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Lowered Electroencephalographic Spectral Edge Frequency Predicts the Presence of Cerebral White Matter Injury in Premature Infants

Terrie E. Inder, MD, FRACP, MBChB^*,, Liz Buckland, Christopher E. Williams, PhD^||, Carole Spencer, Mark I. Gunning, MSc^||, Brian A. Darlow, MD, FRACP, Joseph J. Volpe, MD, PhD and Peter D. Gluckman, PhD, FRACP, MBChB^||

^* Murdoch Children’s Research Institute and Howard Florey Institute and Royal Women’s and Royal Children’s Hospital, Melbourne, Australia
Department of Pediatrics, Christchurch Hospital and School of Medicine, University of Otago, Christchurch, New Zealand
Department of Neurology, Children’s Hospital and Harvard Medical School, Boston, Massachusetts
^|| Liggins Institute, University of Auckland, Auckland, New Zealand

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	ABSTRACT

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Objective. Current methods for early identification of cerebral white matter injury in the premature infant at the bedside are inadequate. This study investigated the utility of advanced spectral analysis of the neonatal electroencephalogram (EEG) in the early diagnosis of white matter injury in the premature infant. The critical measurement used, suggested largely by previous studies in animal models, was the spectral edge frequency (SEF), calculated here as the frequency below which 90% of the power in the EEG exists.

Methods. Fifty-nine very low birth weight infants (87% of eligible infants) had electrodes placed over the central and parietal regions (C3, P3, C4, and P4 sites according to the 10-20 international system) for the collection of EEG amplitude, intensity, and SEF. All averaged signals were analyzed off-line using software (Chart Analyzer; BrainZ Instruments, Auckland, NZ). All infants had a magnetic resonance imaging scan at term to identify the presence and severity of white matter injury.

Results. There was no significant difference between conventional EEG amplitude and intensity for infants with or without evidence of white matter injury. However, premature infants with increasingly severe white matter injury had progressively lower SEFs compared with infants who did not exhibit white matter injury.

Conclusions. These data suggest that SEF-based measures are useful for defining the presence and severity of white matter injury at the bedside.

Key Words: white matter injury • prematurity • very low birth weight infant • electroencephalography • spectral analysis

Abbreviations: EEG, electroencephalogram • a-EEG, amplitude-integrated electroencephalogram • CFM, cerebral function monitor • SEF, spectral edge frequency • GA, gestational age • MRI, magnetic resonance imaging • ANOVA, analysis of variance

	INTRODUCTION

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Early identification of cerebral white matter injury in the premature infant at the bedside is imperfect. An ideal diagnostic tool for the detection of white matter injury should be able to detect injury early, at the bedside of the sick vulnerable infant, with quantitative technology allowing a high reproducibility that can be applied and interpreted easily by the neonatal clinical team. The most widely used method for the detection of cerebral white matter injury, cranial ultrasonography, has been shown in correlative neuropathological studies to fail to detect as much as 70% of apparent hypoxic-ischemic injury, such as multiple small areas of necrosis and diffuse gliosis.^1–4

Electrophysiological assessment has also been studied for its utility in the diagnosis of cerebral white matter injury in the premature infant. With conventional electroencephalogram (EEG) analysis, the finding of positive rolandic sharp waves has been shown to be a highly specific indicator of ultrasonographically identified cystic white matter injury,^5–12 but seems to be lacking in sensitivity, shown to be as low as 30% in extremely low birth weight infants. Conventional EEGs also have the disadvantage of requiring highly skilled interpretation, which renders this tool of limited availability in many neonatal intensive care unit settings. A more recently developed technique is amplitude-integrated EEG (a-EEG) methods, such as the cerebral function monitor (CFM), which utilizes only 2 channels, 1 from each hemisphere, for an integrated EEG signal that reflects averaged levels of total cerebral activity. The a-EEG has been demonstrated to have utility in the assessment of the severity of neonatal encephalopathy in the term-born infant,¹³ but no published data exist on its utility in evaluation of cerebral injury in the premature infant.

In recent years, automated methods for the spectral analysis of the neonatal EEG have been developed and have shown promise for the study of the fetal and neonatal EEG, both in animal models and in human infants.^14–20 Spectral analysis of the EEG quantifies the intensity and the frequency distribution of the EEG. In pathologic states such as hypoxic-ischemic encephalopathies, EEG intensity has been shown to increase or decrease,²¹ whereas frequency almost invariably decreases.²² Thus, a focus on the frequency below which most of the power of the EEG tracing exists is potentially a reliable, quantitative measure of such a pathologic state. This spectral edge frequency (SEF), the frequency below which 90% of the power in the EEG exists, was evaluated in this study of human premature infants.

Thus, this study was undertaken with the hypothesis that the SEF- but not intensity- or amplitude-based measures of EEG activity will be lower in very low birth weight infants who develop cerebral white matter injury during the first 3 weeks of life. Such a finding would suggest that this quantitative electrophysiological technique, which is easily conducted at the bedside, could identify cerebral white matter injury potentially early enough for therapeutic intervention.

	METHODS

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Study Population
We conducted a longitudinal cohort study in infants who were admitted to the special care and neonatal intensive care unit nurseries at Christchurch Women’s Hospital between July 1999 and August 2000 with birth weight <1500 g. Fifty-nine infants were studied during this period (87% recruitment). The median gestational age (GA) was 27 weeks (range: 23–31 weeks), and mean birth weight was 1080 ± 389 g. Informed parental consent was obtained as soon after delivery as possible. All infants were treated according to the standard protocols of the nurseries. No infants were on anticonvulsant therapy, but 5 infants were receiving low-dose morphine infusions (5–10 µg/kg/h) during their study.

Electrophysiological Analysis
The electrodes were placed bilaterally at the central and parietal positions according to the C3, C4, P3, and P4 placement on the newborn infant using the international 10-20 system (Hydrospot neonatal electrodes, Physiometrix Inc, North Billerica, MA). Application of the electrodes was modified to accommodate the fragility of the newborn premature skin. Hydrospot electrodes were applied with minimal skin preparation in the infants and without colloidin adhesive by a senior investigator (T.I., B.D.) or the trained research nurse (L.B., C.S.). The length of the recordings ranged from 24 to 106 hours during this pilot data phase.

The right- and left-side EEG signals were amplified 5000 times and bandpass-filtered with a first-order high-pass filter with -3 dB, frequency at 1 Hz and a fourth-order low-pass Butterworth filter at -3 dB frequency at 50 Hz. The signal was digitized by the computer at a sampling rate of 256 Hz, and the intensity spectra and derived parameters were calculated from 4-second epochs of the digitized signal¹⁴ (SEEQ Brainz Instruments, Auckland, NZ). The total EEG intensity (µV²) was calculated on the intensity (power) spectrum between 2 and 20 Hz. SEF was calculated as the frequency below which 90% of the intensity was present. The amplitude was calculated from the bandpass filtered and rectified signal. The EEG intensity, amplitude, and spectral edge measurements were averaged and stored to disk at 1-minute intervals.

The averaged signals were analyzed off-line using customized software (Chart analyser, Brainz Instruments). EEG data were defined as valid and included in the analyses if the electrode impedance were <20 kohm per pair, the signal intensity was >10µV², and the continuous data epoch was >120 minutes. The EEG intensity data (µV²) were log-transformed to approximate a normal distribution.²³

Data Collection
Data regarding the pregnancy, birth history, and neonatal course, including respiratory, cardiovascular, and nutritional history, were recorded. Early severity of initial illness was assessed using the clinical risk index for babies score.²⁴ Levels of arterial blood pressure, blood glucose concentrations, pulse oximetry recordings, and any change in stability of the infant were recorded by event and time on a record sheet for each session.

Definition of White Matter Abnormality on Magnetic Resonance Imaging
All images were independently double-read by a qualified pediatric neuroradiologist who was blinded to the infant’s clinical course and were defined as normal or white matter injury, based on scoring using a modified standardized scoring sheet.²⁵ Thus, on MRI at term, infants were classified as 1) severe white matter injury, characterized by cystic white matter change and by ventriculomegaly, consistent with diffuse loss of white matter volume; 2) moderate white matter injury, characterized by signal abnormality in periventricular white matter and by ventriculomegaly but no cysts; 3) mild white matter injury, characterized by focal white matter signal abnormality but no cysts or definite ventriculomegaly; or 4) normal. An illustration of the MRI characteristics of these groupings of white matter injury is shown in Fig 1.

View larger version (63K): [in this window] [in a new window]   Fig 1. Representative MRIs of 3 different premature infants with white matter injury in the 3 classification groups.

Among the premature infants, white matter injury was the main outcome variable analyzed. To adjust for potential confounding variables, we analyzed continuous variables by multiple linear regression and dichotomous variables by logistic regression. The relationship of the possible confounding variables of clinical risk index for babies score, as a measure of severity of early illness, and the severity of cardiorespiratory illness (hypotension, days of oxygen) were explored and found not to be significant on univariate analysis and thus excluded from the model. Birth weight was not included in the analysis because of its co-linearity with GA. Thus, the logistic modeling analysis of SEF to the presence of moderate or severe white matter injury included the potential confounding variables of postnatal day of age of monitoring and corrected GA.

	RESULTS

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Fifty-nine very low birth weight infants who were admitted to the Christchurch Women’s Hospital neonatal intensive care unit in New Zealand were studied between June 1999 and August 2000. Five recordings were excluded for inadequate signal quality; thus, 54 infants with recordings from each hemisphere (n = 108) were analyzed. The characteristics of the study population are shown in Table 1. The recordings were undertaken between days 1 and 20 of life (median day 6).

View larger version (23K): [in this window] [in a new window]   Fig 2. Levels of EEG measures in premature infants without white matter injury (no WMI) and in premature infants with mild WMI, moderate WMI, and cystic WMI defined at term. Values shown are median level (25th/75th box; 10th/90th error bars; 5th/95th outliers) of SEF (graph A), amplitude (graph B), and intensity (graph C).

View larger version (30K): [in this window] [in a new window]   Fig 3. Representative SEF recordings during the first 10 days of life in an infant with no evidence of white matter injury on MRI at term (A) and an infant who had evidence of moderate white matter injury on MRI at term (B).

	DISCUSSION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION CONCLUSION REFERENCES

 
This study is the first to document a relationship between SEF-based measures and the presence and severity of white matter injury in the premature infant. Our results indicate that SEF measurement may have value in the diagnosis of white matter injury in the premature infant.

Conventional EEG has many disadvantages for bedside use in cerebral monitoring of the newborn. It requires skilled interpretation of qualitative changes and is not easily suited to prolonged recording or monitoring. In addition, the value of the conventional EEG in the premature infant has been debated.^26–28 Positive rolandic sharp waves have been associated with periventricular white matter injury^5–12 but lack sensitivity.^5,6 Because of these limitations, simpler accurate methods for continuous cerebral electrophysiologic monitoring in the human newborn have been sought. One such method is an a-EEG known as the CFM, which records on slow-running paper.²⁹ The CFM reflects trends in averaged levels of cerebral activity and has been shown to have predictive value for neurologic outcome in the term newborn infant with hypoxic-ischemic encephalopathy.¹³ During the first 6 hours of life, all term infants with continuous a-EEG background survived with normal outcome, whereas infants with isoelectric recordings or burst-suppression patterns had high rates of death or disability.³⁰ There have been no published studies of use of the a-EEG in the premature infant. The a-EEG uses signal-processing techniques, amplitude, and time compression and rectification,²⁹ which limit the discrimination of detailed information regarding amplitude and frequency components of the signal. The output of a-EEG recording produces only a semiquantitative measure of patterns of cerebral function.

In contrast to conventional EEG and a-EEG, spectral analysis of the EEG offers quantitative data output and ease of interpretation. Spectral intensity analysis allows tracking of temporal changes in the frequency distribution of the EEG activity, rather than splitting the signal into a number of frequency bands. The SEF was originally defined as the highest frequency at which a significant amount of intensity was present in the EEG.¹⁸ This may be interpreted as 95% of the intensity¹⁸ or more commonly in recent analyses, including our study, as 90%.¹⁶ No previous studies have documented the relationship of spectral edge-based measures to neurologic status or outcome in the premature or term infant. Our study documents an association between lower SEF and the presence and severity of white matter injury in the premature infant. Our analysis was undertaken using newly developed software processing of the EEG signal between 2 Hz and 20 Hz to generate the spectral array and SEF (BrainZ Instruments). These frequencies above 2 Hz were analyzed to gain optimal signal quality without artifact. Although an appreciable amount of the cerebral activity in extremely premature infants may reside in the very slow frequency below 2 Hz, the presence of artifacts and high noise at these low frequencies significantly diminishes signal-to-noise ratio and thus reliable analysis. In the future, technical advancements may overcome these limitations to allow signal detection across a broader frequency band.

No relationship was found between measures of total EEG amplitude and intensity and white matter injury, indicating that the loss of EEG intensity in high frequency bands reflected in the SEF seems to be a new critical measure in identification of white matter injury. The pathophysiological basis for the alteration in this spectral edge-based measure seems specifically related to the presence of cerebral white matter injury. In the premature lamb model of hypoxic-ischemic injury, SEF was a sensitive marker of white matter injury, with sustained alteration in the measure when the injury resulted in periventricular white matter cyst formation.^14,15 Thaler et al²⁰ documented the association of decreased SEF with variable decelerations during labor, suggesting that the measure reflected fetal condition and risk of cerebral insult. By contrast, changes in the amplitude and intensity of the EEG signal are most prominent in the term sheep cerebral cortex sustaining hypoxic-ischemic injury with parasagittal cerebral cortical neuronal necrosis.²¹

It is not possible to speculate on the relationship of the change in SEF to the timing of the white matter injury as our data were collected as a single recording during the first 3 weeks of life. Additional studies with serial recordings from the first 24 hours of life in association with earlier MRI are planned to address specifically the postnatal time course of the change in SEF in relation to the evolution of white matter injury.

SEF has been shown to correlate with cerebral maturation in fetal lambs¹⁶ and in human neonates in late gestation.¹⁷ Our study did not document any relationship of the SEF to advancing GA but did control for GA, age at recording, and also corrected GA as covariates in the analysis of the relationship of EEG measures to the presence and severity of white matter injury. It is important to note that all infants in our series had GAs of <32 weeks. Bell et al¹⁷ had found a change with maturation only in infants of >34 weeks’ gestation, with no clear relationship of SEF to gestation for infants between 28 and 34 weeks.

	CONCLUSION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION CONCLUSION REFERENCES

 
This study is the first to document the utility of automated spectral analysis of the EEG and, specifically, calculation of the SEF in relation to the early detection of the presence and the severity of white matter injury, identified by MRI at term. Although it is acknowledged that additional detailed studies are required to confirm the value of this measure, these preliminary results are encouraging. If validated, then this bedside measure may allow early detection of white matter injury and an understanding of the time sequence of brain injury to enable the clinician to determine the best strategies of care for the preterm infant.

	ACKNOWLEDGMENTS

 
This study was supported by grants from the Neurological Foundation of New Zealand, Health Research Council of New Zealand, Lottery Health of New Zealand, and National Institutes of Health.

All of the authors, with the exception of Carole Spencer and Brian Darlow, have varying levels of affiliation with BrainZ Instruments Ltd (incorporated in 2002), which is the company that now owns the intellectual property to the EEG technology used in this research.

	FOOTNOTES

 
Received for publication Feb 6, 2002; Accepted May 17, 2002.

Reprint requests to (T.E.I.) Murdoch Children’s Research Institute and Howard Florey Institute, Royal Women’s and Royal Children’s Hospital, Flemington Rd, Parkville, Victoria 3052, Australia. E-mail: indert{at}cryptic.rch.unimelb.edu.au

	REFERENCES

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION CONCLUSION REFERENCES

 

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