Clinical Use of EEG in the ICU: Technical Setting

The Utility of Limited-Montage Electroencephalography for Seizure Detection in Children

BACKGROUND

The primary objective of this study was to investigate the utility of limited-montage electroencephalography (EEG) for seizure detection in children. We also aimed to determine whether the detection rate differed among different montage patterns.

METHODS

This study was carried out between November 2019 and October 2020 at a tertiary children's hospital in Japan. The subjects were inpatients in the pediatric intensive care unit who had an epileptic seizure during EEG monitoring. Each patient's EEG record, consisting of a 15-minute recording during an epileptic seizure and a 15-minute recording in the absence of an epileptic seizure, was extracted from the medical charts. The EEG data were then analyzed using six, limited-montage coverage patterns: (1) Fp1-C3, Fp2-C4, (2) Fp1-O1, Fp2-O2, (3) Fp1-T3, Fp2-T4, (4) C3-O1, C4-O2, (5) C3-T3, C4-T4, and (6) O1-T3, O2-T4. The sensitivity and specificity of each montage for seizure detection was analyzed.

RESULTS

One hundred thirty-two EEG data points from 11 patients were examined. Sensitivity and specificity were the highest for Fp1-O1 and Fp2-O2 at 73% and 91%, respectively. Overall, the montage covering the frontopolar area had the highest detection rate, followed by the montage covering the occipital, central, and temporal areas.

CONCLUSION

Limited-montage EEG identified seizures in children hospitalized in the intensive care unit, but the detection rate differed by montage coverage. The detection rate was highest in the montage covering the frontopolar area.

Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study

Functional MRI (fMRI) and EEG may reveal residual consciousness in patients with disorders of consciousness (DoC), as reflected by a rapidly expanding literature on chronic DoC. However, acute DoC is rarely investigated, although identifying residual consciousness is key to clinical decision-making in the intensive care unit (ICU). Therefore, the objective of the prospective, observational, tertiary centre cohort, diagnostic phase IIb study 'Consciousness in neurocritical care cohort study using EEG and fMRI' (CONNECT-ME, NCT02644265) was to assess the accuracy of fMRI and EEG to identify residual consciousness in acute DoC in the ICU. Between April 2016 and November 2020, 87 acute DoC patients with traumatic or non-traumatic brain injury were examined with repeated clinical assessments, fMRI and EEG. Resting-state EEG and EEG with external stimulations were evaluated by visual analysis, spectral band analysis and a Support Vector Machine (SVM) consciousness classifier. In addition, within- and between-network resting-state connectivity for canonical resting-state fMRI networks was assessed. Next, we used EEG and fMRI data at study enrolment in two different machine-learning algorithms (Random Forest and SVM with a linear kernel) to distinguish patients in a minimally conscious state or better (≥MCS) from those in coma or unresponsive wakefulness state (≤UWS) at time of study enrolment and at ICU discharge (or before death). Prediction performances were assessed with area under the curve (AUC). Of 87 DoC patients (mean age, 50.0 ± 18 years, 43% female), 51 (59%) were ≤UWS and 36 (41%) were ≥ MCS at study enrolment. Thirty-one (36%) patients died in the ICU, including 28 who had life-sustaining therapy withdrawn. EEG and fMRI predicted consciousness levels at study enrolment and ICU discharge, with maximum AUCs of 0.79 (95% CI 0.77-0.80) and 0.71 (95% CI 0.77-0.80), respectively. Models based on combined EEG and fMRI features predicted consciousness levels at study enrolment and ICU discharge with maximum AUCs of 0.78 (95% CI 0.71-0.86) and 0.83 (95% CI 0.75-0.89), respectively, with improved positive predictive value and sensitivity. Overall, both machine-learning algorithms (SVM and Random Forest) performed equally well. In conclusion, we suggest that acute DoC prediction models in the ICU be based on a combination of fMRI and EEG features, regardless of the machine-learning algorithm used.

Development of a mobile low-field MRI scanner

Magnetic resonance imaging (MRI) allows important visualization of the brain and central nervous system anatomy and organization. However, unlike electroencephalography (EEG) or functional near infrared spectroscopy, which can be brought to a patient or study participant, MRI remains a hospital or center-based modality. Low magnetic field strength MRI systems, however, offer the potential to extend beyond these traditional hospital and imaging center boundaries. Here we describe the development of a modified cargo van that incorporates a removable low-field permanent magnet MRI system and demonstrate its proof-of-concept. Using phantom scans and in vivo T₂-weighted neuroimaging data, we show no significant differences with respect to geometric distortion, signal-to-noise ratio, or tissue segmentation outcomes in data acquired in the mobile system compared to a similar static system in a laboratory setting. These encouraging results show, for the first time, MRI that can be performed at a participant’s home, community center, school, etc. Breaking traditional barriers of access, this mobile approach may enable imaging of patients and participants who have mobility challenges, live long distances from imaging centers, or are otherwise unable to travel to an imaging center or hospital.

Seizure Management in the Intensive Care Unit

Purpose of this review

This review presents current therapy for seizures in the intensive care unit. The reader is provided with recent evidence regarding the use of EEG in determining treatment for acute seizures. Proposed treatment approaches for seizures and status epilepticus are provided. Controversies and complexity of selecting treatments are discussed.

Recent findings

Critical Care EEG Monitoring Research Consortium analyzed the association of periodic and rhythmic electroencephalographic patterns with seizures and found that lateralized and generalized periodic discharges and lateralized rhythmic delta were associated with increased seizure risk. Applications using modified EEG techniques have demonstrated more rapid feedback to the ICU than was previously possible.

Summary

Accurate diagnosis and efficient treatment of seizures in the ICU is challenging due to patient factors, complexities of antiepileptic drug therapy, and the required expertise for EEG interpretation. Selection of optimally effective therapy for seizures or status epilepticus depends on multiple factors, making collaboration between neurophysiologists and the ICU team of paramount importance.

Continuous vs Routine Electroencephalogram in Critically Ill Adults With Altered Consciousness and No Recent Seizure: A Multicenter Randomized Clinical Trial

Question

In patients with acute consciousness impairment and no recent seizures, does continuous electroencephalogram (cEEG) correlate with reduced mortality compared with repeated routine EEG (rEEG)?

Findings

In this pragmatic, multicenter randomized clinical trial analyzing 364 adults, cEEG translated into a higher rate of seizures/status epilepticus detection and antiseizure treatment modifications but did not improve mortality compared with rEEG.

Meaning

Pending larger studies, rEEG may represent a valid alternative to cEEG in centers with limited resources.

Importance

In critically ill patients with altered consciousness, continuous electroencephalogram (cEEG) improves seizure detection, but is resource-consuming compared with routine EEG (rEEG). It is also uncertain whether cEEG has an effect on outcome.

Objective

To assess whether cEEG is associated with reduced mortality compared with rEEG.

Design, Setting, and Participants

The pragmatic multicenter Continuous EEG Randomized Trial in Adults (CERTA) was conducted between 2017 and 2018, with follow-up of 6 months. Outcomes were assessed by interviewers blinded to interventions.The study took place at 4 tertiary hospitals in Switzerland (intensive and intermediate care units). Depending on investigators’ availability, we pragmatically recruited critically ill adults having Glasgow Coma Scale scores of 11 or less or Full Outline of Responsiveness score of 12 or less, without recent seizures or status epilepticus. They had cerebral (eg, brain trauma, cardiac arrest, hemorrhage, or stroke) or noncerebral conditions (eg, toxic-metabolic or unknown etiology), and EEG was requested as part of standard care. An independent physician provided emergency informed consent.

Interventions

Participants were randomized 1:1 to cEEG for 30 to 48 hours vs 2 rEEGs (20 minutes each), interpreted according to standardized American Clinical Neurophysiology Society guidelines.

Main Outcomes and Measures

Mortality at 6 months represented the primary outcome. Secondary outcomes included interictal and ictal features detection and change in therapy.

Results

We analyzed 364 patients (33% women; mean [SD] age, 63 [15] years). At 6 months, mortality was 89 of 182 in those with cEEG and 88 of 182 in those with rEEG (adjusted relative risk [RR], 1.02; 95% CI, 0.83-1.26; P = .85). Exploratory comparisons within subgroups stratifying patients according to age, premorbid disability, comorbidities on admission, deeper consciousness reduction, and underlying diagnoses revealed no significant effect modification. Continuous EEG was associated with increased detection of interictal features and seizures (adjusted RR, 1.26; 95% CI, 1.08-1.15; P = .004 and 3.37; 95% CI, 1.63-7.00; P = .001, respectively) and more frequent adaptations in antiseizure therapy (RR, 1.84; 95% CI, 1.12-3.00; P = .01).

Conclusions and Relevance

This pragmatic trial shows that in critically ill adults with impaired consciousness and no recent seizure, cEEG leads to increased seizure detection and modification of antiseizure treatment but is not related to improved outcome compared with repeated rEEG. Pending larger studies, rEEG may represent a valid alternative to cEEG in centers with limited resources.

Trial Registration

ClinicalTrials.gov Identifier: NCT03129438

Clinical and advanced neurophysiology in the prognostic and diagnostic evaluation of disorders of consciousness: review of an IFCN-endorsed expert group

The analysis of spontaneous EEG activity and evoked potentialsis a cornerstone of the instrumental evaluation of patients with disorders of consciousness (DoC). Thepast few years have witnessed an unprecedented surge in EEG-related research applied to the prediction and detection of recovery of consciousness after severe brain injury,opening up the prospect that new concepts and tools may be available at the bedside. This paper provides a comprehensive, critical overview of bothconsolidated and investigational electrophysiological techniquesfor the prognostic and diagnostic assessment of DoC.We describe conventional clinical EEG approaches, then focus on evoked and event-related potentials, and finally we analyze the potential of novel research findings. In doing so, we (i) draw a distinction between acute, prolonged and chronic phases of DoC, (ii) attempt to relate both clinical and research findings to the underlying neuronal processes and (iii) discuss technical and conceptual caveats.The primary aim of this narrative review is to bridge the gap between standard and emerging electrophysiological measures for the detection and prediction of recovery of consciousness. The ultimate scope is to provide a reference and common ground for academic researchers active in the field of neurophysiology and clinicians engaged in intensive care unit and rehabilitation.

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.

Continuous EEG Monitoring in a Consecutive Patient Cohort with Sepsis and Delirium

BACKGROUND

Delirium is common during sepsis, although under-recognized. We aimed to assess the value of continuous electroencephalography (cEEG) to aid in the diagnosis of delirium in septic patients.

METHODS

We prospectively evaluated 102 consecutive patients in a medical intensive care unit (ICU), who had sepsis or septic shock, without evidence of acute primary central nervous system disease. We initiated cEEG recording immediately after identification. The median cEEG time per patient was 44 h (interquartile range 21-99 h). A total of 6723 h of cEEG recordings were examined. The Confusion Assessment Method for the ICU (CAM-ICU) was administered six times daily to identify delirium. We analyzed the correlation between cEEG and delirium using 1252 two-minute EEG sequences recorded simultaneously with the CAM-ICU scorings.

RESULTS

Of the 102 included patients, 66 (65%) had at least one delirium episode during their ICU stay, 30 (29%) remained delirium-free, and 6 (6%) were not assessable due to deep sedation or coma. The absence of delirium was independently associated with preserved high-frequency beta activity (> 13 Hz) (P < 10-7) and cEEG reactivity (P < 0.001). Delirium was associated with preponderance of low-frequency cEEG activity and absence of high-frequency cEEG activity. Sporadic periodic cEEG discharges occurred in 15 patients, 13 of whom were delirious. No patient showed clinical or electrographic evidence of non-convulsive status epilepticus.

CONCLUSIONS

Our findings indicate that cEEG can help distinguish septic patients with delirium from non-delirious patients.

Feasibility of continuous sedation monitoring in critically ill intensive care unit patients using the NeuroSENSE WAVCNS index

Sedation in the intensive care unit (ICU) is challenging, as both over- and under-sedation are detrimental. Current methods of assessment, such as the Richmond Agitation Sedation Scale (RASS), are measured intermittently and rely on patients' behavioral response to stimulation, which may interrupt sleep/rest. A non-stimulating method for continuous sedation monitoring may be beneficial and allow more frequent assessment. Processed electroencephalography (EEG) monitors have not been routinely adopted in the ICU. The aim of this observational study was to assess the feasibility of using the NeuroSENSE™ monitor for EEG-based continuous sedation assessment. With ethical approval, ICU patients on continuous propofol sedation were recruited. Depth-of-hypnosis index (WAV_CNS) values were obtained from the NeuroSENSE. Bedside nurses, blinded to the NeuroSENSE, performed regular RASS assessments and maintained the sedation regimen as per standard of care. Participants were monitored throughout the duration of their propofol infusion, up to 24 h. Fifteen patients, with median [interquartile range] age of 57 [52-62.5] years were each monitored for a duration of 9.0 [5.7-20.1] h. Valid WAV_CNS values were obtained for 89% [66-99] of monitoring time and were widely distributed within and between individuals, with 6% [1-31] spent < 40 (very deep), and 3% [1-15] spent > 90 (awake). Significant EEG suppression was detected in 3/15 (20%) participants. Observed RASS matched RASS goals in 36/89 (40%) assessments. The WAV_CNS variability, and incidence of EEG suppression, highlight the limitations of using RASS as a standalone sedation measure, and suggests potential benefit of adjunct continuous brain monitoring.

An update on the recognition and treatment of non-convulsive status epilepticus in the intensive care unit

INTRODUCTION

Non-convulsive status epilepticus (NCSE) is a complex and diverse condition which is often an under-recognised entity in the intensive care unit. When NCSE is identified the optimal treatment strategy is not always clear. Areas covered: This review is based on a literature review of the key literature in the field over the last 5-10 years. The articles were selected based on their importance to the field by the authors. Expert commentary: This review discusses the complex situations when a neurological consultation may occur in a critical care setting and provides an update on the latest evidence regarding the recognition of NCSE and the decision making around determining the aggressiveness of treatment. It also considers the ictal-interictal continuum of conditions which may be met with, particularly in the era of continuous EEG, and provides an approach for dealing with these. Suggestions for how the field will develop are discussed.

Clinical neurophysiology for neurological prognostication of comatose patients after cardiac arrest

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A multimodal prognostic approach is recommended after cardiac arrest.

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EEG (background and, reactivity, repetitive epileptiform features) and SSEP are core assessments.

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Some outlook into long-latency evoked potentials is offered.

Early prognostication of outcome in comatose patients after cardiac arrest represents a daunting task for clinicians, also considering the nowadays commonly used targeted temperature management with sedation in the first 24–48 h. A multimodal approach is currently recommended, in order to minimize the risks of false-positive prediction of poor outcome, including clinical examination off sedation, EEG (background characterization and reactivity, occurrence of repetitive epileptiform features), and early-latency SSEP responses represent the core assessments in this setting; they may be complemented by biochemical markers and neuroimaging.

This paper, which relies on a recent comprehensive review, focuses on an updated review of EEG and SSEP, and also offers some outlook into long-latency evoked potentials, which seem promising in clinical use.

The use and yield of continuous EEG in critically ill patients: A comparative study of three centers

OBJECTIVE

Continuous EEG (cEEG) monitoring of critically ill patients has gained widespread use, but there is substantial reported variability in its use. We analyzed cEEG and antiseizure drug (ASD) usage at three high volume centers.

METHODS

We utilized a multicenter cEEG database used daily as a clinical reporting tool in three tertiary care sites (Emory Hospital, Brigham and Women's Hospital and Yale - New Haven Hospital). We compared the cEEG usage patterns, seizure frequency, detection of rhythmic/periodic patterns (RPP), and ASD use between the sites.

RESULTS

5792 cEEG sessions were analyzed. Indication for cEEG monitoring and recording duration were similar between the sites. Seizures detection rate was nearly identical between the three sites, ranging between 12.3% and 13.6%. Median time to first seizure and detection rate of RPPs were similar. There were significant differences in doses of levetiracetam, valproic acid, and lacosamide used between the three sites.

CONCLUSIONS

There was remarkable uniformity in seizure detection rates within three high volume centers. In contrast, dose of ASD used frequently differed between the three sites.

SIGNIFICANCE

These large volume data are in line with recent guidelines regarding cEEG use. Difference in ASD use suggests discrepancies in how cEEG results influence patient management.

The Benefit of Neuromuscular Blockade in Patients with Postanoxic Myoclonus Otherwise Obscuring Continuous Electroencephalography (CEEG)

Introduction. Myoclonus status epilepticus is independently associated with poor outcome in coma patients after cardiac arrest. Determining if myoclonus is of cortical origin on continuous electroencephalography (CEEG) can be difficult secondary to the muscle artifact obscuring the underlying CEEG. The use of a neuromuscular blocker can be useful in these cases. Methods. Retrospective review of CEEG in patients with postanoxic myoclonus who received cisatracurium while being monitored. Results. Twelve patients (mean age: 53.3 years; 58.3% male) met inclusion criteria of clinical postanoxic myoclonus. The initial CEEG patterns immediately prior to neuromuscular blockade showed myoclonic artifact with continuous slowing (50%), burst suppression with myoclonic artifact (41.7%), and continuous myogenic artifact obscuring CEEG (8.3%). After intravenous administration of cisatracurium (0.1 mg–2 mg), reduction in artifact improved quality of CEEG recordings in 9/12 (75%), revealing previously unrecognized patterns: continuous EEG seizures (33.3%), lateralizing slowing (16.7%), burst suppression (16.7%), generalized periodic discharges (8.3%), and, in the patient who had an initially uninterpretable CEEG from myogenic artifact, continuous slowing. Conclusion. Short-acting neuromuscular blockade is useful in determining background cerebral activity on CEEG otherwise partially or completely obscured by muscle artifact in patients with postanoxic myoclonus. Fully understanding background cerebral activity is important in prognostication and treatment, particularly when there are underlying EEG seizures.

Neurological prognostication of outcome in patients in coma after cardiac arrest

Management of coma after cardiac arrest has improved during the past decade, allowing an increasing proportion of patients to survive, thus prognostication has become an integral part of post-resuscitation care. Neurologists are increasingly confronted with raised expectations of next of kin and the necessity to provide early predictions of long-term prognosis. During the past decade, as technology and clinical evidence have evolved, post-cardiac arrest prognostication has moved towards a multimodal paradigm combining clinical examination with additional methods, consisting of electrophysiology, blood biomarkers, and brain imaging, to optimise prognostic accuracy. Prognostication should never be based on a single indicator; although some variables have very low false positive rates for poor outcome, multimodal assessment provides resassurance about the reliability of a prognostic estimate by offering concordant evidence.