Diagnosis of seizures and encephalopathy using conventional EEG and amplitude integrated EEG

doi:10.1016/B978-0-444-64029-1.00018-7

Handbook of Clinical Neurology

Volume 162, 2019, Pages 363-400

https://doi.org/10.1016/B978-0-444-64029-1.00018-7 Get rights and content

Abstract

Seizures are more common in the neonatal period than at any other time of life, partly due to the relative hyperexcitability of the neonatal brain. Brain monitoring of sick neonates in the NICU using either conventional electroencephalography or amplitude integrated EEG is essential to accurately detect seizures. Treatment of seizures is important, as evidence increasingly indicates that seizures damage the brain in addition to that caused by the underlying etiology. Prompt treatment has been shown to reduce seizure burden with the potential to ameliorate seizure-mediated damage. Neonatal encephalopathy most commonly caused by a hypoxia–ischemia results in an alteration of mental status and problems such as seizures, hypotonia, apnea, and feeding difficulties. Confirmation of encephalopathy with EEG monitoring can act as an important adjunct to other investigations and the clinical examination, particularly when considering treatment strategies such as therapeutic hypothermia. Brain monitoring also provides useful early prognostic indicators to clinicians. Recent use of machine learning in algorithms to continuously monitor the neonatal EEG, detect seizures, and grade encephalopathy offers the exciting prospect of real-time decision support in the NICU in the very near future.

Introduction

Monitoring the brain of the sick newborn infant in the NICU using either conventional electroencephalography (cEEG) or amplitude integrated EEG (aEEG) is essential in order to accurately detect seizures and assess the severity of neonatal encephalopathy (NE).

Seizures are more likely to occur in the neonatal period than at any other time in childhood (Silverstein, 2009), partly due to the susceptibility and relative hyperexcitability of the newborn brain. Intense synaptic formation in the neonatal period, overexpression of excitatory receptors, and the excitatory role of GABA up to term age all contribute to the increase in seizure frequency seen in this age group (Jensen, 2009). Seizures are a medical emergency and both animal (Wirrell et al., 2001) and human studies (Miller et al., 2002; Glass et al., 2009; Kharoshankaya et al., 2016) increasingly suggest that they may exacerbate neuronal damage beyond that caused by the underlying etiology alone.

Urgent detection is paramount and prompt treatment of seizures has been shown to reduce seizure burden (Srinivasakumar et al., 2015) with the potential to improve outcome.

Encephalopathy in the neonatal period is characterised by an altered mental status that can range anywhere from irritability to coma; seizures are common and other problems such as hypotonia, apnea, and feeding difficulties may be present, depending on the severity of the encephalopathy. The most common cause of NE is hypoxia–ischemia and it is therefore very common to see the term hypoxic–ischemic encephalopathy (HIE) used in the literature. However, the cause of an encephalopathy in the first hours and days after birth may not always be apparent and, as a result, many now prefer to use the term neonatal encephalopathy (Dammann et al., 2011; Volpe, 2012). Other causes of encephalopathy include stroke, hemorrhage, meningitis, structural brain abnormalities, inborn errors of metabolism, epileptic encephalopathy, vitamin responsive seizures, and nonaccidental injury (Martinello et al., 2017).

Confirmation of encephalopathy with EEG monitoring can act as an important adjunct to other investigations and the clinical examination, particularly when considering treatment strategies such as therapeutic hypothermia (TH) for HIE (Weeke et al., 2017b). Assessment of both cEEG and aEEG in infants with NE can offer valuable information regarding prognosis (Thoresen et al., 2010; Azzopardi, 2014; Weeke et al., 2016).

The more recent use of machine learning for the development of algorithms to continuously monitor the neonatal EEG, detect seizures (Temko and Lightbody, 2016), and grade encephalopathy (Stevenson et al., 2013) offers the exciting prospect of real-time decision support in the NICU in the very near future.

Section snippets

The Neonatal EEG: What Does It Measure?

The signals measured by the EEG are on the order of microvolts (μV) and represent summated postsynaptic neuronal activity in the cortex. EEG has a temporal resolution that is much higher than functional MRI and it can display brain activity on a millisecond scale. EEG also exhibits a rich variety of frequencies, amplitudes, and waveform morphologies from all monitored brain regions and features such as synchrony and symmetry across both cerebral hemispheres are very easily measured. A knowledge

aEEG and cEEG Monitoring in the NICU

Multichannel EEG with video is the gold standard for monitoring newborn brain function and at least 24-h monitoring is recommended for infants that are at risk of seizures. In infants with confirmed seizures, monitoring should be continued for 24 h after the last electrographic seizure (Shellhaas et al., 2011). Over the last 10 years, there has been an increase in demand for continuous cEEG monitoring for at-risk neonates (Wusthoff, 2016), particularly with the advent of TH and the

aEEG and EEG for the Diagnosis of Neonatal Seizures

The estimated incidence of neonatal seizures in the general population is between 1–3 per 1000 live births but with a higher incidence reported in infants born prematurely and with low birth weight (Uria-Avellanal et al., 2013). In preterm infants, reported incidence rates of seizures confirmed with cEEG or aEEG vary considerably, from 5% to 43% (Shah et al., 2010; Wikstrom et al., 2012; Lloyd et al., 2017). The reason for the disparity in these reported values is not clear, but it may reflect

aEEG and cEEG for the Diagnosis of Neonatal Encephalopathy

The commonest cause of NE is hypoxia–ischemia around the time of birth and both the aEEG and cEEG patterns seen in this condition are very different from those encountered in other types of neonatal encephalopathy. Therefore the typical findings that have been described in hypoxic–ischemic encephalopathy will be described in detail in the first instance followed by the EEG patterns encountered in other types of NE.

It is worth noting that there is much heterogeneity in studies that have used EEG

EEG Diagnosis of Neonatal Encephalopathy

Before any attempt is made to assess the background pattern of the neonatal EEG and diagnose encephalopathy in an infant, a thorough knowledge of the characteristics of the EEG in the healthy newborn infant is essential. Any patterns that deviate from this can signal an encephalopathy of varying severity. It is beyond the scope of this chapter to describe all of the normal and abnormal features of term and preterm background EEG, but a number of excellent references do exist (Pressler et al.,

aEEG in Hypoxic–Ischemic Encephalopathy

Prior to the introduction of therapeutic hypothermia, many studies described the typical aEEG patterns encountered in HIE. Most of these studies originated from very experienced aEEG centers in Sweden and the Netherlands; the classification schemes generally used for aEEG grading are illustrated in Fig. 18.14 (Hellstrom-Westas et al., 1995; al Naqeeb et al., 1999; Toet et al., 1999; van Rooij et al., 2005; Thoresen et al., 2010).

The pattern recognition method of aEEG classification, prior to

cEEG in HIE

Prior to the introduction of TH, the first studies that analyzed the role of cEEG in HIE generally recorded short duration (2 − 3h) multichannel EEG between 2 and 7 days after birth. Normal cEEG background within the first week of life was reported to be predictive of a normal long-term outcome (Sarnat and Sarnat, 1976; Watanabe et al., 1980), while significant attenuation of amplitude and burst suppression pattern (burst duration 1–10 s and interburst interval > 10 s with amplitude between bursts < 5

EEG in Other Causes of Neonatal Encephalopathy

The evolving EEG pattern seen in infants with HIE is characteristic and was discussed previously; the EEG generally improves over time (except in those with a nonrecovering inactive pattern). In a neonate with an abnormal EEG pattern that is unchanging or deteriorating, non-hypoxic ischemic causes should be suspected. EEG characteristics have been described in other causes of NE including infection, stroke, hemorrhage, brain malformation, inborn errors of metabolism and, less frequently,

Epileptic Encephalopathy

In neonatal onset epileptic or myoclonic encephalopathy, characteristic EEG changes have been well described (Yamamoto et al., 2011; Dulac, 2013; Kojima et al., 2018). Genetic causes for these conditions are constantly emerging with some genetic causes being linked to specific EEG changes, e.g., in KCNQ2 encephalopathy (Numis et al., 2014; Vilan et al., 2017).

Early infantile epileptic encephalopathy, often referred to as Ohtahara syndrome, may be caused by a number of etiologies. The EEG

Factors Affecting EEG Background Characteristics in Neonates With Encephalopathy

Several factors may influence the aEEG and cEEG other than the underlying pathology and should be considered when making any diagnosis in neonates. The administration of AEDs, in particular phenobarbitone, is associated with either general EEG amplitude depression or increased discontinuity that can last for 30–60 min. However, the rate of aEEG recovery is believed to be unaffected by AED administration (Hellstrom-Westas et al., 1995; Toet et al., 1999; ter Horst et al., 2004a; van Leuven et

Conclusion

EEG provides a unique insight into newborn brain function. It is essential for the accurate detection of seizures and to monitor the efficacy of AED treatment. In neonatal encephalopathy, EEG not only helps to assess the severity of the encephalopathy, but it can also help determine etiology. EEG provides a unique physiologic biomarker that may soon provide unique information to help instigate personalized medicine approaches for the treatment of neonatal seizures and encephalopathy.

References (201)

A. Aarabi et al.
A multistage knowledge-based system for EEG seizure detection in newborn infants
Clin Neurophysiol
(2007)
W.C. Allan et al.
Perinatal cerebrovascular disease in the neonate. Parenchymal ischemic lesions in term and preterm infants
Pediatr Clin North Am
(1992)
J. Altenburg et al.
Seizure detection in the neonatal EEG with synchronization likelihood
Clin Neurophysiol
(2003)
G. Ancora et al.
Early predictors of short term neurodevelopmental outcome in asphyxiated cooled infants. A combined brain amplitude integrated electroencephalography and near infrared spectroscopy study
Brain Dev
(2013)
M. Andre et al.
Electroencephalography in premature and full-term infants. Developmental features and glossary
Neurophysiol Clin
(2010)
M. Bazhenov et al.
Cellular and network mechanisms of electrographic seizures
Drug Discov Today Dis Model
(2008)
J.C. Beal et al.
Early-onset epileptic encephalopathies: Ohtahara syndrome and early myoclonic encephalopathy
Pediatr Neurol
(2012)
Y. Ben-Ari et al.
GABAA, NMDA and AMPA receptors: a developmentally regulated 'menage a trois'
Trends Neurosci
(1997)
M.D. Bourez-Swart et al.
Detection of subclinical electroencephalographic seizure patterns with multichannel amplitude-integrated EEG in full-term neonates
Clin Neurophysiol
(2009)
G.B. Boylan et al.
Monitoring neonatal seizures
Semin Fetal Neonatal Med
(2013)

R.S. Chequer et al.

Prognostic value of EEG in neonatal meningitis: retrospective study of 29 infants

Pediatr Neurol

(1992)

P.J. Cherian et al.

Validation of a new automated neonatal seizure detection system: a clinician's perspective

Clin Neurophysiol

(2011)

R.R. Clancy et al.

EEG changes during recovery from acute severe neonatal citrullinemia

Electroencephalogr Clin Neurophysiol

(1991)

W. Deburchgraeve et al.

Automated neonatal seizure detection mimicking a human observer reading EEG

Clin Neurophysiol

(2008)

O. Dulac

Epileptic encephalopathy with suppression-bursts and nonketotic hyperglycinemia

Handb Clin Neurol

(2013)

C. Ficicioglu et al.

Isolated neonatal seizures: when to suspect inborn errors of metabolism

Pediatr Neurol

(2011)

N. Frenkel et al.

Neonatal seizure recognition—comparative study of continuous-amplitude integrated EEG versus short conventional EEG recordings

Clin Neurophysiol

(2011)

H.C. Glass et al.

Clinical neonatal seizures are independently associated with outcome in infants at risk for hypoxic-ischemic brain injury

J Pediatr

(2009)

J. Gotman et al.

Automatic seizure detection in the newborn: methods and initial evaluation

Electroencephalogr Clin Neurophysiol

(1997)

M.M. Grigg-Damberger et al.

Neonatal burst suppression: its developmental significance

Pediatr Neurol

(1989)

S. Hamelin et al.

Influence of hypothermia on the prognostic value of early EEG in full-term neonates with hypoxic ischemic encephalopathy

Neurophysiol Clin

(2011)

G. Holmes et al.

Prognostic value of the electroencephalogram in neonatal asphyxia

Electroencephalogr Clin Neurophysiol

(1982)

M. Horan et al.

Lack of influence of mild hypothermia on amplitude integrated-electroencephalography in neonates receiving extracorporeal membrane oxygenation

Early Hum Dev

(2007)

S. Janackova et al.

Electroencephalographic characteristics of epileptic seizures in preterm neonates

Clin Neurophysiol

(2016)

F.E. Jensen

Neonatal seizures: an update on mechanisms and management

Clin Perinatol

(2009)

R. Khazipov et al.

Early patterns of electrical activity in the developing cerebral cortex of humans and rodents

Trends Neurosci

(2006)

M. Kitayama et al.

Wavelet analysis for neonatal electroencephalographic seizures

Pediatr Neurol

(2003)

G. Klinger et al.

Prognostic value of EEG in neonatal bacterial meningitis

Pediatr Neurol

(2001)

K. Kojima et al.

A patient with early myoclonic encephalopathy (EME) with a de novo KCNQ2 mutation

Brain Dev

(2018)

I. Korotchikova et al.

EEG in the healthy term newborn within 12 hours of birth

Clin Neurophysiol

(2009)

I. Korotchikova et al.

Sleep-wake cycle of the healthy term newborn infant in the immediate postnatal period

Clin Neurophysiol

(2016)

M.D. Lamblin et al.

The electroencephalogram of the full-term newborn: review of normal features and hypoxic-ischemic encephalopathy patterns

Neurophysiol Clin

(2013)

A. Liu et al.

Detection of neonatal seizures through computerized EEG analysis

Electroencephalogr Clin Neurophysiol

(1992)

N.E. Lynch et al.

The temporal characteristics of seizures in neonatal hypoxic ischemic encephalopathy treated with hypothermia

Seizure

(2015)

K. Malk et al.

Drug effects on endogenous brain activity in preterm babies

Brain Dev

(2014)

E. Mariani et al.

Prognostic value of electroencephalograms in asphyxiated newborns treated with hypothermia

Pediatr Neurol

(2008)

M. Mastrangelo et al.

Acute neonatal encephalopathy and seizures recurrence: a combined aEEG/EEG study

Seizure

(2013)

S. Mathieson et al.

In-depth performance analysis of an EEG based neonatal seizure detection algorithm

Clin Neurophysiol

(2016)

S.R. Mathieson et al.

Phenobarbital reduces EEG amplitude and propagation of neonatal seizures but does not alter performance of automated seizure detection

Clin Neurophysiol

(2016)

S.R. Mathieson et al.

Validation of an automated seizure detection algorithm for term neonates

Clin Neurophysiol

(2016)

M.A. Mikati et al.

Pyridoxine-dependent epilepsy: EEG investigations and long-term follow-up

Electroencephalogr Clin Neurophysiol

(1991)

N. al Naqeeb et al.

Assessment of neonatal encephalopathy by amplitude-integrated electroencephalography

Pediatrics

(1999)

D. Azzopardi

Predictive value of the amplitude integrated EEG in infants with hypoxic ischaemic encephalopathy: data from a randomised trial of therapeutic hypothermia

Arch Dis Child Fetal Neonatal Ed

(2014)

D.V. Azzopardi et al.

Moderate hypothermia to treat perinatal asphyxial encephalopathy

N Engl J Med

(2009)

W. Banoczi

How some drugs affect the electroencephalogram (EEG)

Am J Electroneurodiagnostic Technol

(2005)

J.D. Barks et al.

Phenobarbital augments hypothermic neuroprotection

Pediatr Res

(2010)

M. Battin et al.

Rebound seizures during rewarming

Pediatrics

(2004)

P. Baxter et al.

Pyridoxine-dependent seizures: demographic, clinical, MRI and psychometric features, and effect of dose on intelligence quotient

Dev Med Child Neurol

(1996)

A.H. Bell et al.

Comparison of the effects of phenobarbitone and morphine administration on EEG activity in preterm babies

Acta Paediatr

(1993)

L. Bennet et al.

Differential effects of hypothermia on early and late epileptiform events after severe hypoxia in preterm fetal sheep

J Neurophysiol

(2007)

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The prognosis for infants with NE and seizures is particularly poor, with seizures leading to an increased rate of mortality and worsening of clinical outcomes (Pisani and Spagnoli, 2016). EEG is an excellent tool for detecting seizures but interpretation in neonates is an ongoing challenge that requires specialised personnel and equipment (Boylan et al., 2019). In a recent study, blood purines were measured, using the same technique as used for adults, in a mouse model of neonatal hypoxia-induced seizures and infants with neonatal encephalopathy (NE).
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La electroencefalografía integrada por amplitud (aEEG) es una herramienta utilizada en la neuromonitorización del neonato crítico. En el paciente con asfixia perinatal, su interpretación es clave para identificar a los candidatos a hipotermia terapéutica, detectar crisis subclínicas y aportar información pronóstica. Nuestro objetivo fue analizar la concordancia en la interpretación del aEEG entre neonatólogos con distinto nivel de experiencia.
Estudio retrospectivo unicéntrico de los recién nacidos ≥ 35 semanas con asfixia perinatal incluidos consecutivamente durante un periodo de dos años y monitorizados con aEEG durante al menos 6 horas. El médico de guardia interpretó el aEEG respecto al trazado de base, los ciclos vigilia-sueño y las crisis. Los aEEG fueron revisados de forma ciega por dos neonatólogas con distinta experiencia. Se analizó la concordancia (coeficiente Kappa de Cohen, k) de los aEEG divididos en periodos de 0-3 horas y 3-6 horas de vida, entre ambas y la de su consenso con el médico de guardia.
Se incluyeron 75 neonatos, 5 de ellos no se monitorizaron. Se analizaron 132 trazados con una concordancia muy buena entre las dos examinadoras en las tres características del aEEG. El k respecto al médico de guardia fue muy bueno para el trazado de base (k= 0,93), moderado (k= 0,52) para los ciclos vigilia-sueño y débil (k= 0,32) para las crisis.
Este estudio apoya una mayor facilidad para interpretar adecuadamente el trazado de base frente a los ciclos vigilia-sueño o las crisis, mejorando cuando se recibe una formación dirigida en el aEEG.
Amplitude integrated electroencephalography (aEEG) is a widely tool used for neuromonitoring in the critical neonate. In the patient with perinatal asphyxia, its interpretation is key to identifying candidates for therapeutic hypothermia, detecting subclinical seizures and providing pronostic information. Our aim was to analyze the concordance in the interpretation of aEEG among neonatologists with different level of experience.
Unicenter retrospective study of newborns ≥35 weeks with perinatal asphyxia included consecutively over a two-year period and monitored with aEEG for at least 6 h. The bedside neonatologist interpreted aEEG regarding background pattern, sleep-wake cycling, and seizures. The aEEG tracings were blindly reviewed by two neonatologists with different experience. The aEEG tracings were divided into periods of 0–3 h and 3-6 h of life, and the concordance (Cohen Kappa coefficient, k), between the two examiners and that of their consensus with the bedside neonatologist, was analyzed.
Seventy-five newborns were included, 5 of them were not aEEG-monitored. 132 tracings were analyzed with a very good concordance between the two examiners in the three characteristics of the aEEG. The k for the bedside neonatologist was very good for background pattern (k = 0.93), moderate (k = 0.52) for sleep-wake cycling, and weak (k = 0.32) for seizures.
This study supports that background pattern is easily interpreted compared to sleep-wake cycling or crisis, improving when targeted training on aEEG is received.
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Our study showed that the NE-RS may be used without the 2 aEEG items, for example, in resource-limited settings. However, it is important to note that aEEG monitoring helps to detect and treat electroencephalographic seizures and further characterize the evolution of the neurologic dysfunction beyond 6 hours of age.39,46 In our opinion, a key original aspect of the NE-RS is that the numerical assignment for each item is not symmetrical, which emphasizes the severity of neurologic dysfunction.
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Chapter 18 - Diagnosis of seizures and encephalopathy using conventional EEG and amplitude integrated EEG

Abstract

Introduction

Section snippets

The Neonatal EEG: What Does It Measure?

aEEG and cEEG Monitoring in the NICU

aEEG and EEG for the Diagnosis of Neonatal Seizures

aEEG and cEEG for the Diagnosis of Neonatal Encephalopathy

EEG Diagnosis of Neonatal Encephalopathy

aEEG in Hypoxic–Ischemic Encephalopathy

cEEG in HIE

EEG in Other Causes of Neonatal Encephalopathy

Epileptic Encephalopathy

Factors Affecting EEG Background Characteristics in Neonates With Encephalopathy

Conclusion

Clin Neurophysiol

Pediatr Clin North Am

Clin Neurophysiol

Brain Dev

Neurophysiol Clin

Drug Discov Today Dis Model

Pediatr Neurol

Trends Neurosci

Clin Neurophysiol

Semin Fetal Neonatal Med

Pediatr Neurol

Clin Neurophysiol

Electroencephalogr Clin Neurophysiol

Clin Neurophysiol

Handb Clin Neurol

Pediatr Neurol

Clin Neurophysiol

J Pediatr

Electroencephalogr Clin Neurophysiol

Pediatr Neurol

Neurophysiol Clin

Electroencephalogr Clin Neurophysiol

Early Hum Dev

Clin Neurophysiol

Clin Perinatol

Trends Neurosci

Pediatr Neurol

Pediatr Neurol

Brain Dev

Clin Neurophysiol

Clin Neurophysiol

Neurophysiol Clin

Electroencephalogr Clin Neurophysiol

Seizure

Brain Dev

Pediatr Neurol

Seizure

Clin Neurophysiol

Clin Neurophysiol

Clin Neurophysiol

Electroencephalogr Clin Neurophysiol

Assessment of neonatal encephalopathy by amplitude-integrated electroencephalography

Pediatrics

Predictive value of the amplitude integrated EEG in infants with hypoxic ischaemic encephalopathy: data from a randomised trial of therapeutic hypothermia

Arch Dis Child Fetal Neonatal Ed

Moderate hypothermia to treat perinatal asphyxial encephalopathy

N Engl J Med

How some drugs affect the electroencephalogram (EEG)

Am J Electroneurodiagnostic Technol

Phenobarbital augments hypothermic neuroprotection

Pediatr Res

Rebound seizures during rewarming

Pediatrics

Pyridoxine-dependent seizures: demographic, clinical, MRI and psychometric features, and effect of dose on intelligence quotient

Dev Med Child Neurol

Comparison of the effects of phenobarbitone and morphine administration on EEG activity in preterm babies

Acta Paediatr

Differential effects of hypothermia on early and late epileptiform events after severe hypoxia in preterm fetal sheep

J Neurophysiol