Sensitivity of quantitative EEG for seizure identification in the intensive care unit

Survey of Continuous EEG Monitoring Practices in the United States

OBJECTIVE

Continuous EEG (cEEG) practice has markedly changed over the last decade given its utility in improving critical care outcomes. However, there are limited data describing the current cEEG infrastructure in US hospitals.

METHODS

A web-based cEEG practice survey was sent to neurophysiologists at 123 ACGME-accredited epilepsy or clinical neurophysiology programs.

RESULTS

Neurophysiologists from 100 (81.3%) institutions completed the survey. Most institutions had 3 to 10 EEG faculty (80.0%), 1 to 5 fellows (74.8%), ≥6 technologists (84.9%), and provided coverage to neurology ICUs with >10 patients (71.0%) at a time. Round-the-clock EEG technologist coverage was available at most (90.0%) institutions with technologists mostly being in-house (68.0%). Most institutions without after-hours coverage (8 of 10) attributed this to insufficient technologists. The typical monitoring duration was 24 to 48 hours (23.0 and 40.0%), most commonly for subclinical seizures (68.4%) and spell characterization (11.2%). Larger neurology ICUs had more EEG technologists ( p = 0.02), fellows ( p = 0.001), and quantitative EEG use ( p = 0.001).

CONCLUSIONS

This survey explores current cEEG practice patterns in the United States. Larger centers had more technologists and fellows. Overall technologist numbers are stable over time, but with a move toward more in-hospital compared with home-based coverage. Reduced availability of EEG technologists was a major factor limiting cEEG availability at some centers.

Advancing Neurocritical Care with Artificial Intelligence and Machine Learning: The Promise, Practicalities, and Pitfalls ahead

Expansion of artificial intelligence (AI) in the field of medicine is changing the paradigm of clinical practice at a rapid pace. Incorporation of AI in medicine offers new tools as well as challenges, and physicians and learners need to adapt to assimilate AI into practice and education. AI can expedite early diagnosis and intervention with real-time multimodal monitoring. AI assistants can decrease the clerical burden of heath care improving the productivity of work force while mitigating burnout. There are still no regulatory parameters for use of AI and regulatory framework is needed for the implementation of AI systems in medicine to ensure transparency, accountability, and equitable access.

Accuracy of a Rapid-Response EEG's Automated Seizure-Burden Estimator: AccuRASE Study

BACKGROUND AND OBJECTIVES

The use of rapid response EEG (rr-EEG) has recently expanded in limited-resource settings and as a supplement to conventional EEG to rapidly detect and treat nonconvulsive status epilepticus. The study objective was to test the accuracy of an rr-EEG's automated seizure burden estimator (ASBE).

METHODS

This is a retrospective observational study using multiple blinded reviewers. All consecutive clinical rr-EEG procedures performed between November 2019 and February 2021 at Yale New Haven Hospital, one affiliated community hospital, and one affiliated inner-city regional hospital were included. Three reviewers blindly reviewed each EEG. The reference standard was 2/3 agreement. The co-primary outcome measures were the negative predictive value (NPV) of the ASBE for the detection of electrographic status epilepticus (ESE) or possible ESE (ESE/pESE) (to be used as a screening method to exclude ESE without the need for urgent expert review) and the positive predictive value (PPV, to be used for immediate treatment without requiring urgent expert review). These were assessed using a variety of seizure burden cutoffs determined by the algorithm (>1%, >10%, >20%, >50%, and >90%).

RESULTS

In the first 2 hours, a >10% burden cutoff detected 86% (95% CI 42%-100%) of studies with ESE alone and 88% (68%-97%) with ESE/pESE; this >10% cutoff had a NPV of 99% (97%-100%) for ESE and 98% (95%-100%) for ESE/pESE. The specificity at this threshold was 79% (73%-84%) for ESE and 84% (79%-89%) for ESE/pESE, but the PPV was low at 11% (4%-23%) for ESE and 39% (26%-53%) for ESE/pESE. A >90% burden cutoff was 97% (94%-99%) specific for detecting ESE (PPV 33% [7%-70%]) and 99% (97%-100%) specific for detecting ESE/pESE [PPV 78% (40%-97%)], although the sensitivity dropped significantly to 29% (13%-51%) for ESE/pESE and 43% (10%-82%) for ESE at the >90% threshold.

DISCUSSION

The ASBE has high specificity at >90% seizure burden threshold for detecting ESE and ESE/pESE, with good PPV for ESE/pESE, though with only low-to-moderate sensitivity; at this threshold, it can be used to help triage patients for immediate treatment/transfer, urgent expert review, and additional CEEG. A >10% threshold has a high sensitivity, detecting approximately 85% of patients with ESE; at this lower cutoff, it can be used as a screening tool to exclude ESE with >95% NPV.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that ASBE software can reliably exclude ESE (98% negative predictive value using a <10% burden cutoff) without expert review in most patients requiring rapid response EEG.

Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury

Electroencephalography (EEG) is invaluable in the management of acute neurological emergencies. Characteristic EEG changes have been identified in diverse neurologic conditions including stroke, trauma, and anoxia, and the increased utilization of continuous EEG (cEEG) has identified potentially harmful activity even in patients without overt clinical signs or neurologic diagnoses. Manual annotation by expert neurophysiologists is a major resource limitation in investigating the prognostic and therapeutic implications of these EEG patterns and in expanding EEG use to a broader set of patients who are likely to benefit. Artificial intelligence (AI) has already demonstrated clinical success in guiding cEEG allocation for patients at risk for seizures, and its potential uses in neurocritical care are expanding alongside improvements in AI itself. We review both current clinical uses of AI for EEG-guided management as well as ongoing research directions in automated seizure and ischemia detection, neurologic prognostication, and guidance of medical and surgical treatment.

Utility of Quantitative EEG in Neurological Emergencies and ICU Clinical Practice

The electroencephalogram (EEG) is a cornerstone tool for the diagnosis, management, and prognosis of selected patient populations. EEGs offer significant advantages such as high temporal resolution, real-time cortical function assessment, and bedside usability. The quantitative EEG (qEEG) added the possibility of long recordings being processed in a compressive manner, making EEG revision more efficient for experienced users, and more friendly for new ones. Recent advancements in commercially available software, such as Persyst, have significantly expanded and facilitated the use of qEEGs, marking the beginning of a new era in its application. As a result, there has been a notable increase in the practical, real-world utilization of qEEGs in recent years. This paper aims to provide an overview of the current applications of qEEGs in daily neurological emergencies and ICU practice, and some elementary principles of qEEGs using Persyst software in clinical settings. This article illustrates basic qEEG patterns encountered in critical care and adopts the new terminology proposed for spectrogram reporting.

Advances in Electroencephalographic Biomarkers of Neonatal Hypoxic Ischemic Encephalopathy

Electroencephalography (EEG) is a key objective biomarker of newborn brain function, delivering critical, cotside insights to aid the management of encephalopathy. Access to continuous EEG is limited, forcing reliance on subjective clinical assessments. In hypoxia ischaemia, the primary cause of encephalopathy, alterations in EEG patterns correlate with. injury severity and evolution. As HIE evolves, causing secondary neuronal death, EEG can track injury progression, informing neuroprotective strategies, seizure management and prognosis. Despite its value, challenges with interpretation and lack of on site expertise has limited its broader adoption. Technological advances, particularly in digital EEG and machine learning, are enhancing real-time analysis. This will allow EEG to expand its role in HIE diagnosis, management and outcome prediction.

Economic aspects of prolonged home video-EEG monitoring: a simulation study

INTRODUCTION

Video EEG monitoring (VEM) is an important tool for characterizing clinical events suspected as seizures. It is also used for pre-surgical workups in patients with drug-resistant epilepsy (DRE). In-hospital VEM high cost, long admission waiting periods and some other inconveniences led to an interest in home VEM (HVEM). However, because antiseizure medications cannot be reduced at home, HVEM may require longer monitoring. While the economic aspect is one of the main motivations for HVEM, the cost of HVEM lasting several weeks has not been assessed.

METHODS

We modeled the cost of HVEM for 8 weeks and compared it to the cost of 1-week in-hospital VEM. Additionally, we modeled the per-patient cost for a combination of HVEM and in-hospital VEM, considering that if in a proportion of patients HVEM fails to achieve its goal, they should undergo in-hospital VEM with drug reduction.

RESULTS

The average cost of HVEM up to 4-6 weeks of monitoring was lower than that for the 1-week in-hospital VEM. Combining the 3-week HVEM with 1-week in-hospital VEM (if needed) reduced the per-patient cost by 6.6-28.6% as compared to the situation when all the patients with DRE were referred to the in-hospital VEM.

CONCLUSIONS

A prolonged intermittent HVEM can be cost-effective, especially if the minimal seizure frequency is about one seizure per week. The study findings support directing efforts into clinical trials and technology development.

A nomogram based on quantitative EEG to predict the prognosis of nontraumatic coma patients in the neuro-intensive care unit

OBJECTIVE

We aimed to establish a quantitative electroencephalography-based prognostic prediction model specifically tailored for nontraumatic coma patients to guide clinical work.

METHODS

This retrospective study included 126 patients with nontraumatic coma admitted to the First Affiliated Hospital of Chongqing Medical University from December 2020 to December 2022. Six in-hospital deaths were excluded. The Glasgow Outcome Scale assessed the prognosis at 3 months after discharge. The least absolute shrinkage and selection operator regression analysis and stepwise regression method were applied to select the most relevant predictors. We developed a predictive model using binary logistic regression and then presented it as a nomogram. We assessed the predictive effectiveness and clinical utility of the model.

RESULTS

After excluding six deaths that occurred within the hospital, a total of 120 patients were included in this study. Three predictor variables were identified, including APACHE II score [39.129 (1.4244-1074.9000)], sleep cycle [OR: 0.006 (0.0002-0.1808)], and RAV [0.068 (0.0049-0.9500)]. The prognostic prediction model showed exceptional discriminative ability, with an AUC of 0.939 (95 % CI: 0.899-0.979).

CONCLUSION

A lack of sleep cycles, smaller relative alpha variants, and higher APACHE II scores were associated with a poor prognosis of nontraumatic coma patients in the neurointensive care unit at 3 months after discharge.

CLINICAL IMPLICATION

This study presents a novel methodology for the prognostic assessment of nontraumatic coma patients and is anticipated to play a significant role in clinical practice.

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.

Status epilepticus: what's new for the intensivist

PURPOSE OF REVIEW

Status epilepticus (SE) is a common neurologic emergency affecting about 36.1/100 000 person-years that frequently requires intensive care unit (ICU) admission. There have been advances in our understanding of epidemiology, pathophysiology, and EEG monitoring of SE, and there have been large-scale treatment trials, discussed in this review.

RECENT FINDINGS

Recent changes in the definitions of SE have helped guide management protocols and we have much better predictors of outcome. Observational studies have confirmed the efficacy of benzodiazepines and large treatment trials indicate that all routinely used second line treatments (i.e., levetiracetam, valproate and fosphenytoin) are equally effective. Better understanding of the pathophysiology has indicated that nonanti-seizure medications aimed at underlying pathological processes should perhaps be considered in the treatment of SE; already immunosuppressant treatments are being more widely used in particular for new onset refractory status epilepticus (NORSE) and Febrile infection-related epilepsy syndrome (FIRES) that sometimes revealed autoimmune or paraneoplastic encephalitis. Growing evidence for ICU EEG monitoring and major advances in automated analysis of the EEG could help intensivist to assess the control of electrographic seizures.

SUMMARY

Research into the morbi-mortality of SE has highlighted the potential devastating effects of this condition, emphasizing the need for rapid and aggressive treatment, with particular attention to cardiorespiratory and neurological complications. Although we now have a good evidence-base for the initial status epilepticus management, the best treatments for the later stages are still unclear and clinical trials of potentially disease-modifying therapies are long overdue.

SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables

This work presents SeizFt-a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by interpretable sleep staging, our novel approach employs a unique combination of data augmentation, meaningful feature extraction, and an ensemble of decision trees to improve resilience to variations in EEG and to increase the capacity to generalize to unseen data. Fourier Transform (FT) Surrogates were utilized to increase sample size and improve the class balance between labeled non-seizure and seizure epochs. To enhance model stability and accuracy, SeizFt utilizes an ensemble of decision trees through the CatBoost classifier to classify each second of EEG recording as seizure or non-seizure. The SeizIt1 dataset was used for training, and the SeizIt2 dataset for validation and testing. Model performance for seizure detection was evaluated using two primary metrics: sensitivity using the any-overlap method (OVLP) and False Alarm (FA) rate using epoch-based scoring (EPOCH). Notably, SeizFt placed first among an array of state-of-the-art seizure detection algorithms as part of the Seizure Detection Grand Challenge at the 2023 International Conference on Acoustics, Speech, and Signal Processing (ICASSP). SeizFt outperformed state-of-the-art black-box models in accurate seizure detection and minimized false alarms, obtaining a total score of 40.15, combining OVLP and EPOCH across two tasks and representing an improvement of ~30% from the next best approach. The interpretability of SeizFt is a key advantage, as it fosters trust and accountability among healthcare professionals. The most predictive seizure detection features extracted from SeizFt were: delta wave, interquartile range, standard deviation, total absolute power, theta wave, the ratio of delta to theta, binned entropy, Hjorth complexity, delta + theta, and Higuchi fractal dimension. In conclusion, the successful application of SeizFt to wearable SensorDot data suggests its potential for real-time, continuous monitoring to improve personalized medicine for epilepsy.

Advanced Neuromonitoring Modalities on the Horizon: Detection and Management of Acute Brain Injury in Children

Timely detection and monitoring of acute brain injury in children is essential to mitigate causes of injury and prevent secondary insults. Increasing survival in critically ill children has emphasized the importance of neuroprotective management strategies for long-term quality of life. In emergent and critical care settings, traditional neuroimaging modalities, such as computed tomography and magnetic resonance imaging (MRI), remain frontline diagnostic techniques to detect acute brain injury. Although detection of structural and anatomical abnormalities remains crucial, advanced MRI sequences assessing functional alterations in cerebral physiology provide unique diagnostic utility. Head ultrasound has emerged as a portable neuroimaging modality for point-of-care diagnosis via assessments of anatomical and perfusion abnormalities. Application of electroencephalography and near-infrared spectroscopy provides the opportunity for real-time detection and goal-directed management of neurological abnormalities at the bedside. In this review, we describe recent technological advancements in these neurodiagnostic modalities and elaborate on their current and potential utility in the detection and management of acute brain injury.

Tonic Seizures in a Patient With Lennox-Gastaut Syndrome Manifest as "Icicles" Rather Than "Flames" on Quantitative EEG Analysis

SUMMARY

Quantitative analysis of continuous electroencephalography (QEEG) is increasingly being used to augment seizure detection in critically ill patients. Typically, seizures manifest on QEEG as abrupt increases in power and frequency, a visual pattern often called "flames." Here, we present a case of a 16-year-old patient with intractable Lennox-Gastaut syndrome secondary to a pathogenic variant in the SCN2A gene who had tonic seizures that manifest as abrupt decreases in power on QEEG, a visual pattern we term "icicles." Recognition of QEEG patterns representative of different seizure types is important as QEEG use becomes more widespread including in pediatric populations.

Multimodal monitoring: practical recommendations (dos and don'ts) in challenging situations and uncertainty

With the advancements in modern medicine, new methods are being developed to monitor patients in the intensive care unit. Different modalities evaluate different aspects of the patient's physiology and clinical status. The complexity of these modalities often restricts their use to the realm of clinical research, thereby limiting their use in the real world. Understanding their salient features and their limitations can aid physicians in interpreting the concomitant information provided by multiple modalities to make informed decisions that may affect clinical care and outcomes. Here, we present a review of the commonly used methods in the neurological intensive care unit with practical recommendations for their use.

Macroperiodic Oscillations Are Associated With Seizures Following Acquired Brain Injury in Young Children

PURPOSE

Seizures occur in 10% to 40% of critically ill children. We describe a phenomenon seen on color density spectral array but not raw EEG associated with seizures and acquired brain injury in pediatric patients.

METHODS

We reviewed EEGs of 541 children admitted to an intensive care unit between October 2015 and August 2018. We identified 38 children (7%) with a periodic pattern on color density spectral array that oscillates every 2 to 5 minutes and was not apparent on the raw EEG tracing, termed macroperiodic oscillations (MOs). Internal validity measures and interrater agreement were assessed. We compared demographic and clinical data between those with and without MOs.

RESULTS

Interrater reliability yielded a strong agreement for MOs identification (kappa: 0.778 [0.542-1.000]; P < 0.0001). There was a 76% overlap in the start and stop times of MOs among reviewers. All patients with MOs had seizures as opposed to 22.5% of the general intensive care unit monitoring population ( P < 0.0001). Macroperiodic oscillations occurred before or in the midst of recurrent seizures. Patients with MOs were younger (median of 8 vs. 208 days; P < 0.001), with indications for EEG monitoring more likely to be clinical seizures (42 vs. 16%; P < 0.001) or traumatic brain injury (16 vs. 5%, P < 0.01) and had fewer premorbid neurologic conditions (10.5 vs. 33%; P < 0.01).

CONCLUSIONS

Macroperiodic oscillations are a slow periodic pattern occurring over a longer time scale than periodic discharges in pediatric intensive care unit patients. This pattern is associated with seizures in young patients with acquired brain injuries.

Electroencephalogram in the intensive care unit: a focused look at acute brain injury

Over the past decades, electroencephalography (EEG) has become a widely applied and highly sophisticated brain monitoring tool in a variety of intensive care unit (ICU) settings. The most common indication for EEG monitoring currently is the management of refractory status epilepticus. In addition, a number of studies have associated frequent seizures, including nonconvulsive status epilepticus (NCSE), with worsening secondary brain injury and with worse outcomes. With the widespread utilization of EEG (spot and continuous EEG), rhythmic and periodic patterns that do not fulfill strict seizure criteria have been identified, epidemiologically quantified, and linked to pathophysiological events across a wide spectrum of critical and acute illnesses, including acute brain injury. Increasingly, EEG is not just qualitatively described, but also quantitatively analyzed together with other modalities to generate innovative measurements with possible clinical relevance. In this review, we discuss the current knowledge and emerging applications of EEG in the ICU, including seizure detection, ischemia monitoring, detection of cortical spreading depolarizations, assessment of consciousness and prognostication. We also review some technical aspects and challenges of using EEG in the ICU including the logistics of setting up ICU EEG monitoring in resource-limited settings.

Utilization of quantitative electroencephalogram in China: an online questionnaire survey

Background

Quantitative electroencephalogram (QEEG) is a tool that uses a computer to analyze brain activity monitored by electroencephalogram (EEG) according to measurements such as frequency, amplitude, and slope. The purpose of this study was to understand the current situation of QEEG utilization in China and further compare the situations among different regions and different levels of hospitals.

Methods

An online questionnaire comprising 14 questions was designed. Statistical description and analysis were made for the results of the questionnaire survey.

Results

A total of 158 people from 134 medical institutions participated in the survey. The participants came from 21 provinces, accounting for 61.76% (21/34) of the 34 provincial administrative regions in China. The Eastern China region accounted for 66.42% (89/134) of all the medical institutions that participated in this survey. Among the institutions surveyed, QEEG was routinely used in only 23.88% (32/134) of them. Among the medical institutions in which QEEG was routinely used, 87.50% (28/32) of them were 3A-grade hospitals. Among the institutions with routine use of QEEG, 56.25% (18/32) were affiliated hospitals of medical schools. There was a significant difference in the utilization of QEEG between the 3A-grade and non-3A-grade hospitals (P = 0.040) and between the hospitals affiliated to medical schools and those non-affiliated to medical schools (P = 0.020).

Conclusions

The utilization of QEEG is still limited in China. There are differences in the use of QEEG among different hospitals and regions.

Quantitative EEG parameters can improve the predictive value of the non-traumatic neurological ICU patient prognosis through the machine learning method

Background

Better outcome prediction could assist in reliable classification of the illnesses in neurological intensive care unit (ICU) severity to support clinical decision-making. We developed a multifactorial model including quantitative electroencephalography (QEEG) parameters for outcome prediction of patients in neurological ICU.

Methods

We retrospectively analyzed neurological ICU patients from November 2018 to November 2021. We used 3-month mortality as the outcome. Prediction models were created using a linear discriminant analysis (LDA) based on QEEG parameters, APACHEII score, and clinically relevant features. Additionally, we compared our best models with APACHEII score and Glasgow Coma Scale (GCS). The DeLong test was carried out to compare the ROC curves in different models.

Results

A total of 110 patients were included and divided into a training set (n=80) and a validation set (n = 30). The best performing model had an AUC of 0.85 in the training set and an AUC of 0.82 in the validation set, which were better than that of GCS (training set 0.64, validation set 0.61). Models in which we selected only the 4 best QEEG parameters had an AUC of 0.77 in the training set and an AUC of 0.71 in the validation set, which were similar to that of APACHEII (training set 0.75, validation set 0.73). The models also identified the relative importance of each feature.

Conclusion

Multifactorial machine learning models using QEEG parameters, clinical data, and APACHEII score have a better potential to predict 3-month mortality in non-traumatic patients in neurological ICU.

Processed Electroencephalogram-Based Monitoring to Guide Sedation in Critically Ill Adult Patients: Recommendations from an International Expert Panel-Based Consensus

BACKGROUND

The use of processed electroencephalography (pEEG) for depth of sedation (DOS) monitoring is increasing in anesthesia; however, how to use of this type of monitoring for critical care adult patients within the intensive care unit (ICU) remains unclear.

METHODS

A multidisciplinary panel of international experts consisting of 21 clinicians involved in monitoring DOS in ICU patients was carefully selected on the basis of their expertise in neurocritical care and neuroanesthesiology. Panelists were assigned four domains (techniques for electroencephalography [EEG] monitoring, patient selection, use of the EEG monitors, competency, and training the principles of pEEG monitoring) from which a list of questions and statements was created to be addressed. A Delphi method based on iterative approach was used to produce the final statements. Statements were classified as highly appropriate or highly inappropriate (median rating ≥ 8), appropriate (median rating ≥ 7 but < 8), or uncertain (median rating < 7) and with a strong disagreement index (DI) (DI < 0.5) or weak DI (DI ≥ 0.5 but < 1) consensus.

RESULTS

According to the statements evaluated by the panel, frontal pEEG (which includes a continuous colored density spectrogram) has been considered adequate to monitor the level of sedation (strong consensus), and it is recommended by the panel that all sedated patients (paralyzed or nonparalyzed) unfit for clinical evaluation would benefit from DOS monitoring (strong consensus) after a specific training program has been performed by the ICU staff. To cover the gap between knowledge/rational and routine application, some barriers must be broken, including lack of knowledge, validation for prolonged sedation, standardization between monitors based on different EEG analysis algorithms, and economic issues.

CONCLUSIONS

Evidence on using DOS monitors in ICU is still scarce, and further research is required to better define the benefits of using pEEG. This consensus highlights that some critically ill patients may benefit from this type of neuromonitoring.

Adult Critical Care Electroencephalography Monitoring for Seizures: A Narrative Review

Electroencephalography (EEG) is an important and relatively inexpensive tool that allows intensivists to monitor cerebral activity of critically ill patients in real time. Seizure detection in patients with and without acute brain injury is the primary reason to obtain an EEG in the Intensive Care Unit (ICU). In response to the increased demand of EEG, advances in quantitative EEG (qEEG) created an approach to review large amounts of data instantly. Finally, rapid response EEG is now available to reduce the time to detect electrographic seizures in limited-resource settings. This review article provides a concise overview of the technical aspects of EEG monitoring for seizures, clinical indications for EEG, the various available modalities of EEG, common and challenging EEG patterns, and barriers to EEG monitoring in the ICU.

Comparative analysis of background EEG activity based on MRI findings in neonatal hypoxic-ischemic encephalopathy: a standardized, low-resolution, brain electromagnetic tomography (sLORETA) study

Background

It is important to assess the degree of brain injury and predict long-term outcomes in neonates diagnosed with hypoxic-ischemic encephalopathy (HIE). However, routine studies, including magnetic resonance imaging (MRI) and conventional encephalography (EEG) or amplitude-integrated EEG (aEEG), have their own limitations in terms of availability and accuracy of evaluation. Recently, quantitative EEG (qEEG) has been shown to improve the predictive reliability of neonatal HIE and has been further refined with brain mapping techniques.

Methods

We investigated background EEG activities in 29 neonates with HIE who experienced therapeutic hypothermia, via qEEG using a distributed source model. MRI images were evaluated and classified into two groups (normal-to-mild injury vs moderate-to-severe injury), based on a scoring system. Non-parametric statistical analysis using standardized low-resolution brain electromagnetic tomography was performed to compare the current density distribution of four frequency bands (delta, theta, alpha, and beta) between the two groups.

Results

Electrical neuronal activities were significantly lower in the moderate-to-severe injury group compared with the normal-to-mild injury group. Background EEG activities in moderate-to-severe HIE were most significantly reduced in the temporal and parietal lobes. Quantitative EEG also revealed a decrease in background activity at all frequency bands, with a maximum in decrease in the delta component. The maximum difference in current density was found in the inferior parietal lobule of the right parietal lobe for the delta frequency band.

Conclusions

Our study demonstrated quantitative and topographical changes in EEG in moderate-to-severe neonatal HIE. They also suggest possible implementation and evaluation of conventional EEG and aEEG in neonatal HIE. The findings have implications as biomarkers in the assessment of neonatal HIE.

A Primer on EEG Spectrograms

SUMMARY

As continuous brain monitoring becomes a routine part of clinical care, continuous EEG has allowed better detection and characterization of nonconvulsive seizures, and patterns along the ictal-interictal continuum in critically ill patients. However, this increased workload has led many to turn to quantitative EEG whose central tool is the "spectrogram." Although in relatively wide use, many clinicians lack a detailed understanding of how spectrograms relate to the underlying "raw" EEG signal. This article provides an approachable set of first principles to help clinicians understand how spectrograms encode information about the raw EEG and how to interpret spectrograms to efficiently infer underlying EEG patterns.

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.

Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021

BACKGROUND

With the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis.

METHODS

QEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis.

RESULTS

A total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends.

CONCLUSION

The present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.

Electrographic Seizure Detection by Neuroscience Intensive Care Unit Nurses via Bedside Real-Time Quantitative EEG

Objective

Our primary objective was to determine the performance of real-time neuroscience intensive care unit (neuro-ICU) nurse interpretation of quantitative EEG (qEEG) at the bedside for seizure detection. Secondary objectives included determining nurse time to seizure detection and assessing factors that influenced nurse accuracy.

Methods

Nurses caring for neuro-ICU patients undergoing continuous EEG (cEEG) were trained using a 1-hour qEEG panel (rhythmicity spectrogram and amplitude-integrated EEG) bedside display. Nurses' hourly interpretations were compared with post hoc cEEG review by 2 neurophysiologists as the gold standard. Diagnostic performance, time to seizure detection compared with standard of care (SOC), and effects of other factors on nurse accuracy were calculated.

Results

A total of 109 patients and 65 nurses were studied. Eight patients had seizures during the study period (7%). Nurse sensitivity and specificity for the detection of seizures were 74% and 92%, respectively. Mean nurse time to seizure detection was significantly shorter than SOC by 132 minutes (Cox proportional hazard ratio 6.96). Inaccurate nurse interpretation was associated with increased hours monitored and presence of brief rhythmic discharges.

Conclusions

This prospective study of real-time nurse interpretation of qEEG for seizure detection in neuro-ICU patients showed clinically adequate sensitivity and specificity. Time to seizure detection was less than that of SOC.

Trial Registration Information

Clinical trial registration number NCT02082873.

Classification of Evidence

This study provides Class I evidence that neuro-ICU nurse interpretation of qEEG detects seizures in adults with a sensitivity of 74% and a specificity of 92% compared with traditional cEEG review.

Utility of Quantitative EEG for Seizure Detection in Adults

SUMMARY

Traditional review of EEG for seizure detection requires time and the expertise of a trained neurophysiologist; therefore, it is time- and resource-intensive. Quantitative EEG (qEEG) encompasses a variety of methods to make EEG review more efficient and allows for nonexpert review. Literature supports that qEEG is commonly used by neurophysiologists and nonexperts in clinical practice. In this review, the different types of qEEG trends and spectrograms used for seizure detection in adults, from basic concepts to clinical applications, are discussed. The merits and drawbacks of the most common qEEG trends are detailed. The authors detail the retrospective literature on qEEG sensitivity, specificity, and false alarm rate as interpreted by experts and nonexperts alike. Finally, the authors discuss the future of qEEG as a useful screening tool and speculate on the trajectory of future investigations in the field.

Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit

Background: Using machine learning to combine wrist accelerometer (ACM) and electrodermal activity (EDA) has been shown effective to detect primarily and secondarily generalized tonic-clonic seizures, here termed as convulsive seizures (CS). A prospective study was conducted for the FDA clearance of an ACM and EDA-based CS-detection device based on a predefined machine learning algorithm. Here we present its performance on pediatric and adult patients in epilepsy monitoring units (EMUs).

Methods: Patients diagnosed with epilepsy participated in a prospective multi-center clinical study. Three board-certified neurologists independently labeled CS from video-EEG. The Detection Algorithm was evaluated in terms of Sensitivity and false alarm rate per 24 h-worn (FAR) on all the data and on only periods of rest. Performance were analyzed also applying the Detection Algorithm offline, with a less sensitive but more specific parameters configuration (“Active mode”).

Results: Data from 152 patients (429 days) were used for performance evaluation (85 pediatric aged 6–20 years, and 67 adult aged 21–63 years). Thirty-six patients (18 pediatric) experienced a total of 66 CS (35 pediatric). The Sensitivity (corrected for clustered data) was 0.92, with a 95% confidence interval (CI) of [0.85-1.00] for the pediatric population, not significantly different (p > 0.05) from the adult population's Sensitivity (0.94, CI: [0.89–1.00]). The FAR on the pediatric population was 1.26 (CI: [0.87–1.73]), higher (p < 0.001) than in the adult population (0.57, CI: [0.36–0.81]). Using the Active mode, the FAR decreased by 68% while reducing Sensitivity to 0.95 across the population. During rest periods, the FAR's were 0 for all patients, lower than during activity periods (p < 0.001).

Conclusions: Performance complies with FDA's requirements of a lower bound of CI for Sensitivity higher than 0.7 and of a FAR lower than 2, for both age groups. The pediatric FAR was higher than the adult FAR, likely due to higher pediatric activity. The high Sensitivity and precision (having no false alarms) during sleep might help mitigate SUDEP risk by summoning caregiver intervention. The Active mode may be advantageous for some patients, reducing the impact of the FAR on daily life. Future work will examine the performance and usability outside of EMUs.

A Full-Stack Application for Detecting Seizures and Reducing Data During Continuous Electroencephalogram Monitoring

Supplemental Digital Content is available in the text.

BACKGROUND:

Continuous electroencephalogram monitoring is associated with lower mortality in critically ill patients; however, it is underused due to the resource-intensive nature of manually interpreting prolonged streams of continuous electroencephalogram data. Here, we present a novel real-time, machine learning–based alerting and monitoring system for epilepsy and seizures that dramatically reduces the amount of manual electroencephalogram review.

METHODS:

We developed a custom data reduction algorithm using a random forest and deployed it within an online cloud-based platform, which streams data and communicates interactively with caregivers via a web interface to display algorithm results. We developed real-time, machine learning–based alerting and monitoring system for epilepsy and seizures on continuous electroencephalogram recordings from 77 patients undergoing routine scalp ICU electroencephalogram monitoring and tested it on an additional 20 patients.

RESULTS

We achieved a mean seizure sensitivity of 84% in cross-validation and 85% in testing, as well as a mean specificity of 83% in cross-validation and 86% in testing, corresponding to a high level of data reduction. This study validates a platform for machine learning–assisted continuous electroencephalogram analysis and represents a meaningful step toward improving utility and decreasing cost of continuous electroencephalogram monitoring. We also make our high-quality annotated dataset of 97 ICU continuous electroencephalogram recordings public for others to validate and improve upon our methods.

Evaluation and Management of Seizures and Status Epilepticus

Seizures are frequently triggered by an inciting event and result from uninhibited excitation and/or decreased inhibition of a pool of neurons. If physiologic seizure abortive mechanisms fail, the ensuing unrestrained synchronization of neurons-status epilepticus-can be life-threatening and is associated with the potential for marked morbidity in survivors and high medical care costs. Prognosis is intimately related to etiology and its response to therapeutic measures. Timely implementation of pharmacologic therapy while concurrently performing a stepwise workup for etiology are paramount. Neurodiagnostic testing should guide titration of pharmacologic therapies, and help determine if there is a role for immune modulation.

What's new on EEG monitoring in the ICU

Continuous video-EEG (cEEG, lasting hours to several days) is increasingly used in ICU patients, as it is more sensitive than routine video-EEG (rEEG, lasting 20-30 min) to detect seizures or status epilepticus, and allows more frequent changes in therapeutic regimens. However, cEEG is more resource-consuming, and its relationship to outcome compared to repeated rEEG has only been formally assessed very recently in a randomized controlled trial, which did not show any significant difference in terms of long-term mortality or functional outcome. Awaiting more refined trials, it seems therefore that using repeated rEEG in ICU patients may represent a reasonable alternative in resource-limited settings. Prolonged EEG has been used recently in patients with severe COVID-19 infection, the proportion of seizures seems albeit relatively low, and similar to ICU patients with medical conditions. As in any case a timely EEG recording is recommended in the ICU, r ecent technical developments may ease its use in clinical practice.

Continuous EEG use and status epilepticus treatment in Australasia: a practice survey of Australian and New Zealand epileptologists

Objective

Continuous electroencephalography (cEEG) is increasingly used to detect non-convulsive seizures in critically ill patients but is not widely practised in Australasia. Use of cEEG is also influencing the management of status epilepticus (SE), which is rapidly evolving. We aimed to survey Australian and New Zealand cEEG use and current treatment of SE

Methods

A web-based survey was distributed to Epilepsy Society of Australia (ESA) members, between October and November 2019. Adult and paediatric neurologists/epileptologists with ESA membership involved in clinical epilepsy care and cEEG interpretation were invited to participate.

Results

Thirty-five paediatric/adult epileptologists completed the survey, 51% with over 10 years of consultant experience. cEEG was always available for only 31% of respondents, with the majority having no or only ad hoc access to cEEG. Lack of funding (74%) and personnel (71%) were the most common barriers to performing cEEG. Although experience with SE was common, responses varied regarding treatment approaches for both convulsive and non-convulsive SE. Escalation to anaesthetic treatment of convulsive SE tended to occur later than international guideline recommendations. There was general agreement that formal training in cEEG and national guidelines for SE/cEEG were needed.

Conclusions

cEEG availability remains limited in Australia, with lack of funding and resourcing being key commonly identified barriers. Current opinions on the use of cEEG and treatment of SE vary reflecting the complexity of management and a rapidly evolving field. An Australian-based guideline for the management of SE, including the role of cEEG is recommended.

Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings

Automated seizure detection in long-term video-EEG recordings is far from being integrated into common clinical practice. Here, we leverage classical and state-of-the-art complexity measures to robustly and automatically detect seizures from scalp recordings. Brain activity is scored through eight features, encompassing traditional time domain and novel measures of recurrence. A binary classification algorithm tailored to treat unbalanced dataset is used to determine whether a time window is ictal or non-ictal from its features. The application of the algorithm on a cohort of ten adult patients with focal refractory epilepsy indicates sensitivity, specificity, and accuracy of 90%, along with a true alarm rate of 95% and less than four false alarms per day. The proposed approach emphasizes ictal patterns against noisy background without the need of data preprocessing. Finally, we benchmark our approach against previous studies on two publicly available datasets, demonstrating the good performance of our algorithm.

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Complexity measures are formulated to enhance classical time-domain statistics of EEG

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The detection algorithm does not need ad-hoc data preprocessing to address artifacts

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Focal seizures are detected 95% of the time with less than four false alarms per day

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The approach offers a visual representation of a seizure as a time-evolving network

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

PURPOSE

This study evaluated the sensitivity of nonconvulsive seizure detection by non-neurophysiologist physicians and nurses using a panel of quantitative EEG (QEEG) trends in the setting of a pediatric intensive care unit.

METHODS

Forty-five 1-hour QEEG epochs were obtained retrospectively from 10 patients admitted to the McMaster Children's Hospital pediatric intensive care unit, which included 184 electrographic seizures. Each epoch constituted 4 QEEG trends, a seizure probability marker, automated seizure detector, rhythmicity spectrograms, and amplitude-integrated EEG. Six pediatric residents and 5 pediatric intensive care unit nurses analyzed the epochs for possible seizures after a 15-minute power point presentation. This was compared with the gold standard of a board-certified epileptologist interpreting the conventional EEG data for seizures.

RESULTS

Sensitivity of seizure detection for pediatric residents and intensive care unit nurses were 0.90. The specificity was 0.87 and 0.89, respectively. The interrater agreement among the pediatric residents was moderate with a kappa (κ) value of 0.45 (confidence interval: 0.41-0.49), and among the nurses were moderate with a κ value of 0.59 (confidence interval: 0.54-0.63). A post hoc analysis involving 2 neurophysiologists demonstrated a sensitivity of 0.90 and a specificity of 0.93 (confidence interval: 0.90-0.96) for seizure detection and a substantial interrater agreement of κ = 0.76 (confidence interval: 0.61-0.91).

CONCLUSIONS

A panel of QEEG trends can be used by non-neurophysiologists in a pediatric critical care setting to detect nonconvulsive seizures with a reasonable accuracy, which may expedite subclinical seizure identification and timely intervention.

The effects of oral administration of curcumin-galactomannan complex on brain waves are consistent with brain penetration: a randomized, double-blinded, placebo-controlled pilot study

OVERVIEW

A novel highly bioavailable curcumin-galactomannan (CGM) formulation was shown to have improved blood-brain-barrier (BBB) permeability of free curcuminoids in animal models; however, this has not been established in humans. The present study was conducted to determine the functional effects of CGM on brain waves in healthy individuals, owing to its BBB permeability.

METHODS

A total of 18 healthy volunteers aged 35-65 were randomly assigned to consume 500 mg CGM, Unformulated curcumin (UC) or Placebo capsules twice daily for 30 days. Electroencephalogram (EEG) measurements, audio-visual reaction time tests and a working memory test were conducted at baseline and after 30 days.

RESULTS

Supplementation of CGM resulted in a significant increase in α- and β-waves (p < 0.05) as well as a significant reduction in α/β ratio in comparison with unformulated curcumin and placebo groups. Furthermore, the CGM showed significant reduction in the audio-reaction time (29.8 %; p < 0.05) in comparison with placebo and 24.6% (p < 0.05) with unformulated curcumin. The choice-based visual-reaction time was also significantly decreased (36%) in CGM as compared to unformulated curcumin and placebo which produced 15.36% and 5.2% respectively.

CONCLUSION

The observed increase in α and β waves and reduction in α/β ratio in the CGM group suggest that CGM can influence the brain waves in healthy subjects in a manner consistent with penetration of the blood-brain-barrier. The EEG results correlated with improved audio-visual and working memory tests which further support the role of CGM on memory improvements and fatigue reduction.

IRIS: A Modular Platform for Continuous Monitoring and Caretaker Notification in the Intensive Care Unit

OBJECTIVE

New approaches are needed to interpret large amounts of physiologic data continuously recorded in the ICU. We developed and prospectively validated a versatile platform (IRIS) for real-time ICU physiologic monitoring, clinical decision making, and caretaker notification.

METHODS

IRIS was implemented in the neurointensive care unit to stream multimodal time series data, including EEG, intracranial pressure (ICP), and brain tissue oxygenation (PbtO2), from ICU monitors to an analysis server. IRIS was applied for 364 patients undergoing continuous EEG, 26 patients undergoing burst suppression monitoring, and four patients undergoing intracranial pressure and brain tissue oxygen monitoring. Custom algorithms were used to identify periods of elevated ICP, compute burst suppression ratios (BSRs), and detect faulty or disconnected EEG electrodes. Hospital staff were notified of clinically relevant events using our secure API to route alerts through a password-protected smartphone application.

RESULTS

Sustained increases in ICP and concordant decreases in PbtO2 were reliably detected using user-defined thresholds and alert throttling. BSR trends computed by the platform correlated highly with manual neurologist markings (r2 0.633-0.781; p < 0.0001). The platform identified EEG electrodes with poor signal quality with 95% positive predictive value, and reduced latency of technician response by 93%.

CONCLUSION

This study validates a flexible real-time platform for monitoring and interpreting ICU data and notifying caretakers of actionable results, with potential to reduce the manual burden of continuous monitoring services on care providers.

SIGNIFICANCE

This work represents an important step toward facilitating translational medical data analytics to improve patient care and reduce health care costs.

A Trial of Real-Time Electrographic Seizure Detection by Neuro-ICU Nurses Using a Panel of Quantitative EEG Trends

BACKGROUND

Non-convulsive seizures (NCS) are a common occurrence in the neurologic intensive care unit (Neuro-ICU) and are associated with worse outcomes. Continuous electroencephalogram (cEEG) monitoring is necessary for the detection of NCS; however, delays in interpretation are a barrier to early treatment. Quantitative EEG (qEEG) calculates a time-compressed simplified visual display from raw EEG data. This study aims to evaluate the performance of Neuro-ICU nurses utilizing bedside, real-time qEEG interpretation for detecting recurrent NCS.

METHODS

This is a prospective, single-institution study of patients admitted to the Duke Neuro-ICU between 2016 and 2018 who had NCS identified on traditional cEEG review. The accuracy of recurrent seizure detection on hourly qEEG review by bedside Neuro-ICU nurses was compared to the gold standard of cEEG interpretation by two board-certified neurophysiologists. The nurses first received brief qEEG training, individualized for their specific patient. The bedside qEEG display consisted of rhythmicity spectrogram (left and right hemispheres) and amplitude-integrated EEG (left and right hemispheres) in 1-h epochs.

RESULTS

Twenty patients were included and 174 1-h qEEG blocks were analyzed. Forty-seven blocks contained seizures (27%). The sensitivity was 85.1% (95% CI 71.1-93.1%), and the specificity was 89.8% (82.8-94.2%) for the detection of seizures for each 1-h block when compared to interpretation of conventional cEEG by two neurophysiologists. The false positive rate was 0.1/h. Hemispheric seizures (> 4 unilateral EEG electrodes) were more likely to be correctly identified by nurses on qEEG than focal seizures (≤ 4 unilateral electrodes) (p = 0.03).

CONCLUSIONS

After tailored training sessions, Neuro-ICU nurses demonstrated a good sensitivity for the interpretation of bedside real-time qEEG for the detection of recurrent NCS with a low false positive rate. qEEG is a promising tool that may be used by non-neurophysiologists and may lead to earlier detection of NCS.

Continuous Electroencephalography Monitoring in Critically Ill Infants and Children

Continuous video electroencephalography (CEEG) monitoring of critically ill infants and children has expanded rapidly in recent years. Indications for CEEG include evaluation of patients with altered mental status, characterization of paroxysmal events, and detection of electrographic seizures, including monitoring of patients with limited neurological examination or conditions that put them at high risk for electrographic seizures (e.g., cardiac arrest or extracorporeal membrane oxygenation cannulation). Depending on the inclusion criteria and clinical characteristics of the population studied, the percentage of pediatric patients with electrographic seizures varies from 7% to 46% and with electrographic status epilepticus from 1% to 23%. There is also evidence that epileptiform and background CEEG patterns may provide important information about prognosis in certain clinical populations. Quantitative EEG techniques are emerging as a tool to enhance the value of CEEG to provide real-time bedside data for management and prognosis. Continued research is needed to understand the clinical value of seizure detection and identification of other CEEG patterns on the outcomes of critically ill infants and children.

A standardized nomenclature for spectrogram EEG patterns: Inter-rater agreement and correspondence with common intensive care unit EEG patterns

OBJECTIVE

To determine the inter-rater agreement (IRA) of a standardized nomenclature for EEG spectrogram patterns, and to estimate the probability distribution of ictal-interictal continuum (IIC) patterns vs. other EEG patterns within each category in this nomenclature.

METHODS

We defined seven spectrogram categories: "Solid Flames", "Irregular Flames", "Broadband-monotonous", "Narrowband-monotonous", "Stripes", "Low power", and "Artifact". Ten electroencephalographers scored 115 spectrograms and the corresponding raw EEG samples. Gwet's agreement coefficient was used to calculate IRA.

RESULTS

Solid Flames represented seizures or IIC patterns 69.4% of the time. Irregular Flames represented seizures or IIC patterns 38.7% of the time. Broadband-monotonous primarily corresponded with seizures or IIC (54.3%) and Narrowband-monotonous with focal or generalized slowing (43.8%). Stripes were associated with burst-suppression (37.2%) and generalized suppression (34.4%). Low Power category was associated with generalized suppression (94%). There was "near perfect" agreement for Solid Flames (κ = 94.36), Low power (κ = 92.61), and Artifact (κ = 93.72). There was "substantial agreement" for all other categories (κ = 74.65-79.49).

CONCLUSIONS

This EEG spectrogram nomenclature has high IRA among electroencephalographers.

SIGNIFICANCE

The nomenclature can be a useful tool for EEG screening. Future studies are needed to determine if using this nomenclature shortens time to IIC identification, and how best to use it in practice to reduce time to intervention.

Validation of an algorithm of time-dependent electro-clinical risk stratification for electrographic seizures (TERSE) in critically ill patients

OBJECTIVE

The clinical implementation of continuous electroencephalography (CEEG) monitoring in critically ill patients is hampered by the substantial burden of work that it entails for clinical neurophysiologists. Solutions that might reduce this burden, including by shortening the duration of EEG to be recorded, would help its widespread adoption. Our aim was to validate a recently described algorithm of time-dependent electro-clinical risk stratification for electrographic seizure (ESz) (TERSE) based on simple clinical and EEG features.

METHODS

We retrospectively reviewed the medical records and EEG recordings of consecutive patients undergoing CEEG between October 1, 2015 and September, 30 2016 and assessed the sensitivity of TERSE for seizure detection, as well as the reduction in EEG time needed to be reviewed.

RESULTS

In a cohort of 407 patients and compared to full CEEG review, the model allowed the detection of 95% of patients with ESz and 97% of those with electrographic status epilepticus. The amount of CEEG to be recorded to detect ESz was reduced by two-thirds, compared to the duration of CEEG taht was actually recorded.

CONCLUSIONS

TERSE allowed accurate time-dependent ESz risk stratification with a high sensitivity for ESz detection, which could substantially reduce the amount of CEEG to be recorded and reviewed, if applied prospectively in clinical practice.

SIGNIFICANCE

Time-dependent electro-clinical risk stratification, such as TERSE, could allow more efficient practice of CEEG and its more widespread adoption. Future studies should aim to improve risk stratification in the subgroup of patients with acute brain injury and absence of clinical seizures.

Teaching Important Basic EEG Patterns of Bedside Electroencephalography to Critical Care Staffs: A Prospective Multicenter Study

BACKGROUND

Continuous electroencephalography (cEEG) is commonly recommended for neurocritical care patients. Routine implementation of such monitoring requires the specific training of professionals. The aim of this research was to evaluate the effectiveness of a training program on initiation of the basic interpretation of cEEG for critical care staff in a prospective multicenter study.

METHODS

After completion of a pretest, participants (senior physicians, fellows, residents, medical students, and nurses) recruited in six French ICUs participated in a face-to-face electroencephalogram (EEG) training program followed by additional e-learning sessions at day 1 (post-course), day 15, day 30, and day 90, based on training tests followed by illustrated and commented answers. Each test was designed to evaluate knowledge and skills through correct recognition of ten predefined EEG sequences covering the most common normal and abnormal patterns. The primary objective was to achieve a success rate > 80% correct answers at day 90 by at least 75% of the participants.

RESULTS

Among 250 participants, 77/108 (71.3%) who completed the full training program achieved at least 80% correct answers at day 90. Paired comparisons between the scores obtained at each evaluation showed an increase over time. The rate of correct answers at day 90 was > 80% for all common predefined EEG sequences, except for the recognition of periodic and burst-suppression patterns and reactivity, which were identified in only 42.6% (95% CI 36.4-48.8), 60.2% (54.1-66.3), and 70.4% (64.7-76.1) of the tests, respectively.

CONCLUSIONS

A training strategy for the basic interpretation of EEG in ICUs, consisting of a face-to-face EEG course supplemented with reinforcement of knowledge by e-learning, was associated with significant resignation and an effectiveness of training allowing 71% of learners to accurately recognize important basic EEG patterns encountered in critically ill patients.

TRIAL REGISTRATION

ClinicalTrials.gov number: NCT03545776.

Quantified EEG for the Characterization of Epileptic Seizures versus Periodic Activity in Critically Ill Patients

Epileptic seizures (ES) are frequent in critically ill patients and their detection and treatment are mandatory. However, sometimes it is quite difficult to discriminate between ES and non-epileptic bursts of periodic activity (BPA). Our aim was to characterize ES and BPA by means of quantified electroencephalography (qEEG). Records containing either ES or BPA were visually identified and divided into 1 s windows that were 10% overlapped. Differential channels were grouped by frontal, parieto-occipital and temporal lobes. For every channel and window, the power spectrum was calculated and the area for delta (0–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands and spectral entropy (Se) were computed. Mean values of percentage changes normalized to previous basal activity and standardized mean difference (SMD) for every lobe were computed. We have observed that BPA are characterized by a selective increment of delta activity and decrease in Se along the scalp. Focal seizures (FS) always propagated and were similar to generalized seizures (GS). In both cases, although delta and theta bands increased, the faster bands (alpha and beta) showed the highest increments (more than 4 times) without modifications in Se. We have defined the numerical features of ES and BPA, which can facilitate its clinical identification.