respectively), cognitive impairment ( P = .02; P = .007), and cerebral palsy ( P = .03; P = .002) compared with patients with other seizure types. Subtle seizures were, in addition, more likely to be associated with abnormalities on neurologic examination at follow-up ( P = .03). Prolonged seizures can increase brain temperature and thus increase metabolic demands. Prolonged seizures cause progressive cerebral hypoxia and increase local cerebral blood flow and may steal perfusion from injured brain regions. In a study by Miller et al. of term newborns with HIE, brain injury was independently associated with the severity of seizures. They performed MRI and proton magnetic resonance spectroscopy (MRS) in 90 full-term infants. EEG-confirmed seizures developed in 33 (37%), and the seizures were scored based on frequency and severity, EEG findings, and AED use. Multivariable linear regression tested the independent association of seizure severity with impaired cerebral metabolism measured by lactate/choline and compromised neuronal integrity measured by N -acetylaspartate/choline in the basal nuclei and the intervascular boundary zones. Seizure severity was associated with increased lactate/choline in both the intervascular boundary zone ( P < .001) and the basal nuclei ( P = .011) when controlling for potential confounders of MRI abnormalities and extent of resuscitation at birth. Seizure severity was independently associated with diminished N -acetylaspartate/choline in the intervascular boundary zone ( P = .034).


The same group was able to show an independent effect of clinical neonatal seizures and their treatment on neurodevelopment in 77 children who were born at term and were at risk for hypoxic-ischemic brain injury, and had brain MRI performed in the newborn period. About one-third of the children (25/77) had clinically detected neonatal seizures. Neonatal MRIs were classified for anatomic distribution and severity of acute injury. The severity of brain injury was assessed using high-resolution newborn MRI, and outcome was assessed at age 4 years, using the Full-Scale Intelligence Quotient (FSIQ) of the Wechsler Preschool and Primary Scale of Intelligence–Revised, as well as a neuromotor score. After controlling for severity of injury on MRI, the children with neonatal seizures had worse motor and cognitive outcomes compared with those without seizures. The children with severe seizures had a lower FSIQ than those with mild or moderate seizures ( P < .001). The major limitation of these two important studies is the reliance on clinical evaluation for classification of seizure diagnosis and severity.


In the randomized controlled trial of van Rooij et al. the seizure burden was very high in both groups (treatment of subclinical seizures vs. treatment of clinical seizures only), but the burden was higher in the treatment of clinical seizures only group. It was of interest to see that there was a significant correlation between the duration of seizure patterns and the severity of brain injury on MRI in the blinded group, but not in the group treated for clinical as well as subclinical seizures. More recently, another randomized controlled trial showed that infants treated for electrographic seizures alone had fewer seizures, a lower seizure burden. and a shorter time to treatment than infants treated for clinical seizures only. These authors also confirmed that seizure burden was associated with brain injury scores and showed in addition that high seizure burden was associated with poorer outcome at 18 to 24 months.


Adequate and fast detection of electrographic seizures is important to reduction of the seizure burden. However, it is difficult to constantly monitor real-time EEG. On some digital aEEG machines seizure detection algorithms are available, which may help in detecting seizures. More recently, attempts have been made to develop neonatal seizure detection algorithms for multichannel EEG. The performance of this seizure detection algorithm was not altered by the presence of a depressed EEG background activity caused by phenobarbital administration.


aEEG in Preterm Infants


In parallel with multichannel EEG, aEEG background activity is more discontinuous in preterm infants. Normative values for aEEG background activity at different gestational ages have been published. A scoring system for evaluation of brain maturation in preterm infants has also been developed. Zhang et al. described reference values for aEEG amplitude obtained for 274 infants with a wide range of postmenstrual ages (PMAs) (30–55 weeks). The normative amplitudes of aEEG margins, especially of the lower margin in quiet sleep, were recommended as a source of reference data for the identification of potentially abnormal aEEG results. The upper and lower margins of the aEEGs in both active and quiet sleep clearly rose after the neonatal period. The BW, defined as the graphic distance between the upper and lower margins, decreased almost monotonically throughout the PMA range from 30 to 55 weeks. The lower margin of the aEEG was positively correlated with PMA, with a larger rank correlation coefficient during quiet sleep (r = 0.89) than during active sleep (r = 0.49).


Studies with automated quantification of aEEG characteristics in premature infants by use of digital equipment have also been reported. Niemarkt et al. describe a method where the upper margin amplitude (UMA), lower margin amplitude (LMA), and BW were quantitatively calculated using a special software system. In addition, the relative duration of discontinuous background pattern (discontinuous background defined as activity with LMA <5 μV, expressed as a percentage) was calculated. Analyses of the first-week recordings demonstrated a strong positive correlation between gestational age (GA) and LMA, while the percentage of discontinuous pattern decreased significantly. Longitudinally, all infants showed an increase of LMA. They found that GA and postnatal age (PA) both contributed independently and equally to LMA and the percentage of discontinuous pattern. They also found a strong correlation between postmenstrual age (GA + PA) and LMA and the percentage of discontinuous pattern, respectively. They conclude that LMA and percentage of discontinuous pattern are simple quantitative measures of neurophysiologic development and may be used to evaluate neurodevelopment in infants.


Other groups studied IBI duration or burst duration as a measure of discontinuity (or maturation). Palmu et al. described the characteristics of activity bursts in the early preterm EEG to assess interrater agreement of burst detection by visual inspection, and to determine the performance of an automated burst detector that uses a nonlinear energy operator (NLEO). They conclude that visual detection of bursts from the early preterm EEG is comparable, albeit not identical, between raters. The original automated detector underestimates the amount of burst occurrence but can be readily improved to yield results comparable to visual detection. More recently Koolen et al. developed an automated burst detection method based on line length, which had a sensitivity and specificity above 84 and even performed well with a limited number of channels. The same group also developed a quantitative measure of discontinuity called the suppression curve, which proved to be a reliable measure of preterm brain maturation. Further clinical studies are warranted to assess the optimal descriptors of burst detection for monitoring and prognostication. Validation of a burst detector may offer an evidence-based platform for further development of brain monitors in very preterm babies.


SWC can be clearly identified in the aEEG from around 30 weeks’ gestation, but also at 25 to 26 weeks GA a cyclical pattern resembling SWC can be seen in stable infants ( Fig. 14.5 ). Effects from common medications (e.g., surfactant, morphine, and diazepam) and elevated carbon dioxide blood levels can be readily seen as a deterioration of the aEEG background pattern in preterm infants.


Jun 25, 2019 | Posted by in NEUROLOGY | Comments Off on Amplitude-Integrated EEG and Its Potential Role in Augmenting Management Within the NICU

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