Amplitude-Integrated EEG for Neurological Assessment and Seizure Detection in a German Pediatric Intensive Care Unit

Association of bilaterally suppressed EEG amplitudes and outcomes in critically ill children

Background and objectives

Amplitude-integrated EEG (aEEG) is used to assess electrocortical activity in pediatric intensive care if (continuous) full channel EEG is unavailable but evidence regarding the meaning of suppressed aEEG amplitudes in children remains limited. This retrospective cohort study investigated the association of suppressed aEEG amplitudes in critically ill children with death or decline of neurological functioning at hospital discharge.

Methods

Two hundred and thirty-five EEGs derived from individual patients <18 years in the pediatric intensive care unit at the University Hospital Essen (Germany) between 04/2014 and 07/2021, were converted into aEEGs and amplitudes analyzed with respect to age-specific percentiles. Crude and adjusted odds ratios (OR) for death, and functional decline at hospital discharge in patients with bilateral suppression of the upper or lower amplitude below the 10th percentile were calculated. Sensitivity, specificity, positive (PPV) and negative predictive values (NPV) were assessed.

Results

The median time from neurological insult to EEG recording was 2 days. PICU admission occurred due to neurological reasons in 43% and patients had high overall disease severity. Thirty-three (14%) patients died and 68 (29%) had a functional decline. Amplitude suppression was observed in 48% (upper amplitude) and 57% (lower amplitude), with unilateral suppression less frequent than bilateral suppression. Multivariable regression analyses yielded crude ORs between 4.61 and 14.29 and adjusted ORs between 2.55 and 8.87 for death and functional decline if upper or lower amplitudes were bilaterally suppressed. NPVs for bilaterally non-suppressed amplitudes were above 95% for death and above 83% for pediatric cerebral performance category Scale (PCPC) decline, whereas PPVs ranged between 22 and 32% for death and 49-52% for PCPC decline.

Discussion

This study found a high prevalence of suppressed aEEG amplitudes in critically ill children. Bilaterally normal amplitudes predicted good outcomes, whereas bilateral suppression was associated with increased odds for death and functional decline. aEEG assessment may serve as an element for risk stratification of PICU patients if conventional EEG is unavailable with excellent negative predictive abilities but requires additional information to identify patients at risk for poor outcomes.

Amplitude Integrated Electroencephalography: Simulated Assessment of Neonatal Seizure Detection in PICU Patients

OBJECTIVES

Amplitude integrated electroencephalography (aEEG) is a mainstay of care in neonatal ICUs; however, knowledge gaps exist in relation to its accuracy for identifying seizures in older children. We aimed to review the diagnostic accuracy of existing neonatal seizure detection criteria for seizure detection in older children in hospital.

DESIGN

Retrospective study.

SETTING

PICU/Neurophysiology Department in Dublin.

PATIENTS

One hundred twenty patients (2 mo to 16 yr old) were chosen from a database of formal 10-20 system, 21-lead electroencephalography recordings (2012-2020), comprising 30 studies with seizures, 90 without.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Electroencephalography studies containing electrographic seizures (ESzs) were annotated to describe number, duration, distribution, and spread. Two-channel aEEG (using leads C3-P3, C4-P4) recordings were generated and independently reviewed by a professional specialist in clinical neurophysiology blinded to outcome and without reference to the raw electroencephalography trace. Logistic regression was used to identify factors associated with correct seizure identification on aEEG. Median patient age was 6.1 years. Abnormal recordings featured 123 seizures. Status epilepticus (SE) was evident by electroencephalography in 10 cases. Using neonatal criteria, aEEG had a sensitivity of 70% and negative predictive value of 90% for identifying any ESz. Accurate detection of individual seizures was diminished when seizures were very short or occurred during waking. Sensitivity for individual seizures was 81% when seizures less than 1 minute were excluded. aEEG correctly identified SE in 70% of the 10 cases, although ESz were confirmed to be present in 80% of this subpopulation.

CONCLUSIONS

aEEG criteria for neonatal seizure identification can be applied with caution to older children and should be supplemented by formal electroencephalography. Seizure identification is better for longer seizures and those arising from sleep. SE is not always recognized by aEEG among older children.

Electroencephalography Quality and Application Times in a Pediatric Emergency Department Setting: A Feasibility Study

BACKGROUND

Status epilepticus is the most common neurological emergency presenting to pediatric emergency departments. Nonconvulsive status epilepticus can be extremely challenging to diagnose, however, requiring electroencephalographic (EEG) confirmation for definitive diagnosis. We aimed to determine the feasibility of achieving a good-quality pediatric EEG recording within 20 minutes of presentation to the emergency department.

METHODS

Single-center prospective feasibility study in Cork University Hospital, Ireland, between July 2021 and June 2022. Two-channel continuous EEG was recorded from children (1) aged <16 years and (2) with Glasgow Coma Scale <11 or a reduction in baseline Glasgow Coma Scale in the case of a child with a neurodisability.

RESULTS

Twenty patients were included. The median age at presentation was 65.8 months (interquartile range, 23.2 to 119.0); 50% had a background diagnosis of epilepsy. The most common reason for EEG monitoring was status epilepticus (85%) followed by suspected nonconvulsive status (10%) and reduced consciousness of unknown etiology (5%). The mean length of recording was 93.1 minutes (S.D. 47.4). The mean time to application was 41.3 minutes (S.D. 11.7). The mean percent of artifact in all recordings was 19.3% (S.D. 15.9). Thirteen (65%) EEGs had <25% artifact. Artifact was higher in cases in which active airway management was ongoing.

CONCLUSIONS

EEG monitoring can be achieved in a pediatric emergency department setting within one hour of presentation. Overall, artifact percentage was low outside of periods of airway manipulation. Future studies are required to determine its use in early seizure detection and its support role in clinical decision-making in these patients.

[Pediatric neurocritical care]

Pediatric neurocritical care requires multidisciplinary expertise for the care of critically ill children. Approximately 14-16% of critically ill children in pediatric intensive care suffer from a primary neurological disease, whereby cardiac arrest and severe traumatic brain injury play major roles in Europe. The short-term goal of interventions in the pediatric intensive care unit is to stabilize vital functions, whereas the overarching goal is to achieve survival without neurological damage that enables fulfillment of the individual developmental physiological potential. For this reason, evidence-based methods for brain monitoring during the acute phase and recovery are necessary, which can be performed clinically or with technical devices. This applies to critically ill children with primary neurological diseases and for all children at risk for secondary neurological insults. Patients with diseases of the peripheral nervous system are also treated in pediatric intensive care medicine. In these patients, the primary aim frequently consists of bridging the time until recovery after acute deterioration, for example during an infection. In these patients, monitoring the cerebral function can be especially challenging, because due to the underlying disease the results of the examination cannot be interpreted in the same way as for previously neurologically healthy children. This article summarizes the complexity of pediatric neurocritical care by presenting examples of diagnostic and therapeutic approaches in the context of various neurological diseases that can be routinely encountered in the pediatric intensive care unit and can only be successfully treated by multidisciplinary teams.

The impact of age and electrode position on amplitude-integrated EEGs in children from 1 month to 17 years of age

Aim

Amplitude-integrated electroencephalography (aEEG) is used to monitor electrocortical activity in critically ill children but age-specific reference values are lacking. We aimed to assess the impact of age and electrode position on aEEG amplitudes and derive normal values for pediatric aEEGs from neurologically healthy children.

Methods

Normal EEGs from awake children aged 1 month to 17 years (213 female, 237 male) without neurological disease or neuroactive medication were retrospectively converted into aEEGs. Two observers manually measured the upper and lower amplitude borders of the C3 – P3, C4 – P4, C3 – C4, P3 – P4, and Fp1 – Fp2 channels of the 10–20 system. Percentiles (10th, 25th, 50th, 75th, 90th) were calculated for each age group (&lt;1 year, 1 year, 2–5 years, 6–9 years, 10–13 years, 14–17 years).

Results

Amplitude heights and curves differed between channels without sex-specific differences. During the first 2 years of life, upper and lower amplitudes of all but the Fp1–Fp2 channel increased and then declined until 17 years. The decline of the upper Fp1–Fp2 amplitude began at 4 years, while the lower amplitude declined from the 1st year of life.

Conclusions

aEEG interpretation must account for age and electrode positions but not for sex in infants and children.

Amplitude Integrated Electroencephalography - Reference Values in Children aged 2 months to 16 years

AIM

Amplitude integrated electroencephalography (aEEG) is a bedside neuromonitoring tool, standard within neonatal critical care provision. Its application in children is increasing but normative data underpinning such use are lacking. We present a dataset of normative aEEG values for children aged 2 months to 16 years.

METHODS

This retrospective observational cohort study derives aEEG normative amplitude characteristics from electroencephalograms (EEGs) recorded in Children's Health Ireland at Crumlin. aEEG was derived from 350 normal EEGs, recorded in children aged 2 months to 16 years. Supplementary aEEGs were derived from children with abnormal EEG traces. Median upper and lower margin amplitudes, and bandwidth were calculated from 5-minute waking and sleeping EEG epochs.

RESULTS

aEEG amplitudes vary with age and state, increasing over the first two years of life before diminishing. Upper and lower margin amplitudes, and bandwidth are greater during sleep for children < 6 years. Reference ranges may be cohorted into 2 groups (upper and lower reference limits; < 6 years - 38μV/7μV awake, 54μV/10μV asleep; > 6 years - 33μV/5μV awake, 36μV/6μV asleep) CONCLUSION: aEEG traces evolve with age in childhood and differ from neonatal values. We provide a comprehensive set of aEEG normatives to facilitate clinical interpretation in older children.

Amplitude-Integrated EEG Monitoring in Pediatric Intensive Care: Prognostic Value in Meningitis before One Year of Age

Pediatric morbidity from meningitis remains considerable. Preventing complications is a major challenge to improve neurological outcome. Seizures may reveal the meningitis itself or some complications of this disease. Amplitude-integrated electroencephalography (aEEG) is gaining interest for the management of patients with acute neurological distress, beyond the neonatal age. This study aimed at evaluating the predictive value of aEEG monitoring during the acute phase in meningitis among a population of infants hospitalized in the pediatric intensive care unit (PICU), and at assessing the practicability of the technique. AEEG records of 25 infants younger than one year of age hospitalized for meningitis were retrospectively analyzed and correlated to clinical data and outcome. Recording was initiated, on average, within the first six hours for n = 18 (72%) patients, and overall quality was considered as good. Occurrence of seizure, of status epilepticus, and the background pattern were significantly associated with unfavorable neurological outcomes. AEEG may help in the management and prognostic assessment of pediatric meningitis. It is an easily achievable, reliable technique, and allows detection of subclinical seizures with minimal training. However, it is important to consider the limitations of aEEG, and combinate it with conventional EEG for the best accuracy.

EEG monitoring during anesthesia in children aged 0 to 18 months: amplitude-integrated EEG and age effects

Background

The amplitude-integrated EEG (aEEG) is a widely used monitoring tool in neonatology / pediatric intensive care. It takes into account the amplitudes, but not the frequency composition, of the EEG. Advantages of the aEEG are clear criteria for interpretation and time compression. During the first year of life, the electroencephalogram (EEG) during sedation / anesthesia changes from a low-differentiated to a differentiated EEG; higher-frequency waves develop increasingly. There are few studies on the use of aEEG during pediatric anesthesia. A systematic evaluation of the aEEG in defined EEG stages during anesthesia / sedation is not yet available. Parameters of pediatric EEGs (power, median frequency, spectral edge frequency) recorded during anesthesia and of the corresponding aEEGs (upper and lower value of the aEEG trace) should be examined for age-related changes. Furthermore, it should be examined whether the aEEG can distinguish EEG stages of sedation / anesthesia in differentiated EEGs.

Methods

In a secondary analysis of a prospective observational study EEGs and aEEGs (1-channel recordings, electrode positions on forehead) of 50 children (age: 0–18 months) were evaluated. EEG stages: A (awake), Slow EEG, E2, F0, and F1 in low-differentiated EEGs and A (awake), B0–2, C0–2, D0–2, E0–2, F0–1 in differentiated EEGs.

Results

Median and spectral edge frequency increased significantly with age (p < 0.001 each). In low-differentiated EEGs, the power of the Slow EEG increased significantly with age (p < 0.001). In differentiated EEGs, the power increased significantly with age in each of the EEG stages B1 to E1 (p = 0.04, or less), and the upper and lower values of the aEEG trace increased with age (p < 0.001). A discriminant analysis using the upper and lower values of the aEEG showed that EEG epochs from the stages B1 to E1 were assigned to the original EEG stage in only 19.3% of the cases. When age was added as the third variable, the rate of correct reclassifications was 28.5%.

Conclusions

The aEEG was not suitable for distinguishing EEG stages above the burst suppression range. For this purpose, the frequency composition of the EEG should be taken into account.

Effects of a reduction of the number of electrodes in the EEG montage on the number of identified seizure patterns

Continuous EEG monitoring (cEEG) is frequently used in neurocritical care. The detection of seizures is one of the main objectives. The placement of the EEG electrodes is time consuming, therefore a reduced montage might lead to an increased availability in the ICU setting. It is unknown whether such a reduction of electrodes reduces the number of seizure patterns that are detected. A total of 95 seizure and 95 control EEG sequences from a pediatric epilepsy monitoring unit (EMU) were anonymized and reduced to an eight-lead montage. Two experts evaluated the recordings and the seizure detection rates using the reduced and the full montage were compared. Sensitivity and specificity for the seizure detection were calculated using the original EMU findings as gold standard. The sensitivity to detect seizures was 0.65 for the reduced montage compared to 0.76 for the full montage (p = 0.031). The specificities (0.97 and 0.96) were comparable (p = 1). A total of 4/9 (44%) of the generalized, 12/44 (27%) of the frontal, 6/14 (43%) of the central, 0/1 (0%) of the occipital, 6/20 (30%) of the temporal, and 5/7 (71%) of the parietal seizure patterns were not detected using the reduced montage. The median time difference between the onset of the seizure pattern in the full and reduced montage was 0.026s (IQR 5.651s). In this study the reduction of the EEG montage from 21 to eight electrodes reduced the sensitivity to detect seizure patterns from 0.76 to 0.65. The specificity remained virtually unchanged.

Artificial Intelligence Analysis of EEG Amplitude in Intensive Heart Care

This article first studied the morphological characteristics of the EEG for intensive cardiac care; that is, based on the analysis of the mechanism of disease diagnosis and treatment, a signal processing and machine learning model was constructed. Then, the methods of signal preprocessing, signal feature extraction, new neural network model structure, training mechanism, optimization algorithm, and efficiency are studied, and experimental verification is carried out for public data sets and clinical big data. Then, the principle of intensive cardiac monitoring, the mechanism of disease diagnosis, the types of arrhythmia, and the characteristics of the typical signal are studied, and the rhythm performance, individual variability, and neurophysiological basis of electrical signals in intensive cardiac monitoring are researched. Finally, the automatic signal recognition technology is studied. In order to improve the training speed and generalization ability, a multiclassification model based on Least Squares Twin Support Vector Machine (LS-TWIN-SVM) is proposed. The computational complexity of the classification model algorithm is compared, and intelligence is adopted. The optimization algorithm selects the parameters of the classifier and uses the EEG signal to simulate the model. Support Vector Machines and their improved algorithms have achieved the ultimum in shallow neural networks and have achieved good results in the classification and recognition of bioelectric signals. The LS-TWIN-SVM algorithm proposed in this paper has achieved good results in the classification and recognition of bioelectric signals. It can perform bioinformatics processing on intensive cardiac care EEG signals, systematically biometric information, diagnose diseases, the real-time detection, auxiliary diagnosis, and rehabilitation of patients.

aEEG as a useful tool for neuromonitoring in critically ill children - Current evidence and knowledge gaps

AIM

Amplitude-integrated electroencephalography (aEEG) is used in children beyond neonatal age, but systematic investigations have been lacking. This mini-review summarised aEEG studies on children aged one month to 18 years, evaluated the usefulness of aEEG and identified knowledge gaps or limitations.

METHODS

We searched the PubMed database for articles published in English up to September 2020, and 23 papers were identified.

RESULTS

aEEG was frequently used to compensate for the absence of continuous full-channel EEG monitoring, particularly for detecting seizures. Interpreting background patterns was based on neonatal classifications, as reference values for older infants and children are lacking. It is possible that aEEG could predict outcomes after paediatric cardiac arrests and other conditions. Gaps in our knowledge exist with regard to normal values in healthy children and the effects of sedation on aEEG background patterns in children.

CONCLUSION

The main application of aEEG was detecting and treating paediatric seizures. Further research should determine reference values and investigate the potential to predict outcome after critical events or in acute neurological disease. It is likely that aEEG will play a role in paediatric critical care in the future.

Characterization of aEEG During Sleep and Wakefulness in Healthy Children

INTRODUCTION

Interpretation of amplitude-integrated EEG (aEEG) is hindered by lacking knowledge on physiological background patterns in children. The aim of this study was to find out whether aEEG differs between wakefulness and sleep in children.

METHODS

Forty continuous full-channel EEGs (cEEG) recorded during the afternoon and overnight in patients <18 years of age without pathologies or only solitary interictal epileptiform discharges were converted into aEEGs. Upper and lower amplitudes of the C3-C4, P3-P4, C3-P3, C4-P4, and Fp1-Fp2 channels were measured during wakefulness and sleep by two investigators and bandwidths (BW) calculated. Sleep states were assessed according to the American Academy of Sleep Medicine. Median and interquartile ranges (IQR) were calculated to compare the values of amplitudes and bandwidth between wakefulness and sleep.

RESULTS

Median age was 9.9 years (IQR 6.1-14.7). All patients displayed continuous background patterns. Amplitudes and BW differed between wakefulness and sleep with median amplitude values of the C3-C4 channel 35 μV (IQR: 27-49) for the upper and 13 μV (10-19) for the lower amplitude. The BW was 29 μV (21-34). During sleep, episodes with high amplitudes [upper: 99 μV (71-125), lower: 35 μV (25-44), BW 63 μV (44-81)] corresponded to sleep states N2-N3. High amplitude-sections were interrupted by low amplitude-sections, which became the longer toward the morning [upper amplitude: 39 μV (30-51), lower: 16 μV (11-20), BW 23 μV (19-31)]. Low amplitude-sections were associated with sleep states REM, N1, and N2. With increasing age, amplitudes and bandwidths declined.

CONCLUSION

aEEGs in non-critically ill children displayed a wide range of amplitudes and bandwidths. Amplitudes were low during wakefulness and light sleep and high during deep sleep. Interpretation of pediatric aEEG background patterns must take into account the state of wakefulness in in clinical practice and research.

aEEG Use in Pediatric Critical Care-An Online Survey

Background: Evidence supporting continuous EEG monitoring in pediatric intensive care is increasing, but continuous full-channel EEG is a scarce resource. Amplitude-integrated EEG (aEEG) monitors are broadly available in children's hospitals due to their use in neonatology and can easily be applied to older patients. Objective: The aim of this survey was to evaluate the use of amplitude-integrated EEG in German and Swiss pediatric intensive care units (PICUs). Design: An online survey was sent to German and Swiss PICUs that were identified via databases provided by the German Pediatric Association (DGKJ) and the Swiss Society of Intensive Care (SGI). The questionnaire contained 18 multiple choice questions including the PICU size and specialization, indications for aEEG use, perceived benefits from aEEG, and data storage. Main results: Forty-three (26%) PICUs filled out the questionnaire. Two thirds of all interviewed PICUs use aEEG in non-neonates. Main indications were neurological complications or disease and altered mental state. Features assessed were mostly seizures and side differences, less frequently height of amplitude and background pattern. Interpretation of raw EEG also played an important role. All interviewees would appreciate the establishment of reference values for toddlers and children. Conclusions: aEEG is used in a large proportion of the interviewed PICUs. The wide-spread use without validation of data generates the need for further evaluation of this technique and the establishment of reference values for non-neonates.