Single-channel amplitude integrated EEG recording for the identification of epileptic seizures by nonexpert physicians in the adult acute care setting

New wearable and portable EEG modalities in epilepsy: The views of hospital-based healthcare professionals

BACKGROUND

Novel mobile and portable EEG solutions, designed for short and long-term monitoring of individuals with epilepsy have been developed in recent years but, they are underutilized, lacking full integration into clinical routine. Exploring the opinions of hospital-based healthcare professionals regarding their potential application, technical requirements and value would be crucial for future device development and increase their clinical application.

PURPOSE

To evaluate professionals' opinions on novel EEG systems, focusing on their potential application in various clinical settings, professionals' interest in non-invasive solutions for ultra-long monitoring of people with epilepsy (PWE) and factors which could increase future use of novel EEG systems.

MATERIALS AND METHODS

We conducted an online survey where Hospital-based professionals shared opinions on potential advantages, clinical value, and key features of novel wearable EEG systems in five different clinical settings. Additionally, insights were gathered on the need for future research and, the need for additional information about devices from companies and researchers.

RESULTS

Respondents (n = 40) prioritized high performance, data quality, easy patient mobility, and comfort as crucial features for novel devices. Advantages were highlighted, including more natural settings, reduced application time, earlier epilepsy diagnosis, and decreased support requirements. Novel EEG devices were seen as valuable for epilepsy diagnosis, seizure monitoring, automatic seizure documentation, seizure alarms, and seizure forecasting. Interest in integrating these new systems into clinical practice was high, particularly for supervising drug-resistant epilepsy, reducing SUDEP, and detecting nocturnal seizures. Professionals emphasized the need for more research studies and highlighted the need for increased information from companies and researchers.

CONCLUSIONS

Professionals underscore specific technical and practical features, along with potential clinical advantages and value of novel EEG devices that could drive their development. While interest in integrating these solutions in clinical practice exists, further validation studies and enhanced communication between researchers, companies, and clinicians are crucial for overcoming potential scepticism and facilitating widespread adoption.

Quantitative EEG-Based Seizure Estimation in Super-Refractory Status Epilepticus

BACKGROUND

The objective of this study was to evaluate the accuracy of seizure burden in patients with super-refractory status epilepticus (SRSE) by using quantitative electroencephalography (qEEG).

METHODS

EEG recordings from 69 patients with SRSE (2009-2019) were reviewed and annotated for seizures by three groups of reviewers: two board-certified neurophysiologists using only raw EEG (gold standard), two neurocritical care providers with substantial experience in qEEG analysis (qEEG experts), and two inexperienced qEEG readers (qEEG novices) using only a qEEG trend panel.

RESULTS

Raw EEG experts identified 35 (51%) patients with seizures, accounting for 2950 seizures (3,126 min). qEEG experts had a sensitivity of 93%, a specificity of 61%, a false positive rate of 6.5 per day, and good agreement (κ = 0.64) between both qEEG experts. qEEG novices had a sensitivity of 98.5%, a specificity of 13%, a false positive rate of 15 per day, and fair agreement (κ = 0.4) between both qEEG novices. Seizure burden was not different between the qEEG experts and the gold standard (3,257 vs. 3,126 min), whereas qEEG novices reported higher burden (6066 vs. 3126 min).

CONCLUSIONS

Both qEEG experts and novices had a high sensitivity but a low specificity for seizure detection in patients with SRSE. qEEG could be a useful tool for qEEG experts to estimate seizure burden in patients with SRSE.

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.

Educational initiatives for electroencephalography in the critical care setting: a systematic review and meta-analysis

PURPOSE

We systematically reviewed existing critical care electroencephalography (EEG) educational programs for non-neurologists, with the primary goal of reporting the content covered, methods of instruction, overall duration, and participant experience. Our secondary goals were to assess the impact of EEG programs on participants' core knowledge, and the agreement between non-experts and experts for seizure identification.

SOURCE

Major databases were searched from inception to 30 August 2020. Randomized controlled trials, cohort studies, and descriptive studies were all considered if they reported an EEG curriculum for non-neurologists in a critical care setting. Data were presented thematically for the qualitative primary outcome and a meta-analysis using a random effects model was performed for the quantitative secondary outcomes.

PRINCIPAL FINDINGS

Twenty-nine studies were included after reviewing 7,486 citations. Twenty-two studies were single centre, 17 were from North America, and 16 were published after 2016. Most EEG studies were targeted to critical care nurses (17 studies), focused on processed forms of EEG with amplitude-integrated EEG being the most common (15 studies), and were shorter than one day in duration (24 studies). In pre-post studies, EEG programs significantly improved participants' knowledge of tested material (standardized mean change, 1.79; 95% confidence interval [CI], 0.86 to 2.73). Agreement for seizure identification between non-experts and experts was moderate (Cohen's kappa = 0.44; 95% CI, 0.27 to 0.60).

CONCLUSIONS

It is feasible to teach basic EEG to participants in critical care settings from different clinical backgrounds, including physicians and nurses. Brief training programs can enable bedside providers to recognize high-yield abnormalities such as non-convulsive seizures.

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.

Continuous EEG monitoring by a new simplified wireless headset in intensive care unit

Background

In critically ill patients continuous EEG (cEEG) is recommended in several conditions. Recently, a new wireless EEG headset (CerebAir®,Nihon-Kohden) is available. It has 8 electrodes, and its positioning seems to be easier than conventional systems.

Aim of this study was to evaluate the feasibility of this device for cEEG monitoring, if positioned by ICU physician.

Methods

Neurological patients were divided in two groups according with the admission to Neuro-ICU (Study-group:20 patients) or General-ICU (Control-group:20 patients). In Study group, cEEG was recorded by CerebAir® assembled by an ICU physician, while in Control group a simplified 8-electrodes-EEG recording positioned by an EEG technician was performed.

Results

Time for electrodes applying was shorter in Study-group than in Control-group: 6.2 ± 1.1′ vs 10.4 ± 2.3′; p < 0.0001. Thirty five interventions were necessary to correct artifacts in Study-group and 11 in Control-group. EEG abnormalities with or without epileptic meaning were respectively 7(35%) and 7(35%) in Study-group, and 5(25%) and 9(45%) in Control-group;p > 0.05. In Study-group, cEEG was interrupted for risk of skin lesions in 4 cases after 52 ± 4 h. cEEG was obtained without EEG technician in all cases in Study-group; quality of EEG was similar.

Conclusions

Although several limitations should be considered, this simplified EEG system could be feasible even if EEG technician was not present. It was faster to position if compared with standard techniques, and can be used for continuous EEG monitoring. It could be very useful as part of diagnostic process in an emergency setting.

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.

Evaluation of a new wireless technique for continuous electroencephalography monitoring in neurological intensive care patients

A novel wireless eight-channel electroencephalography (EEG) headset specially developed for ICUs was tested in regard of comparability with standard 10/20 EEG systems. The continuous EEG (cEEG) derivations via CerebAir EEG headset (Nihon Kohden Europe, Rosbach, Germany) and internationally standardized 10/20 reference EEGs as the diagnostic standard were performed in a mixed collective on a neurointensive care unit (neuro-ICU). The derivations were verified for comparability in detection of EEG background activity, epileptiform discharges, and seizure patterns. Fifty-two patients with vigilance reduction following serious neurological or metabolic diseases were included, and both methods were applied and further analyzed in 47. EEG background activity matched in 24 of 45 patients (53%; p = 0.126), epileptiform discharges matched in 32 (68%) patients (p = 0.162), and seizure activity matched in 98%. Overall, in 89% of the patients, cEEG detected the same or additional ICU-relevant EEG patterns. The tested wireless cEEG headset is a useful monitoring tool in patients with consciousness disorders. The present study indicates that long-term measurements with the wireless eight-channel cEEG lead to a higher seizure and epileptiform discharge detection compared to intermittent 10/20 EEG derivations in the ICU setting.

Diagnostic Reliability of Headset-Type Continuous Video EEG Monitoring for Detection of ICU Patterns and NCSE in Patients with Altered Mental Status with Unknown Etiology

BACKGROUND/OBJECTIVE

Simplified continuous electroencephalogram (cEEG) monitoring has shown improvement in detecting seizures; however, it is insufficient in detecting abnormal EEG patterns, such as periodic discharges (PDs), rhythmic delta activity (RDA), spikes and waves (SW), and continuous slow wave (CS), as well as nonconvulsive status epilepticus (NCSE). Headset-type continuous video EEG monitoring (HS-cv EEG monitoring; AE-120A EEG Headset™, Nihon Kohden, Tokyo, Japan) is a recently developed easy-to-use technology with eight channels. However, its ability to detect abnormal EEG patterns with raw EEG data has not been comprehensively evaluated. We aimed to examine the diagnostic accuracy of HS-cv EEG monitoring in detecting abnormal EEG patterns and NCSE in patients with altered mental status (AMS) with unknown etiology. We also evaluated the time required to initiate HS-cv EEG monitoring in these patients.

METHODS

We prospectively observed and retrospectively examined patients who were admitted with AMS between January and December 2017 at the neurointensive care unit at Asakadai Central General Hospital, Saitama, Japan. We excluded patients whose data were missing for various reasons, such as difficulties in recording, and those whose consciousness had recovered between HS-cv EEG and conventional cEEG (C-cEEG) monitoring. For the included patients, we performed HS-cv EEG monitoring followed by C-cEEG monitoring. Definitive diagnosis was confirmed by C-cEEG monitoring with the international 10-20 system. As the primary outcome, we verified the sensitivity and specificity of HS-cv EEG monitoring in detecting abnormal EEG patterns including PDs, RDA, SW, and CS, in detecting the presence of PDs, and in detecting NCSE. As the secondary outcome, we calculated the time to initiate HS-cv EEG monitoring after making the decision.

RESULTS

Fifty patients (76.9%) were included in the final analyses. The median age was 72 years, and 66% of the patients were male. The sensitivity and specificity of HS-cv EEG monitoring for detecting abnormal EEG patterns were 0.974 (0.865-0.999) and 0.909 (0.587-0.998), respectively, and for detecting PDs were 0.824 (0.566-0.926) and 0.970 (0.842-0.999), respectively. We diagnosed 13 (26%) patients with NCSE using HS-cv EEG monitoring and could detect NCSE with a sensitivity and specificity of 0.706 (0.440-0.897) and 0.970 (0.842-0.999), respectively. The median time needed to initiate HS-cv EEG was 57 min (5-142).

CONCLUSIONS

HS-cv EEG monitoring is highly reliable in detecting abnormal EEG patterns, with moderate reliability for PDs and NCSE, and rapidly initiates cEEG monitoring in patients with AMS with unknown etiology.

Bedside interpretation of simplified continuous EEG after cardiac arrest

BACKGROUND

Continuous EEG-monitoring (cEEG) in the ICU is recommended to assess prognosis and detect seizures after cardiac arrest but implementation is often limited by the lack of EEG-technicians and experts. The aim of the study was to assess ICU physicians ability to perform preliminary interpretations of a simplified cEEG in the post cardiac arrest setting.

METHODS

Five ICU physicians received training in interpretation of simplified cEEG - total training duration 1 day. The ICU physicians then interpreted 71 simplified cEEG recordings from 37 comatose survivors of cardiac arrest. The cEEG included amplitude-integrated EEG trends and two channels with original EEG-signals. Basic EEG background patterns and presence of epileptiform discharges or seizure activity were assessed on 5-grade rank-ordered scales based on standardized EEG terminology. An EEG-expert was used as reference.

RESULTS

There was substantial agreement (κ 0.69) for EEG background patterns and moderate agreement (κ 0.43) for epileptiform discharges between ICU physicians and the EEG-expert. Sensitivity for detecting seizure activity by ICU physicians was limited (50%), but with high specificity (87%).

CONCLUSIONS

After cardiac arrest, preliminary bedside interpretations of simplified cEEGs by trained ICU physicians may allow earlier detection of clinically relevant cEEG changes, prompting changes in patient management as well as additional evaluation by an EEG-expert. This strategy requires awareness of limitations of both the simplified electrode montage and the cEEG interpretations performed by ICU physicians. cEEG evaluation by an expert should not be delayed.

Seizure Identification by Critical Care Providers Using Quantitative Electroencephalography

OBJECTIVES

To compare the performance of critical care providers with that of electroencephalography experts in identifying seizures using quantitative electroencephalography display tools.

DESIGN

Diagnostic accuracy comparison among healthcare provider groups.

SETTING

Multispecialty quaternary children's hospital in Canada.

SUBJECTS

ICU fellows, ICU nurses, neurophysiologists, and electroencephalography technologists.

INTERVENTION

Two-hour standardized one-on-one training, followed by a supervised individual review of 27 continuous electroencephalography recordings with the task of identifying individual seizures on eight-channel amplitude-integrated electroencephalography and color density spectral array displays.

MEASUREMENTS AND MAIN RESULTS

Each participant reviewed 27 continuous electroencephalograms comprising 487 hours of recording containing a total of 553 seizures. Performance for seizure identification was compared among groups using a nested model analysis with adjustment for interparticipant variability within groups and collinearity among recordings. Using amplitude-integrated electroencephalography, sensitivity for seizure identification was comparable among ICU fellows (83.8%), ICU nurses (73.1%), and neurophysiologists (81.5%) but lower among electroencephalographic technologists (66.7%) (p = 0.003). Using color density spectral array, sensitivity was comparable among ICU fellows (82.4%), ICU nurses (88.2%), neurophysiologists (83.3%), and electroencephalographic technologists (73.3%) (p = 0.09). Daily false-positive rates were also comparable among ICU fellows (2.8 for amplitude-integrated electroencephalography, 7.7 for color density spectral array), ICU nurses (4.2, 7.1), neurophysiologists (1.2, 1.5), and electroencephalographic technologists (0, 0) (p = 0.41 for amplitude-integrated electroencephalography; p = 0.13 for color density spectral array). However, performance varied greatly across individual electroencephalogram recordings. Professional background generally played a greater role in determining performance than individual skill or electroencephalogram recording characteristics.

CONCLUSIONS

Following standardized training, critical care providers and electroencephalography experts displayed similar performance for identifying individual seizures using both amplitude-integrated electroencephalography and color density spectral array displays. Although these quantitative electroencephalographic trends show promise as a tool for bedside seizure screening by critical care providers, these findings require confirmation in a real-world ICU environment and in daily clinical use.

Early Diagnosis of Nonconvulsive Status Epilepticus Recurrence with Raw EEG of a Bispectral Index Monitor

Background

Seizures are frequent in ICU and their diagnosis is challenging, often delayed or missed. Their diagnosis requires a conventional EEG recording. When cEEG is not available, there is no consensus on how patients should be monitored when there is high risk of seizure. This case illustrates how a bispectral index monitor allowed an early diagnosis of an NCSE recurrence.

Case Presentation

A NCSE was diagnosed at the admission. cEEG was not available and then a bispectral index (BIS) monitor was placed and processed parameters were monitored as usual. During the first and second day, both conventional and BIS's EEG showed patterns of burst suppression and the BIS value varied between 25 and 35 while the suppression ratio (SR) varied between 20 and 35. On the third day, while hypnotic drugs were withdrawn progressively, raw EEG of the BIS monitor showed spikes, spikes waves, and polyspikes without significant variation of BIS and SR values. Even if processed parameters stayed between their usual ranges, the typical aspect of the real time EEG raised concern for NCSE recurrence. An unplanned conventional EEG recording was urgently requested, and the diagnosis was confirmed and treated.

Conclusion

Primitive and secondary brain injuries can lead to seizures which are often purely electrical. Even though BIS monitors cannot substitute the conventional EEG, processed parameters and raw EEG should be always analysed jointly. In the present case, seizure was suspected only on the aspect of real time EEG which showed spikes, spikes waves, and polyspikes.

Reduced electrode arrays for the automated detection of rhythmic and periodic patterns in the intensive care unit: Frequently tried, frequently failed?

OBJECTIVE

To investigate the effect of systematic electrode reduction from a common 10-20 EEG system on pattern detection sensitivity (SEN).

METHODS

Two reviewers rated 17130 one-minute segments of 83 prospectively recorded cEEGs according to the ACNS standardized critical care EEG terminology (CCET), including burst suppression patterns (BS) and unequivocal electrographic seizures. Consensus annotations between reviewers were used as a gold standard to determine pattern detection SEN and specificity (SPE) of a computational algorithm (baseline, 19 electrodes). Electrodes were than reduced one by one in four different variations. SENs and SPEs were calculated to determine the most beneficial assembly with respect to the number and location of electrodes.

RESULTS

High automated baseline SENs (84.99-93.39%) and SPEs (90.05-95.6%) were achieved for all patterns. Best overall results in detecting BS and CCET patterns were found using the "hairline+vertex" montage. While the "forehead+behind ear" montage showed an advantage in detecting ictal patterns, reaching a 15% drop of SEN with 10 electrodes, all montages could detect BS sufficiently if at least nine electrodes were available.

CONCLUSION

For the first time an automated approach was used to systematically evaluate the effect of electrode reduction on pattern detection SEN in cEEG.

SIGNIFICANCE

Prediction of the expected detection SEN of specific EEG patterns with reduced EEG montages in ICU patients.

Utilization of Quantitative EEG Trends for Critical Care Continuous EEG Monitoring: A Survey of Neurophysiologists

PURPOSE

Quantitative EEG (QEEG) can be used to assist with review of large amounts of data generated by critical care continuous EEG monitoring. This study aimed to identify current practices regarding the use of QEEG in critical care continuous EEG monitoring of critical care patients.

METHODS

An online survey was sent to 796 members of the American Clinical Neurophysiology Society (ACNS), instructing only neurophysiologists to participate.

RESULTS

The survey was completed by 75 neurophysiologists that use QEEG in their practice. Survey respondents reported that neurophysiologists and neurophysiology fellows are most likely to serve as QEEG readers (97% and 52%, respectively). However, 21% of respondents reported nonneurophysiologists are also involved with QEEG interpretation. The majority of nonneurophysiologist QEEG data review is aimed to alert neurophysiologists to periods of concern, but 22% reported that nonneurophysiologists use QEEG to directly guide clinical care. Quantitative EEG was used most frequently for seizure detection (92%) and burst suppression monitoring (59%). A smaller number of respondents use QEEG for monitoring the depth of sedation (29%), ischemia detection (28%), vasospasm detection (28%) and prognosis after cardiac arrest (21%). About half of the respondents do not review every page of the raw critical care continuous EEG record when using QEEG. Respondents prefer a panel of QEEG trends displayed as hemispheric data, when applicable. There is substantial variability regarding QEEG trend preferences for seizure detection and ischemia detection.

CONCLUSIONS

QEEG is being used by neurophysiologists and nonneurophysiologists for applications beyond seizure detection, but practice patterns vary widely. There is a need for standardization of QEEG methods and practices.

Continuous electroencephalography in a mixed non-neurological intensive care population, an observational study

PURPOSE

Continuous electroencephalography (cEEG) improves monitoring of the brain in unconscious patients, but implementation at ICU is difficult. The present investigation shows a way to introduce cEEG at an anesthesiological ICU and discusses the first experiences.

MATERIALS AND METHODS

The study analyzed the feasibility of cEEG, assessed the interpretable cEEG time, importance of automatic seizure detection, the incidence of seizures, the predominant background EEG activity, incidence of delirium and mortality.

RESULTS

Fifty-three cEEGs of 50 patients with a median interpretable length of 24 hours [IQR 20 to 42 hours] were recorded. One patient had status epilepticus, while 5 patients had non-convulsive seizures. Automated seizure detection recognized the status epilepticus and 3 of 10 non-convulsive seizures, however, detected 42 false positive seizures. Predominant background EEG activity was alpha (9%), theta (17%), delta (26%), burst-suppression (17%), and suppressed background activity (30%). EEG activity correlated neither with dosage of analgo-sedative drugs nor with incidence of delirium or mortality.

CONCLUSION

Continuous electroencephalography recording is feasible and manageable. Automatic seizure detection was often false negative/positive; therefore, the interpretation of the cEEG should be supported by EEG-trained neurologists. Background EEG activity was not associated with outcome parameters, which suggests that background activity is a poor outcome predictor.

Real-time seizure prediction using RLS filtering and interpolated histogram feature based on hybrid optimization algorithm of Bayesian classifier and Hunting search

BACKGROUND AND OBJECTIVES

Epileptic seizure prediction using EEG signal analysis is an important application for drug therapy and pediatric patient monitoring. Time series estimation to obtain the future samples of EEG signal has vital role for detecting seizure attack. In this paper, a novel density-based real-time seizure prediction algorithm based on a trained offline seizure detection algorithm is proposed.

METHODS

In the offline seizure detection procedure, after signal preprocessing, histogram-based statistical features are extracted from signal probability distribution. By defining a deterministic polynomial model on the normalized histogram, a novel syntactic feature that is named Interpolated Histogram Feature (IHF) is proposed. Moreover, with this feature, Seizure Distribution Model (SDM) as a descriptor of the seizure and non-seizure signals is presented. By using a novel hybrid optimization algorithm based on Bayesian classifier and Hunting Search (HuS) algorithm, the optimal features are selected. To detect the seizure attacks in the online mode, a Multi-Layer Perceptron (MLP) classifier is trained with the optimal features in the offline procedure. For online prediction, the enhanced Recursive Least Square (RLS) filter is applied to estimate sample-by-sample of the EEG signal. Also, a density-based signal tracking scenario is introduced to update and tune the parameters of RLS filtering algorithm.

RESULTS

Our prediction algorithm is evaluated on 104 hours of EEG signals recorded from 23 pediatric patients. Our online signal prediction algorithm provides the accuracy rate of 86.56% and precision rate of 86.53% simultaneously using the trained MLP classifier from the offline mode. The recall rate of seizure prediction is 97.27% and the false prediction rate of 0.00215 per hour is achieved as well. Ultimately, the future samples of EEG signal are estimated, and the time of seizure signal prediction is also converged to 6.64 seconds.

CONCLUSION

In our proposed real-time algorithm, by implementing a density-based signal tracking scenario, the future samples of signal with suitable time is predicted and the seizure is detected based on the optimal features from the IHF and histogram-based statistical features with acceptable performance.

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

OBJECTIVE

To evaluate the sensitivity of quantitative EEG (QEEG) for electrographic seizure identification in the intensive care unit (ICU).

METHODS

Six-hour EEG epochs chosen from 15 patients underwent transformation into QEEG displays. Each epoch was reviewed in 3 formats: raw EEG, QEEG + raw, and QEEG-only. Epochs were also analyzed by a proprietary seizure detection algorithm. Nine neurophysiologists reviewed raw EEGs to identify seizures to serve as the gold standard. Nine other neurophysiologists with experience in QEEG evaluated the epochs in QEEG formats, with and without concomitant raw EEG. Sensitivity and false-positive rates (FPRs) for seizure identification were calculated and median review time assessed.

RESULTS

Mean sensitivity for seizure identification ranged from 51% to 67% for QEEG-only and 63%-68% for QEEG + raw. FPRs averaged 1/h for QEEG-only and 0.5/h for QEEG + raw. Mean sensitivity of seizure probability software was 26.2%-26.7%, with FPR of 0.07/h. Epochs with the highest sensitivities contained frequent, intermittent seizures. Lower sensitivities were seen with slow-frequency, low-amplitude seizures and epochs with rhythmic or periodic patterns. Median review times were shorter for QEEG (6 minutes) and QEEG + raw analysis (14.5 minutes) vs raw EEG (19 minutes; p = 0.00003).

CONCLUSIONS

A panel of QEEG trends can be used by experts to shorten EEG review time for seizure identification with reasonable sensitivity and low FPRs. The prevalence of false detections confirms that raw EEG review must be used in conjunction with QEEG. Studies are needed to identify optimal QEEG trend configurations and the utility of QEEG as a screening tool for non-EEG personnel.

CLASSIFICATION OF EVIDENCE REVIEW

This study provides Class II evidence that QEEG + raw interpreted by experts identifies seizures in patients in the ICU with a sensitivity of 63%-68% and FPR of 0.5 seizures per hour.

Neurocritical care update

This update comprises six important topics under neurocritical care that require reevaluation. For post-cardiac arrest brain injury, the evaluation of the injury and its corresponding therapy, including temperature modulation, is required. Analgosedation for target temperature management is an essential strategy to prevent shivering and minimizes endogenous stress induced by catecholamine surges. For severe traumatic brain injury, the diverse effects of therapeutic hypothermia depend on the complicated pathophysiology of the condition. Continuous electroencephalogram monitoring is an essential tool for detecting nonconvulsive status epilepticus in the intensive care unit (ICU). Neurocritical care, including advanced hemodynamic monitoring, is a fundamental approach for delayed cerebral ischemia following subarachnoid hemorrhage. We must be mindful of the high percentage of ICU patients who may develop sepsis-associated brain dysfunction.

Clinical characteristics of non-convulsive status epilepticus diagnosed by simplified continuous electroencephalogram monitoring at an emergency intensive care unit

Aim

The present study aimed to elucidate the clinical characteristics of non‐convulsive status epilepticus (NCSE) in patients with altered mental status (AMS).

Methods

This single‐center retrospective study comprised 149 patients who were hospitalized between March 1, 2015 and September 30, 2015 at the emergency intensive care unit (ICU) of the Kagawa University Hospital (Kagawa, Japan). The primary outcome was NCSE incidence. The secondary outcome was the comparison of duration of ICU stay, hospital stay, and a favorable neurological outcome, as assessed using the modified Rankin Scale score, at discharge from our hospital between patients with and without NCSE. Favorable neurological outcome and poor neurological outcome were defined as modified Rankin Scale scores of 0–2 and 3–6, respectively.

Results

Simplified continuous electroencephalogram was used to monitor 36 patients (median age, 68 years; 69.4% males) with acute AMS; among them, NCSE was observed in 11 (30.1%) patients. Rates of favorable neurological outcome, duration of ICU stay, and hospital stay were not significantly different between the NCSE and non‐NCSE groups (P = 0.45, P = 0.30, and P = 0.26, respectively).

Conclusion

Approximately 30% of the patients with AMS admitted to emergency ICUs developed NCSE. The outcomes of AMS patients with and without NCSE did not differ significantly when appropriate medical attention and antiepileptic drugs were initiated. Simplified continuous electroencephalogram monitoring may be recommended in patients with AMS in emergency ICU to obtain early detection of NCSE followed by appropriate intervention.

Electrophysiologic monitoring in acute brain injury

To determine the optimal use and indications of electroencephalography (EEG) in critical care management of acute brain injury (ABI). An electronic literature search was conducted for articles in English describing electrophysiological monitoring in ABI from January 1990 to August 2013. A total of 165 studies were included. EEG is a useful monitor for seizure and ischemia detection. There is a well-described role for EEG in convulsive status epilepticus and cardiac arrest (CA). Data suggest EEG should be considered in all patients with ABI and unexplained and persistent altered consciousness and in comatose intensive care unit (ICU) patients without an acute primary brain condition who have an unexplained impairment of mental status. There remain uncertainties about certain technical details, e.g., the minimum duration of EEG studies, the montage, and electrodes. Data obtained from both EEG and EP studies may help estimate prognosis in ABI patients, particularly following CA and traumatic brain injury. Data supporting these recommendations is sparse, and high quality studies are needed. EEG is used to monitor and detect seizures and ischemia in ICU patients and indications for EEG are clear for certain disease states, however, uncertainty remains on other applications.

Successful treatment of non-convulsive status epilepticus diagnosed using bedside monitoring by a combination of amplitude-integrated and two-channel simplified electroencephalography

Case

A 66-year-old man developed disturbed consciousness and right hemiparesis with transient convulsions in the right arm. Bedside monitoring using a combination of amplitude-integrated electroencephalography and two-channel simplified electroencephalography revealed intermittent episodes of 1-3 Hz δ waves lasting for approximately 5 min, consistent with non-convulsive status epilepticus. Fosphenytoin (22.5 mg/kg/day) and levetiracetam (1,000 mg) prevented right arm convulsions but did not restore consciousness. The two-channel simplified electroencephalography also showed an intermittent periodic δ wave pattern in the Fp1-C3 channel. Conventional electroencephalography revealed a polymorphic δ activity that was abolished by 2.5 mg diazepam, thus confirming the diagnosis of non-convulsive status epilepticus.

Outcome

The patient recovered completely with the antiepileptic drug combination.

Conclusion

Immediate initiation of bedside monitoring using amplitude-integrated electroencephalography and two-channel simplified electroencephalography allows early detection of non-convulsive status epilepticus in patients with disturbed consciousness, which considerably improves the prognosis.

Detection of electrographic seizures by critical care providers using color density spectral array after cardiac arrest is feasible

OBJECTIVE

To determine the accuracy and reliability of electroencephalographic seizure detection by critical care providers using color density spectral array electroencephalography.

DESIGN

Tutorial and questionnaire.

SUBJECTS

Critical care providers (attending physicians, fellow trainees, and nurses).

INTERVENTIONS

A standardized powerpoint color density spectral array tutorial followed by classification of 200 color density spectral array images as displaying seizures or not displaying seizures.

MEASUREMENTS AND MAIN RESULTS

Using conventional electroencephalography recordings obtained from patients who underwent electroencephalography monitoring after cardiac arrest, we created 100 color density spectral array images, 30% of which displayed seizures. The gold standard for seizure category was electroencephalographer determination from the full montage conventional electroencephalography. Participants did not have access to the conventional electroencephalography tracings. After completing a standardized color density spectral array tutorial, images were presented to participants in duplicate and in random order. Twenty critical care physicians (12 attendings and eight fellows) and 19 critical care nurses classified the color density spectral array images as having any seizure(s) or no seizures. The 39 critical care providers had a color density spectral array seizure detection sensitivity of 70% (95% CI, 67-73%), specificity of 68% (95% CI, 67-70%), positive predictive value of 46%, and negative predictive value of 86%. The sensitivity of color density spectral array detection of status epilepticus was 72% (95% CI, 69-74%).

CONCLUSION

Determining which post-cardiac arrest patients experience electrographic seizures by critical care providers is feasible after a brief training. There is moderate sensitivity for seizure and status epilepticus detection and a high negative predictive value.

Non-expert use of quantitative EEG displays for seizure identification in the adult neuro-intensive care unit

Video-EEG monitoring is the ultimate way to diagnose non-convulsive status epilepticus (NCSE) in intensive care units (ICU). Usually EEG recordings are evaluated once a day by an electrophysiologist, which may lead to delay in diagnosis. Digital EEG trend analysis methods like amplitude integrated EEG (aEEG) and density spectral array (DSA) have been developed to facilitate recognition of seizures. In this study, we aimed to investigate the diagnostic utility of these methods by non-expert physicians and ICU nurses for NCSE identification in an adult neurological ICU. Ten patients with NCSE and ten control patients without seizures were included in the study. The raw EEG recordings of all subjects were converted to both aEEG and DSA and displayed simultaneously without conventional EEG. After training for seizure recognition with both methods, two physicians and two nurses analyzed the visual displays individually, and marked seizure timings. Their results were compared with those of a study epileptologist. Participants analyzed 615h of EEG data with 700 seizures. Overall, 63% of the seizures were recognized by all, 15.6% by three, 11.6% by two, 8.3% by one rater and only 1.5% were missed by all of them (sensitivity was 88-99%, and specificity was 89-95% when the ratings were assessed as 1-h epochs). False positive rates were 1 per 2h in the study and 1 per 6h in the control groups. Interrater agreement was high (κ=0.79-0.81). Bilateral independent seizures and ictal recordings with lower amplitude and shorter duration were more likely to be missed. There was no difference in performance between the rating of physicians and nurses. Our study demonstrates that bedside nurses, ICU fellows and residents can achieve acceptable level of accuracy for seizure identification using the digital EEG trend analysis methods following brief training. This may help earlier notification of the electrophysiologist who is not always available in ICUs.

Efficacy of a reduced electroencephalography electrode array for detection of seizures

BACKGROUND

The expertise required for proper electroencephalography (EEG) setup can make the 10-20 array unwieldy in the hospital setting. There may be a role for an EEG array with reduced leads to improve the efficiency of inpatient practice.

METHODS

Clips from 100 EEG records, 50 ictal and 50 non-ictal, in adult inpatients from January 1, 2007, to January 1, 2012, were retrospectively reviewed and selected for digital lead reduction and blind review. Two epileptologists reviewed these tracings and documented the presence of seizures and severe disturbance of background. The reduced array included 7 leads spanning the scalp. Three different montages were available. Sensitivity and specificity of the reduced array were calculated using the formal EEG report as the comparison standard.

RESULTS

For the detection of any seizure, the reduced array EEG had a sensitivity of 70% and specificity of 96%. Sensitivity for identifying encephalopathic patterns was 62% and specificity was 86%. Focal seizures were more readily identified by the reduced array (20 of 25) than were generalized ictal patterns (13 of 25).

CONCLUSION

The reduced electrode array was insufficiently sensitive to seizure detection. Reducing EEG leads might not be a preferred means of optimizing hospital EEG efficiency.

Recommendations on the use of EEG monitoring in critically ill patients: consensus statement from the neurointensive care section of the ESICM

OBJECTIVES

Recommendations for EEG monitoring in the ICU are lacking. The Neurointensive Care Section of the ESICM assembled a multidisciplinary group to establish consensus recommendations on the use of EEG in the ICU.

METHODS

A systematic review was performed and 42 studies were included. Data were extracted using the PICO approach, including: (a) population, i.e. ICU patients with at least one of the following: traumatic brain injury, subarachnoid hemorrhage, intracerebral hemorrhage, stroke, coma after cardiac arrest, septic and metabolic encephalopathy, encephalitis, and status epilepticus; (b) intervention, i.e. EEG monitoring of at least 30 min duration; (c) control, i.e. intermittent vs. continuous EEG, as no studies compared patients with a specific clinical condition, with and without EEG monitoring; (d) outcome endpoints, i.e. seizure detection, ischemia detection, and prognostication. After selection, evidence was classified and recommendations developed using the GRADE system.

RECOMMENDATIONS

The panel recommends EEG in generalized convulsive status epilepticus and to rule out nonconvulsive seizures in brain-injured patients and in comatose ICU patients without primary brain injury who have unexplained and persistent altered consciousness. We suggest EEG to detect ischemia in comatose patients with subarachnoid hemorrhage and to improve prognostication of coma after cardiac arrest. We recommend continuous over intermittent EEG for refractory status epilepticus and suggest it for patients with status epilepticus and suspected ongoing seizures and for comatose patients with unexplained and persistent altered consciousness.

CONCLUSIONS

EEG monitoring is an important diagnostic tool for specific indications. Further data are necessary to understand its potential for ischemia assessment and coma prognostication.