Non-neurophysiologist Physicians and Nurses Can Detect Subclinical Seizures in Children Using a Panel of Quantitative EEG Trends and a Seizure Detection Algorithm

Competence of healthcare professionals performing electroencephalography test: A systematic review

Objective

To describe the EEG competence of healthcare professionals and how this competence has been measured in previous literature.

Methods

A systematic review following the preferred Reporting Items for Systematic Reviews and Meta-Analyses. A literature search was conducted in CINAHL, PubMed, Scopus, and Web of Science databases focusing on studies that empirically examined the EEG competence of healthcare professionals.

Results

A total of 28 studies were included. EEG competence consists of two main categories: knowledge and skills of EEG, and attitudes and values towards EEG. The EEG competence of healthcare professionals was assessed in three different settings: tests, simulations, and real life. The data collection methods were knowledge tests, self-assessments, and observations. The tools were developed by a researcher(s) for the single study and were not psychometrically tested.

Conclusion

EEG competence is a multidimensional concept that includes knowledge, skills, attitudes, and values that need to be considered when defining EEG competence and developing tools to measure it.

Significance

This systematic review provides information to the educators of healthcare professionals and healthcare organizations involved in developing comprehensive EEG training programs and assessments to foster professional development and ensure reliable diagnostic test results for patients.

Unveiling variability: A systematic review of reproducibility in visual EEG analysis, with focus on seizures

OBJECTIVE

Reproducibility is key for diagnostic tests involving subjective evaluation by experts. Our aim was to systematically review the reproducibility of visual analysis in clinical electroencephalogram (EEG). In this paper, we give data on the scope of EEG features found, and detailed reproducibility data for the most studied feature.

METHODS

We searched four databases for articles reporting reproducibility in clinical EEG, until June 2023. Two raters screened 24 553 citations, and then 2736 full texts. Quality was assessed according to the GRRAS guidelines.

RESULTS

We found 275 studies (268 interrater and 20 intrarater), addressing 606 different EEG features. Only 38 EEG features had been studied in >2 studies. Most studies had <50 patients and EEGs. The most often addressed feature was seizure detection (62 papers). Interrater reproducibility of seizure detection was substantial-to-almost-perfect with experienced raters and raw EEG (kappa .62-.88). With experienced raters and transformed EEG, reproducibility was substantial (kappa .63-.70). Inexperienced raters had lower reproducibility. Seizure lateralization reproducibility was moderate to substantial (kappa .58-.77) but lower than for seizure detection.

SIGNIFICANCE

Most EEG reproducibility studies are done only once. Intrarater studies are rare. The reproducibility of visual EEG analysis is variable. Interrater reproducibility for seizure detection is substantial-to-perfect with experienced raters and raw EEG, less with inexperienced raters or transformed EEG. The results of visual EEG analysis vary within the same rater, and between raters. There is a need for larger collaborative studies, using improved methodology, as well as more intrarater studies of EEG interpretation.

Update in Pediatric Neurocritical Care: What a Neurologist Caring for Critically Ill Children Needs to Know

Currently nearly one-quarter of admissions to pediatric intensive care units (PICUs) worldwide are for neurocritical care diagnoses that are associated with significant morbidity and mortality. Pediatric neurocritical care is a rapidly evolving field with unique challenges due to not only age-related responses to primary neurologic insults and their treatments but also the rarity of pediatric neurocritical care conditions at any given institution. The structure of pediatric neurocritical care services therefore is most commonly a collaborative model where critical care medicine physicians coordinate care and are supported by a multidisciplinary team of pediatric subspecialists, including neurologists. While pediatric neurocritical care lies at the intersection between critical care and the neurosciences, this narrative review focuses on the most common clinical scenarios encountered by pediatric neurologists as consultants in the PICU and synthesizes the recent evidence, best practices, and ongoing research in these cases. We provide an in-depth review of (1) the evaluation and management of abnormal movements (seizures/status epilepticus and status dystonicus); (2) acute weakness and paralysis (focusing on pediatric stroke and select pediatric neuroimmune conditions); (3) neuromonitoring modalities using a pathophysiology-driven approach; (4) neuroprotective strategies for which there is evidence (e.g., pediatric severe traumatic brain injury, post-cardiac arrest care, and ischemic stroke and hemorrhagic stroke); and (5) best practices for neuroprognostication in pediatric traumatic brain injury, cardiac arrest, and disorders of consciousness, with highlights of the 2023 updates on Brain Death/Death by Neurological Criteria. Our review of the current state of pediatric neurocritical care from the viewpoint of what a pediatric neurologist in the PICU needs to know is intended to improve knowledge for providers at the bedside with the goal of better patient care and 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.

Review of Noninvasive Neuromonitoring Modalities in Children II: EEG, qEEG

Critically ill children with acute neurologic dysfunction are at risk for a variety of complications that can be detected by noninvasive bedside neuromonitoring. Continuous electroencephalography (cEEG) is the most widely available and utilized form of neuromonitoring in the pediatric intensive care unit. In this article, we review the role of cEEG and the emerging role of quantitative EEG (qEEG) in this patient population. cEEG has long been established as the gold standard for detecting seizures in critically ill children and assessing treatment response, and its role in background assessment and neuroprognostication after brain injury is also discussed. We explore the emerging utility of both cEEG and qEEG as biomarkers of degree of cerebral dysfunction after specific injuries and their ability to detect both neurologic deterioration and improvement.

Landscape and future directions of machine learning applications in closed-loop brain stimulation

Brain stimulation (BStim) encompasses multiple modalities (e.g., deep brain stimulation, responsive neurostimulation) that utilize electrodes implanted in deep brain structures to treat neurological disorders. Currently, BStim is primarily used to treat movement disorders such as Parkinson's, though indications are expanding to include neuropsychiatric disorders like depression and schizophrenia. Traditional BStim systems are "open-loop" and deliver constant electrical stimulation based on manually-determined parameters. Advancements in BStim have enabled development of "closed-loop" systems that analyze neural biomarkers (e.g., local field potentials in the sub-thalamic nucleus) and adjust electrical modulation in a dynamic, patient-specific, and energy efficient manner. These closed-loop systems enable real-time, context-specific stimulation adjustment to reduce symptom burden. Machine learning (ML) has emerged as a vital component in designing these closed-loop systems as ML models can predict / identify presence of disease symptoms based on neural activity and adaptively learn to modulate stimulation. We queried the US National Library of Medicine PubMed database to understand the role of ML in developing closed-loop BStim systems to treat epilepsy, movement disorders, and neuropsychiatric disorders. Both neural and non-neural network ML algorithms have successfully been leveraged to create closed-loop systems that perform comparably to open-loop systems. For disorders in which the underlying neural pathophysiology is relatively well understood (e.g., Parkinson's, essential tremor), most work has involved refining ML models that can classify neural signals as aberrant or normal. The same is seen for epilepsy, where most current research has focused on identifying optimal ML model design and integrating closed-loop systems into existing devices. For neuropsychiatric disorders, where the underlying pathologic neural circuitry is still being investigated, research is focused on identifying biomarkers (e.g., local field potentials from brain nuclei) that ML models can use to identify onset of symptoms and stratify severity of disease.

Spectrograms for Seizure Detection in Critically Ill Children

SUMMARY

Electrographic seizures are common in critically ill children and a significant proportion of these seizures are nonconvulsive. There is an association between electrographic seizures and neurophysiological disturbances, worse short- and long-term neurologic outcomes, and mortality in critically ill patients. In this context, timely diagnosis and treatment of electrographic seizures in critically ill children becomes important. However, most institutions lack the resources to support round-the-clock or frequent review of continuous EEG recordings causing significant delays in seizure diagnosis. Given the current gaps in review of continuous EEG across institutions globally, use of visually simplified, time-compressed quantitative EEG trends such as spectrograms has the potential to enhance timeliness of seizure diagnosis and treatment in critically ill children.

Alterations in coordinated EEG activity precede the development of seizures in comatose children

OBJECTIVE

We aimed to test the hypothesis that computational features of the first several minutes of EEG recording can be used to estimate the risk for development of acute seizures in comatose critically-ill children.

METHODS

In a prospective cohort of 118 comatose children, we computed features of the first five minutes of artifact-free EEG recording (spectral power, inter-regional synchronization and cross-frequency coupling) and tested if these features could help identify the 25 children who went on to develop acute symptomatic seizures during the subsequent 48 hours of cEEG monitoring.

RESULTS

Children who developed acute seizures demonstrated higher average spectral power, particularly in the theta frequency range, and distinct patterns of inter-regional connectivity, characterized by greater connectivity at delta and theta frequencies, but weaker connectivity at beta and low gamma frequencies. Subgroup analyses among the 97 children with the same baseline EEG background pattern (generalized slowing) yielded qualitatively and quantitatively similar results.

CONCLUSIONS

These computational features could be applied to baseline EEG recordings to identify critically-ill children at high risk for acute symptomatic seizures.

SIGNIFICANCE

If confirmed in independent prospective cohorts, these features would merit incorporation into a decision support system in order to optimize diagnostic and therapeutic management of seizures among comatose children.