Resident training and interrater agreements using the ACNS critical care EEG terminology

What Do You See? Signature Pedagogy in Continuous Electroencephalography Teaching

PURPOSE

Electroencephalography (EEG) is commonly used in neurology, but there is variability in how neurologists interpret EEGs, potentially from variability in EEG teaching. Little is known about how EEG teaching is done to prepare neurologists for professional practice.

METHODS

We interviewed a group of EEG experts to characterize their teaching practices around continuous EEG (cEEG). We used signature pedagogy as a framework to analyze and interpret the data.

RESULTS

We identified pervasive and characteristic forms of cEEG teaching. Teaching is based on apprenticeship, relying on "learning by doing" in the context of real-life clinical practice. There are habitual steps that learners take to anchor teaching, which typically occurs during rounds. There is a common language and core knowledge that trainees need to master early in their training.

CONCLUSIONS

There are pervasive characteristic forms of cEEG teaching. These findings can help facilitate instructional design and implementation of complementary or enhanced cEEG teaching practices.

Pitfalls in scalp EEG: Current obstacles and future directions

Although electroencephalography (EEG) serves a critical role in the evaluation and management of seizure disorders, it is commonly misinterpreted, resulting in avoidable medical, social, and financial burdens to patients and health care systems. Overinterpretation of sharply contoured transient waveforms as being representative of interictal epileptiform abnormalities lies at the core of this problem. However, the magnitude of these errors is amplified by the high prevalence of paroxysmal events exhibited in clinical practice that compel investigation with EEG. Neurology training programs, which vary considerably both in the degree of exposure to EEG and the composition of EEG didactics, have not effectively addressed this widespread issue. Implementation of competency-based curricula in lieu of traditional educational approaches may enhance proficiency in EEG interpretation amongst general neurologists in the absence of formal subspecialty training. Efforts in this regard have led to the development of a systematic, high-fidelity approach to the interpretation of epileptiform discharges that is readily employable across medical centers. Additionally, machine learning techniques hold promise for accelerating accurate and reliable EEG interpretation, particularly in settings where subspecialty interpretive EEG services are not readily available. This review highlights common diagnostic errors in EEG interpretation, limitations in current educational paradigms, and initiatives aimed at resolving these challenges.

SARS-CoV-2 alters neural synchronies in the brain with more severe effects in younger individuals

Coronavirus disease secondary to infection by SARS-CoV-2 (COVID19 or C19) causes respiratory illness, as well as severe neurological symptoms that have not been fully characterized. In a previous study, we developed a computational pipeline for the automated, rapid, high-throughput and objective analysis of electroencephalography (EEG) rhythms. In this retrospective study, we used this pipeline to define the quantitative EEG changes in patients with a PCR-positive diagnosis of C19 (n = 31) in the intensive care unit (ICU) of Cleveland Clinic, compared to a group of age-matched PCR-negative (n = 38) control patients in the same ICU setting. Qualitative assessment of EEG by two independent teams of electroencephalographers confirmed prior reports with regards to the high prevalence of diffuse encephalopathy in C19 patients, although the diagnosis of encephalopathy was inconsistent between teams. Quantitative analysis of EEG showed distinct slowing of brain rhythms in C19 patients compared to control (enhanced delta power and attenuated alpha-beta power). Surprisingly, these C19-related changes in EEG power were more prominent in patients below age 70. Moreover, machine learning algorithms showed consistently higher accuracy in the binary classification of patients as C19 versus control using EEG power for subjects below age 70 compared to older ones, providing further evidence for the more severe impact of SARS-CoV-2 on brain rhythms in younger individuals irrespective of PCR diagnosis or symptomatology, and raising concerns over potential long-term effects of C19 on brain physiology in the adult population and the utility of EEG monitoring in C19 patients.

The development of an automated machine learning pipeline for the detection of Alzheimer's Disease

Although Alzheimer's disease is the most prevalent form of dementia, there are no treatments capable of slowing disease progression. A lack of reliable disease endpoints and/or biomarkers contributes in part to the absence of effective therapies. Using machine learning to analyze EEG offers a possible solution to overcome many of the limitations of current diagnostic modalities. Here we develop a logistic regression model with an accuracy of 81% that addresses many of the shortcomings of previous works. To our knowledge, no other study has been able to solve the following problems simultaneously: (1) a lack of automation and unbiased removal of artifacts, (2) a dependence on a high level of expertise in data pre-processing and ML for non-automated processes, (3) the need for very large sample sizes and accurate EEG source localization using high density systems, (4) and a reliance on black box ML approaches such as deep neural nets with unexplainable feature selection. This study presents a proof-of-concept for an automated and scalable technology that could potentially be used to diagnose AD in clinical settings as an adjunct to conventional neuropsychological testing, thus enhancing efficiency, reproducibility, and practicality of AD diagnosis.

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.

The Past, Present, and Future of Tele-EEG

Tele-electroencephalogram (EEG) has become more pervasive over the last 20 years due to advances in technology, both independent of and driven by personnel shortages. The professionalization of EEG services has both limited growth and controlled the quality of tele-EEG. Growing data on the conditions that benefit from brain monitoring have informed increased critical care EEG and ambulatory EEG utilization. Guidelines that marshal responsible use of still-limited resources and changes in broadband and billing practices have also shaped the tele-EEG landscape. It is helpful to characterize the drivers of tele-EEG to navigate barriers to sustainable growth and to build dynamic systems that anticipate challenges in any of the domains that expand access and enhance quality of these diagnostic services. We explore the historical factors and current trends in tele-EEG in the United States in this review.

Resident Use of EEG Cap System to Rule Out Nonconvulsive Status Epilepticus

BACKGROUND

Nonconvulsive status epilepticus (NCSE) requires an EEG for diagnosis and in many centers access may be limited. The authors aimed to test whether neurology residents can be trained to use and interpret full-montage EEGs using an EEG cap electrode system to detect NCSE while on-call.

METHODS

Neurology residents were trained to interpret EEG recordings using the American Clinical Neurophysiology Society critical care EEG terminology. Residents who achieved a score of 70% or higher in the American Clinical Neurophysiology Society certification test and attended a training session were eligible to use the EEG cap on-call with patients suspected of having NCSE. Residents' experience and interpretation of observed EEG patterns were evaluated using a questionnaire. Each EEG recording was independently reviewed by three epilepsy specialists to determine the interpretability of each study and whether the residents correctly identified the EEG patterns.

RESULTS

Sixteen residents undertook the training and 12 (75%) achieved a score of 70% or higher on the certification test. Seven of these residents performed 14 EEG cap studies between August 2017 and May 2018. The percent agreement between residents and electroencephalographers was 78.6% for EEG interpretability and 57.1% for description of EEG pattern. Residents did not miss any malignant patterns concerning for NCSE, which accounted for 1 of 14 EEGs but "overcalled" patterns as malignant in 3 of 14 recordings.

CONCLUSIONS

This study suggests that neurology residents can be taught to perform and interpret EEGs using a cap system to monitor for NCSE. Additional training will help improve EEG interpretation and sensitivity.

Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis

Epilepsy diagnosis based on Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms (EEGs) is laborious and often subjective. Therefore, it is necessary to build an effective IED detector and an automatic method to classify IED-free versus IED EEGs. In this study, we evaluate features that may provide reliable IED detection and EEG classification. Specifically, we investigate the IED detector based on convolutional neural network (ConvNet) with different input features (temporal, spectral, and wavelet features). We explore different ConvNet architectures and types, including 1D (one-dimensional) ConvNet, 2D (two-dimensional) ConvNet, and noise injection at various layers. We evaluate the EEG classification performance on five independent datasets. The 1D ConvNet with preprocessed full-frequency EEG signal and frequency bands (delta, theta, alpha, beta) with Gaussian additive noise at the output layer achieved the best IED detection results with a false detection rate of 0.23/min at 90% sensitivity. The EEG classification system obtained a mean EEG classification Leave-One-Institution-Out (LOIO) cross-validation (CV) balanced accuracy (BAC) of 78.1% (area under the curve (AUC) of 0.839) and Leave-One-Subject-Out (LOSO) CV BAC of 79.5% (AUC of 0.856). Since the proposed classification system only takes a few seconds to analyze a 30-min routine EEG, it may help in reducing the human effort required for epilepsy diagnosis.

Prospective evaluation of interrater agreement between EEG technologists and neurophysiologists

We aim to prospectively investigate, in a large and heterogeneous population, the electroencephalogram (EEG)-reading performances of EEG technologists. A total of 8 EEG technologists and 5 certified neurophysiologists independently analyzed 20-min EEG recordings. Interrater agreement (IRA) for predefined EEG pattern identification between EEG technologists and neurophysiologits was assessed using percentage of agreement (PA) and Gwet-AC1. Among 1528 EEG recordings, the PA [95% confidence interval] and interrater agreement (IRA, AC1) values were as follows: status epilepticus (SE) and seizures, 97% [96–98%], AC1 kappa = 0.97; interictal epileptiform discharges, 78% [76–80%], AC1 = 0.63; and conclusion dichotomized as “normal” versus “pathological”, 83.6% [82–86%], AC1 = 0.71. EEG technologists identified SE and seizures with 99% [98–99%] negative predictive value, whereas the positive predictive values (PPVs) were 48% [34–62%] and 35% [20–53%], respectively. The PPV for normal EEGs was 72% [68–76%]. SE and seizure detection were impaired in poorly cooperating patients (SE and seizures; p < 0.001), intubated and older patients (SE; p < 0.001), and confirmed epilepsy patients (seizures; p = 0.004). EEG technologists identified ictal features with few false negatives but high false positives, and identified normal EEGs with good PPV. The absence of ictal features reported by EEG technologists can be reassuring; however, EEG traces should be reviewed by neurophysiologists before taking action.

Structure and Outcomes of Educational Programs for Training Non-electroencephalographers in Performing and Screening Adult EEG: A Systematic Review

OBJECTIVE

To qualitatively and quantitatively summarize curricula, teaching methods, and effectiveness of educational programs for training bedside care providers (non-experts) in the performance and screening of adult electroencephalography (EEG) for nonconvulsive seizures and other patterns.

METHODS

PRISMA methodological standards were followed. MEDLINE, EMBASE, Cochrane, CINAHL, WOS, Scopus, and MedEdPORTAL databases were searched from inception until February 26, 2020 with no restrictions. Abstract and full-text review was completed in duplicate. Studies were included if they were original research; involved non-experts performing, troubleshooting, or screening adult EEG; and provided qualitative descriptions of curricula and teaching methods and/or quantitative assessment of non-experts (vs gold standard EEG performance by neurodiagnostic technologists or interpretation by neurophysiologists). Data were extracted in duplicate. A content analysis and a meta-narrative review were performed.

RESULTS

Of 2430 abstracts, 35 studies were included. Sensitivity and specificity of seizure identification varied from 38 to 100% and 65 to 100% for raw EEG; 40 to 93% and 38 to 95% for quantitative EEG, and 95 to 100% and 65 to 85% for sonified EEG, respectively. Non-expert performance of EEG resulted in statistically significant reduced delay (86 min, p < 0.0001; 196 min, p < 0.0001; 667 min, p < 0.005) in EEG completion and changes in management in approximately 40% of patients. Non-experts who were trained included physicians, nurses, neurodiagnostic technicians, and medical students. Numerous teaching methods were utilized and often combined, with instructional and hands-on training being most common.

CONCLUSIONS

Several different bedside providers can be educated to perform and screen adult EEG, particularly for the purpose of diagnosing nonconvulsive seizures. While further rigorous research is warranted, this review demonstrates several potential bridges by which EEG may be integrated into the care of critically ill patients.

Automated Adult Epilepsy Diagnostic Tool Based on Interictal Scalp Electroencephalogram Characteristics: A Six-Center Study

The diagnosis of epilepsy often relies on a reading of routine scalp electroencephalograms (EEGs). Since seizures are highly unlikely to be detected in a routine scalp EEG, the primary diagnosis depends heavily on the visual evaluation of Interictal Epileptiform Discharges (IEDs). This process is tedious, expert-centered, and delays the treatment plan. Consequently, the development of an automated, fast, and reliable epileptic EEG diagnostic system is essential. In this study, we propose a system to classify EEG as epileptic or normal based on multiple modalities extracted from the interictal EEG. The ensemble system consists of three components: a Convolutional Neural Network (CNN)-based IED detector, a Template Matching (TM)-based IED detector, and a spectral feature-based classifier. We evaluate the system on datasets from six centers from the USA, Singapore, and India. The system yields a mean Leave-One-Institution-Out (LOIO) cross-validation (CV) area under curve (AUC) of 0.826 (balanced accuracy (BAC) of 76.1%) and Leave-One-Subject-Out (LOSO) CV AUC of 0.812 (BAC of 74.8%). The LOIO results are found to be similar to the interrater agreement (IRA) reported in the literature for epileptic EEG classification. Moreover, as the proposed system can process routine EEGs in a few seconds, it may aid the clinicians in diagnosing epilepsy efficiently.

The effectiveness of neurology resident EEG training for seizure recognition in critically ill patients

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Neurology residents were trained to read EEG through lectures and epilepsy rotation.

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Seizure recognition in critical ill patients is challenging.

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Improvement of EEG interpretation requires.

EEG monitoring in the ICU is essential for diagnosing seizures in critically ill patients. Neurology residents are the frontline for rapid diagnosis of seizures. Residents received EEG training through didactic lectures and their epilepsy rotations. We hypothesized that seizure recognition was dependent on epilepsy rotation, not the seniority of the residency. Residents were taught ACNS Standardized Critical Care EEG Terminology, unified EEG terminology and criteria for non-convulsive status epilepticus. EEG segments were given to residents for seizure recognition, and explanations provided to residents after each test. Anonymous results with the postgraduate training year (PGY) and time spent in epilepsy rotation were collected. These tests were conducted 3 times, with total of 48 EEG segments, between October, 2017 and May, 2019. There were 43 participates, including 4 PGY-1 (9.3%), 20 PGY-2 (46.5%), 12 PGY-3 (27.9%), and 7 PGY-4 (16.3%) residents. The mean rate of seizure recognition was 57.1% in PGY-1, 63.8% in PGY-2, 58.4% in PGY-3, and 70.1% in PGY-4. Comparing the duration of epilepsy rotations, the mean correct scores of seizure recognition were 58.6%, 64.6%, 64.4%, and 67.3% for duration at 0, 0.5, 1, and 2 months respectively. There was no significant difference regarding the PGY or the time of epilepsy rotation statistically by ANOVA (p = 0.37). Seizure recognition in the EEG of a critically ill patient is not solely dependent time spent in epilepsy rotation or stage of residency training. EEG interpretation skill may require an alternate approach, and continuous training.