Chapter 14: Amplitude-integrated EEG and its potential role in improving neonatal care within the NICU

Lauren C. Weeke, Maria Luisa Tataranno, Mohamed El-Dib

Case description (Fig. 14.1)

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, respectively. 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 (MRI) on day 4 showed severe abnormalities in the basal ganglia and thalami. The infant died on day 5 after redirection of care.

Fig. 14.1A, 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, The raw EEG showing a typical ictal discharge with evolution in amplitude and frequency. D, A sparse burst suppression pattern is seen 36 hours after birth (during cooling). E, A dense burst suppression is seen 48 hours after birth. F, A discontinuous normal voltage pattern is seen 72 hours after birth.

Key points

• aEEG is easy to apply and interpret at the cotside.
• The aEEG background pattern is a reliable marker for encephalopathy in full-term infants and for brain maturation in preterm infants and can guide prognostication.
• Electrographic seizures detected with the aEEG compressed tracing should always be confirmed on the raw EEG, with two channels having greater reliability than single channel.
• Factors influencing the aEEG such as interelectrode spacing, medications, and common artifacts should be considered when interpreting the aEEG.

Introduction

Interest in the neonatal brain has increased considerably throughout the past decades. Imaging techniques such as ultrasound and magnetic resonance imaging (MRI) can evaluate the presence and extent of structural lesions of the brain. Near-infrared spectroscopy (NIRS) allows noninvasive monitoring of brain oxygenation and cerebral hemodynamics. Conventional multichannel electroencephalography (will be referred to as cEEG) and amplitude-integrated EEG (aEEG) provide information about brain function. EEG may detect epileptic discharges, reflect encephalopathy, from common etiologies such as hypoxic-ischemic encephalopathy (HIE, as illustrated in Fig. 14.1), and may give valuable information on brain maturation. Today, aEEG is used routinely in an increasing number of neonatal intensive care units (NICUs) due to its ease of use at the cotside. 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 respondents had access to either cEEG or aEEG monitoring; 51% had both. The cEEG 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/cEEG.¹

Amplitude-integrated EEG

Maynard originally constructed the cerebral function monitor (CFM) in the late 1960s for continuous brain monitoring. Prior developed the clinical application for monitoring adult patients during anesthesia and intensive care, after cardiac arrest, during status epilepticus, or after heart surgery.²

The term aEEG is currently preferred to denote a method for encephalographic monitoring, whereas CFM refers to a specific type of equipment. The EEG signal for the single-channel aEEG is usually recorded from a pair of electrodes placed over the parietal lobes (corresponding to P3 and P4 according to the international EEG 10–20 classification, ground Fz, Fig. 14.2A). Two-channel EEG places frontal-parietal or central-parietal leads (F3-P3 and F4-P4 or C3-P3 and C4-P4, ground Fz according to the international EEG 10–20 classification, Fig. 14.2B and C) and is now predominantly used. This provides additional information about hemispheric asymmetry, which may be especially helpful in children with unilateral brain lesions.³ In the two-channel recording, the F3-P3 and F4-P4 position is preferred for assessment of the background pattern. This arrangement is in opposition to the short electrode distance of the C3-P3 and C4-P4 positions, which is better for seizure detection but may alter the background pattern.⁴

Fig. 14.2Examples of electrode position in single channel aEEG using P3 and P4 (A) atypical two channel with F3-P3/F4-P4 (B) and more commonly used two channel with C3-P3/C4-P4 (C)

For aEEG processing, the raw EEG signal is amplified and passed through an asymmetric band-pass filter that prefers higher frequencies over lower ones and suppresses activity below two Hz and above 15 Hz to minimize artifacts from 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 cotside. A second tracing continuously displays the original or raw EEG from either one or two channels. The electrode impedance is continuously recorded but not necessarily displayed; there is an alarm when the impedance is high, often as a 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.⁵

The aEEG traces are assessed visually based on pattern recognition and are classified into the following five categories in full-term infants⁶:

1. The continuous normal voltage (CNV) pattern is a continuous trace with a voltage between 10 and 25 (–50) μV (Fig. 14.3A).
2. The discontinuous normal voltage (DNV) pattern in which the lower margin is predominantly below 5 μV (no burst suppression [BS]) (Fig. 14.3B).
3. The BS pattern with periods of low amplitude (inactivity) intermixed with bursts of higher amplitude (usually >25 μV). According to spacing between bursts, tracing can be classified either as sparse or dense BS pattern (Fig. 14.3C and D).
4. The continuous low voltage pattern (CLV) of very low voltage (around or below 5 μV) (Fig. 14.3E).
5. The flat tracing (FT) which has very low voltage, mainly inactive tracing with activity below 5 μV (Fig. 14.3F).

Fig. 14.3A, 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. F, Flat trace pattern. *aEEG*, Amplitude-integrated electroencephalogram.

Another classification according to al Naqeeb ⁷ uses absolute values for background patterns in term infants:

• Normal: upper margin greater than 10 μV; lower margin above 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 a baseline drift (Fig. 14.4). 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.⁸ When these two aEEG scoring systems were compared in the same dataset containing comparable normothermia and hypothermia-treated infants,⁹ it was noted that the pattern recognition method was superior for early outcome prediction in a subgroup of patients with HIE. Interobserver agreement was slightly higher 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 cEEG.¹⁰ 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.¹¹ When this artifact is not recognized, the background pattern may be misclassified and consequently hypothermia may not be offered to eligible infants (see Fig. 14.4).

Fig. 14.4The 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, raw EEG shows electrocardiogram artifact. The loading dose of lidocaine was given at point *C. CFM*, Cerebral function monitor; *EEG*, electroencephalogram. *Source:* (Figure from first book publication by Mona Toet and Linda de Vries. Fig. 2 in the previous version from 2016.)

Comparison with cEEG

Several studies investigating simultaneous use of aEEG and cEEG have been performed to compare the two techniques for background pattern recognition. A good correlation between the aEEG background pattern and cEEG background activity was seen in full-term infants with moderate to severe neonatal encephalopathy.12–15

Prognostic value of aEEG in HIE prior to the ERA of therapeutic hypothermia

The value of the background pattern in the prediction of neurodevelopmental outcome in term infants with HIE has been well established with the use of the cEEG. A poor background pattern, which persists beyond the first 12 to 24 hours after birth (BS, low voltage, and FT), is well known to carry a poor prognosis. The best predictive ability was seen at 6 hours of age. cEEG features associated with an abnormal outcome included a 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 eight studies by Spitzmiller et al.¹⁶ A minimum amplitude of less than 4 μV was useful in predicting severe MRI abnormalities.¹⁷ Both positive and negative predictive values were slightly lower when aEEG was assessed at three 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%.¹⁸ A significant correlation was described between clinical examination, by using the Thompson score, and the aEEG background pattern. They have similar predictive values for adverse outcome. However, the aEEG has several advantages over the Thompson score as it is a continuous measurement which can identify deteriorations in neurological status, identify electrographic-only seizures, and can be sent out for expert review.¹⁹

In one study, recovery of poor background activity (BS, FT, and CLV) within 24 hours after perinatal asphyxia has been reported in 20% of the cases.²⁰ 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. 14.3A, and 14.10C). The time of onset of SWC was shown to predict neurodevelopmental outcome in infants with HIE based on whether SWC returns before 36 hours (good outcome) or after 36 hours (poor outcome).²¹

Prognostic value of aEEG in hie in the era of therapeutic hypothermia

Del Rio and colleagues ²² 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).⁹^,^23–28 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.²² (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.⁸ (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.²³ (2013)	0	12	≥12	100 (40–100)	50 (16–84)	75 (19–99)	25 (3–65)	NR	NR	NR	NR
Shankaran et al.²⁴ (2011)	51	57	18	100 (86–100)	30 (16–49)	NR	NR	NR	NR	NR	NR
Gucuyener et al.²⁵ (2012)	0	10	8–24	100 (16–100)	38 (9–76)	NR	NR	NR	NR	NR	NR
Cseko et al.²⁶ (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)
Azzopardi²⁷ (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 positive predictive value (PPV) of aEEG changes over the course of hypothermia. It is well known that the aEEG background may gradually improve over the first 48 to 72 hours of cooling.⁹^,²³^,²⁹ The PPV of an abnormal aEEG in cooled infants increases from 66% at 24 hours to 85% at 48 hours and 89% at 72 hours.³⁰ These findings along with the findings of Sewell et al.,³¹ who showed a better predictive value using a background evolution pattern compared to defining the background at specific timepoints, highlight the importance of monitoring infants throughout the duration of cooling and rewarming to inform the trajectory of recovery, or absence of recovery, over time.

The appearance of SWC in cooled infants with HIE has been addressed in several studies.⁹^,²⁷^,³² Researchers found that the onset of SWC may be markedly delayed in term infants with moderate to severe HIE treated with hypothermia, but when SWC returns within 36 hours, the majority of infants will have a normal outcome.²⁷^,³² However, when SWC is never achieved, this predicts a poor outcome with a PPV of 0.73.⁹^,²⁷ 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 antiseizure medication (ASM) is given in infants who have not recovered their background pattern within 24 to 48 hours. High blood levels of ASM, as a result of altered metabolism and accumulation under hypothermia, may influence the background pattern of the aEEG.

New quantitative EEG measures of delta power and discontinuity are being developed which can aid real-time prognostication in infants with HIE as well.³³^,³⁴

aEEG and seizures

Seizure detection

A multichannel video cEEG study by Murray et al.³⁵ 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.³⁶ 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. 14.1B). Seizures can be recognized as single seizures, repetitive seizures, and status epilepticus (see also Fig. 14.11C). The latter usually resembles a sawtooth pattern. Correct interpretation of aEEG is greatly improved by simultaneous reading of the raw EEG, which is now available on all modern digital aEEG monitors (see Fig. 14.10A).

Multichannel video cEEG is the gold standard for neonatal seizure detection,³⁷ but it is not always readily available or feasible in the NICU. The advantages of limited channel aEEG compared with cEEG are the easy application and interpretation that can be done in real time by NICU personnel. This can significantly reduce the time to diagnosis and treatment of seizures.³⁸ However, owing to the nature of the aEEG technique, it is not surprising that very brief seizure activity and focal seizures may be missed.³⁹ Thus, cEEG remains the gold standard for quantification of seizure burden. 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 originate from central temporal or midline vertex electrodes, which can potentially be picked up by the aEEG electrodes.¹² A recent systematic review by Falsaperla et al. investigating the sensitivity of aEEG in neonatal seizure detection (14 studies included) showed an overall sensitivity varying between 31% and 90% (median 56.8%). When stratified for aEEG technique, the sensitivity for single-channel aEEG was 52.3% (range 29.7%–78%), for two-channel aEEG, it was 58.5% (range 37.5%–90%) and when two-channel aEEG was combined with analysis of the raw EEG, the sensitivity was 80.8% (range 76%–85.6%).⁴⁰ This underlines the importance of using at least two channels in combination with the raw EEG, especially in infants with suspected unilateral brain lesions.³

It has been noted that the aEEG can show a pattern consistent with a seizure, but the two-channel raw EEG is inconclusive. This could be due to multifocal epileptiform activity, which can only be confirmed on a cEEG (Fig. 14.5).

Fig. 14.5Labor 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 raw 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 clear that electrographic-only seizures are common and occur especially following administration of the first ASM. 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 may play an important role in the detection of these electrographic-only seizures.⁴¹^,⁴²

Importantly it has been recognized that status epilepticus is not uncommon occurring in 18% of 56 full-term infants admitted with neonatal seizures recorded with aEEG.⁴³^,⁴⁴ The background pattern at the onset of status epilepticus appears to be the main predictor of outcome in all infants with status epilepticus. The background pattern also proved to be an independent predictor of seizures in infants with HIE treated with therapeutic hypothermia.⁴⁵ The incidence of seizures did not change after the introduction of therapeutic hypothermia, but the overall seizure burden was reduced.⁴⁶ However, status epilepticus is not uncommon in infants with HIE treated with hypothermia (10%–23%).^47–49 A high seizure burden and status epilepticus have been related to more severe brain injury on MRI and postneonatal epilepsy in hypothermia-treated infants.⁴⁷^,⁴⁸^,^50–54 While it is highly recommended to monitor infants with HIE throughout cooling and rewarming, a recent study by Benedetti et al. showed that infants with normal or mildly abnormal background pattern developed seizures during the first 24 hours or not at all, while infants who developed seizures after 24 hours had markedly abnormal background patterns. In limited resource settings, this could guide tailored duration of monitoring.⁵⁵

Should we treat electrographic-only seizures?

There is no consensus on whether clinical events without an EEG correlate should be treated or how aggressively to treat electrographic-only seizures.⁵⁶^,⁵⁷ Although human data are scarce, several studies do suggest an adverse effect of both clinical and electrographic-only 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.^58–60 Two previous aEEG studies have shown that infants treated for both clinical and electrographic-only seizures had a lower incidence of postneonatal epilepsy (8%–9%) compared with those treated only for clinical seizures (20%–50%).^61–64 Prolonged seizures can increase brain temperature and thus increase metabolic demands.⁶⁵ Moreover, prolonged seizures cause progressive cerebral hypoxia, increase local cerebral blood flow, and may steal perfusion from injured brain regions.⁶⁶ In a study by Miller et al. on term newborns with HIE, brain injury was independently associated with the severity of seizures.⁵⁹ They performed MRI and proton magnetic resonance spectroscopy (MRS) in 90 full-term infants. 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 the extent of resuscitation at birth. Seizure severity was independently associated with diminished N-acetylaspartate/choline in the intervascular boundary zone (P =.034).

In the randomized controlled trial of van Rooij et al.,⁶⁷ the seizure burden was very high in both groups (treated for electrographic-only seizures vs. clinical seizures only), but the burden was higher in the treatment of the clinical seizures only group. It was interesting 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, which was not present in the nonblinded group. Another randomized controlled trial showed that infants treated for electrographic-only seizures 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 more severe brain injury and showed that high seizure burden was associated with poorer outcome at 18 to 24 months.⁵⁰ Recently, a study by Hunt et al. showed no difference in outcome, seizure burden, MRI injury, death, or disability between electrographic-only and clinically treated seizures in a heterogenous group of infants. Moreover, there was a concern about cognitive outcomes for those treated for electrographic seizures. Limitations of this study include the heterogenicity of its population, possible difference in seizure burden (though not statistically different), unclear use of raw EEG or seizure detection algorithms, and that both groups received equal ASMs. This study, as in previous studies, was underpowered, making it difficult to draw conclusions.⁶⁸

Adequate and fast detection of electrographic seizures is important to reduce 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.⁶⁹ More recently, a neonatal seizure detection algorithm for cEEG was tested in a multicenter randomized controlled trial.⁷⁰^,⁷¹ Although the algorithm did not enhance identification of individual infants with seizures, the percentage of correctly identified seizure hours was higher in the algorithm group. The authors concluded that the benefit of the algorithm might be greater in less experienced centers.

aEEG in preterm infants

In parallel with cEEG, aEEG background activity is more discontinuous in preterm infants. In relatively healthy and stable premature infants, the maturation of the aEEG background pattern is dependent on both gestational age (GA) and postmenstrual age (PMA). The greater the GA, the more mature the aEEG background pattern is at birth, and subsequently the higher the maturation rate will be.⁷² Moreover, preterm infants of lower GA display a relatively faster aEEG maturation compared with others of higher GA at the same PMA.⁷³ Normative values for aEEG background activity at different GA have been published.⁷⁴ A scoring system for evaluation of brain maturation in preterm infants has also been developed.⁷⁵ Zhang et al. described reference values for aEEG amplitude obtained for 274 infants with a wide range of PMA (30–55 weeks).⁷⁶

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 aEEG in both active and quiet sleep clearly rose after the neonatal period. The BW 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).

The aEEG in preterm infants develops with three major trends through to full-term age: increase in continuity, with defined decreasing periods of normal EEG suppression for specific GA, appearance of several normal and specific transient waveforms of prematurity, and the appearance of SWC. Interpretation of background pattern and SWC maturational patterns may give useful information on brain maturity and eventually also the severity of brain injury, thus it may be useful also for prognosis.^77–79 Currently, nonexpert readers still struggle to interpret the patterns produced and to distinguish abnormal from normal aEEG/EEG pattern. For this reason, in the last years, there has been an attempt to use quantitative measures for the analysis of neonatal EEG in order to assess neonatal brain function.⁸⁰ New aEEG/EEG devices already implemented some real-time quantitative algorithms such as the IBI, which indicates the grade of EEG suppression, helping the clinical interpretation at the cotside.

Niemarkt et al. described a method where the upper margin amplitude (UMA), lower margin amplitude (LMA), and BW were quantitatively calculated using a special software system.⁸¹ In addition, the relative duration of discontinuous background pattern (discontinuous background defined as activity with LMA <5 μV, expressed as a percentage) was calculated. They found that GA and postnatal age both contributed independently and equally to LMA and the percentage of discontinuous pattern. Both GA and PMA correlated positively with LMA and negatively with percentage of discontinuous pattern. They concluded 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).⁸² They concluded 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 0.84 and even performed well with a limited number of channels.⁸³ 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.⁸⁴ 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 GA or postnatal age (PNA), but a cyclical pattern resembling immature SWC can also be seen in stable infants born as early as 25 to 26 weeks GA. As expected, SWC matures with GA and with PNA; using an SWC scoring system, a more mature SWC at 34 weeks PMA was associated with improved neurodevelopmental outcome in preterm infants.⁸⁵

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.^86–90

Early prediction of outcome based on aEEG is a more complicated issue in preterm infants than in full-term infants. A predominantly discontinuous background pattern can be considered normal in most infants younger than 30 weeks GA. In the most immature infants, factors other than initial brain function may influence long-term neurodevelopmental outcome (e.g., bronchopulmonary dysplasia and late-onset sepsis), which makes prediction of outcome from the early EEG less certain. Several studies have investigated the prognostic value of early aEEG in preterm infants. Klebermass et al. showed that an aEEG-pattern score (evaluating the background pattern, SWC, and presence of seizure activity) was highly predictive of adverse outcome at 3 years (defined as death, Bayley-II <85, CP, and neurosensory impairment).⁷⁹ A recent study investigated whether there was an association between early postnatal EEG and neurocognitive outcomes at 10 to 12 years of age in extremely preterm born infants. They found a significantly lower absolute band power in all frequency bands in infants with unfavorable outcome on the full intelligence quotient, adaptive behavior composite score, and global executive composite score (P <.05).⁹¹ However, West et al. showed that the neurophysiologist’s assessment of two-channel EEG in infants <29 weeks GA within 48 hours after delivery was a better predictor of adverse outcome than quantitative continuity measures defined as percentage of time above 25 μV. Moreover, all infants with definite seizures identified by the neurophysiologist had poor outcomes.⁹²

Several cEEG and aEEG studies have shown correlation between early background depression and the severity of a periventricular-intraventricular hemorrhage.⁹³ aEEG is a useful tool for the evaluation of preterm infants with progressive posthemorrhagic ventricular dilatation (PHVD). In a study by Klebermass-Schrehof and colleagues, the aEEG background pattern of 17 preterm infants with PHVD showed increased suppression in 13 patients (76%) with increasing ventricular dilatation. The changes in aEEG background patterns were detected before clinical signs of elevated intracranial pressure occurred and aEEG showed normalization within a week of successful therapeutic intervention.⁹⁴

aEEG has also been related to illness severity (using the Score for Neonatal Acute Physiology II [SNAP-II]) in preterm infants.⁹⁵ They found that severity of illness as measured by the SNAP-II and low blood pressure had a negative effect on the aEEGs of preterm infants (n = 38, mean GA 29+7 weeks [range 26+0–31+8 weeks]). These findings were confirmed by two recent studies relating the SNAP-II and hemodynamic parameters to the Burdjalov score and aEEG amplitudes.⁷⁷^,⁹⁶

Epileptic seizure activity in preterm infants can be identified in a manner similar to that used in full-term infants. Identifying seizure activity on a discontinuous background pattern can be very difficult. With access to the raw EEG on the digital devices, this problem can be handled more easily, especially with the seizure detection algorithm available on some devices (Fig. 14.6). However, care should be taken to distinguish ictal discharges from artifacts.⁹⁷