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

Time Is Brain: The Use of EEG Electrode Caps to Rapidly Diagnose Nonconvulsive Status Epilepticus

OBJECTIVE

To perform a feasibility pilot study comparing the usefulness of EEG electrode cap versus standard scalp EEG for acquiring emergent EEGs in emergency department, inpatient, and intensive care unit patients.

BACKGROUND

Nonconvulsive status epilepticus (NCSE) is a neurological emergency diagnosed exclusively by EEG. Nonconvulsive status epilepticus becomes more resistant to treatment 1 hour after continued seizure activity. EEG technologists are alerted "stat" when there is immediate need for an EEG during oncall hours, yet delays are inevitable. Alternatively, EEG caps can be quickly placed by in-house residents at bedside for assessment.

DESIGN/METHODS

EEG caps were compared with standard-of-care "stat" EEGs for 20 patients with suspected NCSE. After the order for a stat EEG was placed, neurology residents were simultaneously alerted and placed an EEG cap prior to the arrival of the on-call out-of-hospital technologist. Both EEG cap recordings and standard EEG recordings were visually reviewed at 10 and 20 minutes in a blinded manner by two electroencephalographers. The timing, accuracy of interpretation, and diagnosis between the two techniques were then compared.

RESULTS

Of the 20 adult patients, 70% (14 of 20) of EEG cap recordings were interpretable, whereas 95% (19 of 20) standard EEGs were interpretable; three had findings consistent with NCSE on both the EEG cap and standard EEG recordings. In the time analysis, 16 patients were included. EEG cap placement was significantly more time efficient than an EEG performed by technologist using the usual "stat" EEG protocol, with the median EEG cap electrode placement occurring 86 minutes faster than standard EEG (22.5 minutes vs. 104.5 minutes; P < 0.0001; n = 16).

CONCLUSIONS

New rapid EEG recording using improved EEG caps may allow for rapid diagnosis and clinical decision making in suspected NCSE.

Big data in status epilepticus

Status epilepticus care and treatment are already being touched by the revolution in data science. New approaches designed to leverage the tremendous potential of "big data" in the clinical sphere are enabling researchers and clinicians to extract information from sources such as administrative claims data, the electronic medical health record, and continuous physiologic monitoring data streams. Algorithmic methods of data extraction also offer potential to fuse multimodal data (including text-based documentation, imaging data, and time-series data) to improve patient assessment and stratification beyond the manual capabilities of individual physicians. Still, the potential of data science to impact the diagnosis, treatment, and minute-to-minute care of patients with status epilepticus is only starting to be appreciated. In this brief review, we discuss how data science is impacting the field and draw examples from the following three main areas: (1) analysis of insurance claims from large administrative datasets to evaluate the impact of continuous electroencephalogram (EEG) monitoring on clinical outcomes; (2) natural language processing of the electronic health record to find, classify, and stratify patients for prognostication and treatment; and (3) real-time systems for data analysis, data reduction, and multimodal data fusion to guide therapy in real time. While early, it is our hope that these examples will stimulate investigators to leverage data science, computer science, and engineering methods to improve the care and outcome of patients with status epilepticus and other neurological disorders. This article is part of the Special Issue "Proceedings of the 7th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures".

Applications of Higher Order Statistics in Electroencephalography Signal Processing: A Comprehensive Survey

Electroencephalography (EEG) is a noninvasive electrophysiological monitoring technique that records the electrical activities of the brain from the scalp using electrodes. EEG is not only an essential tool for diagnosing diseases and disorders affecting the brain, but also helps us to achieve a better understanding of brain's activities and structures. EEG recordings are weak, nonlinear, and nonstationary signals that contain various noise and artifacts. Therefore, for analyzing them, advanced signal processing techniques are required. Second order statistical features are usually sufficient for analyzing most basic signals. However, higher order statistical features possess characteristics that are missing in the second order; characteristics that can be highly beneficial for analysis of more complex signals, such as EEG. The primary goal of this article is to provide a comprehensive survey of the applications of higher order statistics or spectra (HOS) in EEG signal processing. Therefore, we start the survey with a summary of previous studies in EEG analysis followed by a brief mathematical description of HOS. Then, HOS related features and their applications in EEG analysis are presented. These applications are then grouped into three categories, each of which are further explored thoroughly with examples of prior studies. Finally, we provide some specific recommendations based on the literature survey and discuss possible future directions of this field.

A pragmatic approach to intravenous anaesthetics and electroencephalographic endpoints for the treatment of refractory and super-refractory status epilepticus in critical care

Status epilepticus is a common neurological emergency, with overall mortality around 20%. Over half of cases are first time presentations of seizures. The pathological process by which spontaneous seizures are generated arises from an imbalance in excitatory and inhibitory neuronal networks, which if unchecked, can result in alterations in intracellular signalling pathways and electrolyte shifts, which bring about changes in the blood brain barrier, neuronal cell death and eventually cerebral atrophy. This narrative review focusses on the treatment of status epilepticus in adults. Anaesthetic agents interrupt neuronal activity by enhancing inhibitory or decreasing excitatory transmission, primarily via GABA and NMDA receptors. Intravenous anaesthetic agents are commonly used as second or third line drugs in the treatment of refractory status epilepticus, but the optimal timing and choice of anaesthetic drug has not yet been established by high quality evidence. Titration of antiepileptic and anaesthetic drugs in critically ill patients presents a particular challenge, due to alterations in drug absorbtion and metabolism as well as changes in drug distrubution, which arise from fluid shifts and altered protein binding. Furthermore, side effects associated with prolonged infusions of anaesthetic drugs can lead to multi-organ dysfunction and a need for critical care support. Electroencelography can identify patterns of burst suppression, which may be a target to guide weaning of intravenous therapy. Continuous elctroencephalography has the potential to directly impact clinical care, but despite its utility, major barriers exist which have limited its widespread use in clinical practice. A flow chart outlining the timing and dosage of anaesthetic agents used at our institution is provided.

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.

Automated spectrographic seizure detection using convolutional neural networks

PURPOSE

Non-convulsive seizures are common in critically ill patients, and delays in diagnosis contribute to increased morbidity and mortality. Many intensive care units employ continuous EEG (cEEG) for seizure monitoring. Although cEEG is continuously recorded, it is often reviewed intermittently, which may delay seizure diagnosis and treatment. This may be mitigated with automated seizure detection. In this study, we develop and evaluate convolutional neural networks (CNN) to automate seizure detection on EEG spectrograms.

METHODS

Adult EEGs (12 patients, 12 EEGs, 33 seizures) from New-York Presbyterian Hospital (NYP) and pediatric EEGs (22 patients, 130 EEGs, 177 seizures) from Children's Hospital Boston (CHB) were converted into spectrograms. To simulate a telemetry display, seizure and non-seizure events on spectrograms were sequentially sampled as images across a detection window (26,380 total images). Four CNN models of increasing complexity (number of layers) were trained, cross-validated, and tested on CHB and NYP spectrographic images. All CNNs were based on the VGG-net architecture, with adjustments to alleviate overfitting.

RESULTS

For spectrographically visible seizures, two CNN models (containing 4 and 7 convolution layers) achieved >90% seizure detection sensitivity and specificity on the CHB test set and >90% sensitivity and 75-80% specificity on the NYP test set. The one CNN model (10 convolution layers) did not converge during training; while another CNN (2 convolution layers) performed poorly (60% sensitivity and 32% specificity) on the NYP test set.

CONCLUSIONS

Seizure detection on EEG spectrograms with CNN models is feasible with sensitivity and specificity potentially suitable for clinical use.

Raw Versus Processed EEG: Which One is Better?

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Interstitial Stereotactic Laser Anterior Corpus Callosotomy: A Report of 2 Cases with Operative Technique and Effectiveness

BACKGROUND AND IMPORTANCE

Corpus callosotomy is an effective palliative treatment for medically intractable Lennox–Gastaut syndrome (LGS) that disrupts the interhemispheric synchronization of epileptiform discharges. However, traditional open corpus callosotomy carries a significant risk of surgical complications associated with craniotomy and a parafalcine approach to the corpus callosum. Here, we report 2 cases of anterior corpus callosotomy using MRI-guided stereotactic laser interstitial thermal therapy (LITT) as a minimally invasive technique for mitigating the risks of craniotomy while achieving favorable outcomes.

CLINICAL PRESENTATION

Two patients with medically intractable LGS underwent stereotactic laser anterior corpus callosotomy using a 2 laser-fiber approach. Ablation of 70%-80% of the corpus callosum was confirmed by postoperative MRI diffusion tensor imaging and volumetric analysis. Marked reduction of epileptiform activity was observed in both patients during postoperative video-EEG studies as compared to preoperative video-EEG studies. Freedom from disabling seizures including drop attacks was achieved in 1 patient for 18 mo, and more than a 90% reduction of disabling seizures was achieved in the other patient for 7 mo with cognitive improvement and without surgical complications.

CONCLUSION

These early data demonstrate the technical feasibility, safety, and favorable outcomes of MRI-guided stereotactic laser anterior corpus callosotomy in patients with LGS, making it a potentially safe and effective alternative to traditional open corpus callosotomy and other stereotactic methods including radiofrequency ablation and radiosurgery due to the ability to monitor the ablation in real time with MRI.

Automated Long-Term EEG Review: Fast and Precise Analysis in Critical Care Patients

Background: Ongoing or recurrent seizure activity without prominent motor features is a common burden in neurological critical care patients and people with epilepsy during ICU stays. Continuous EEG (CEEG) is the gold standard for detecting ongoing ictal EEG patterns and monitoring functional brain activity. However CEEG review is very demanding and time consuming. The purpose of the present multirater, EEG expert reviewer study, is to test and assess the clinical feasibility of an automatic EEG pattern detection method (Neurotrend).

Methods: Four board certified EEG reviewers used Neurotrend to annotate 76 CEEG datasets à 6 h (in total 456 h of EEG) for rhythmic and periodic EEG patterns (RPP), unequivocal ictal EEG patterns and burst suppression. All reviewers had a predefined time limit of 5 min (± 2 min) per CEEG dataset and were compared to a predefined gold standard (conventional EEG review with unlimited time). Subanalysis of specific features of RPP was conducted as well. We used Gwet's AC₁ and AC₂ coefficients to calculate interrater agreement (IRA) and multirater agreement (MRA). Also, we determined individual performance measures for unequivocal ictal EEG patterns and burst suppression. Bonferroni-Holmes correction for multiple testing was applied to all statistical tests.

Results: Mean review time was 3.3 min (± 1.9 min) per CEEG dataset. We found substantial IRA for unequivocal ictal EEG patterns (0.61–0.79; mean sensitivity 86.8%; mean specificity 82.2%, p < 0.001) and burst suppression (0.68–0.71; mean sensitivity 96.7%; mean specificity 76.9% p < 0.001). Two reviewers showed substantial IRA for RPP (0.68–0.72), whereas the other two showed moderate agreement (0.45–0.54), compared to the gold standard (p < 0.001). MRA showed almost perfect agreement for burst suppression (0.86) and moderate agreement for RPP (0.54) and unequivocal ictal EEG patterns (0.57).

Conclusions: We demonstrated the clinical feasibility of an automatic critical care EEG pattern detection method on two levels: (1) reasonable high agreement compared to the gold standard, (2) reasonable short review times compared to previously reported EEG review times with conventional EEG analysis.

Updates in Refractory Status Epilepticus

Refractory status epilepticus is defined as persistent seizures despite appropriate use of two intravenous medications, one of which is a benzodiazepine. It can be seen in up to 40% of cases of status epilepticus with an acute symptomatic etiology as the most likely cause. New-onset refractory status epilepticus (NORSE) is a recently coined term for refractory status epilepticus where no apparent cause is found after initial testing. A large proportion of NORSE cases are eventually found to have an autoimmune etiology needing immunomodulatory treatment. Management of refractory status epilepticus involves treatment of an underlying etiology in addition to intravenous anesthetics and antiepileptic drugs. Alternative treatment options including diet therapies, electroconvulsive therapy, and surgical resection in case of a focal lesion should be considered. Short-term and long-term outcomes tend to be poor with significant morbidity and mortality with only one-third of patients reaching baseline neurological status.

Visual EEG reviewing times with SCORE EEG

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There is concern that the SCORE reporting standard for EEG takes too long.

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This study shows that a normal EEG can typically be reported in SCORE EEG in 8 min.

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Reviewing time is higher for abnormal recordings, and declined by 25% in this study.

Objective

Visual EEG analysis is the gold standard for clinical EEG interpretation and analysis, but there is no published data on how long it takes to review and report an EEG in clinical routine. Estimates of reporting times may inform workforce planning and automation initiatives for EEG. The SCORE standard has recently been adopted to standardize clinical EEG reporting, but concern has been expressed about the time spent reporting.

Methods

Elapsed times were extracted from 5889 standard and sleep-deprived EEGs reported between 2015 and 2017 reported using the SCORE EEG software.

Results

The median review time for standard EEG was 12.5 min, and for sleep deprived EEG 20.9 min. A normal standard EEG had a median review time of 8.3 min. Abnormal EEGs took longer than normal EEGs to review, and had more variable review times. 99% of EEGs were reported within 24 h of end of recording. Review times declined by 25% during the study period.

Conclusion

Standard and sleep-deprived EEG review and reporting times with SCORE EEG are reasonable, increasing with increasing EEG complexity and decreasing with experience. EEG reports can be provided within 24 h.

Significance

Clinical standard and sleep-deprived EEG reporting with SCORE EEG has acceptable reporting times.

Comparative sensitivity of quantitative EEG (QEEG) spectrograms for detecting seizure subtypes

PURPOSE

To assess the sensitivity of Persyst version 12 QEEG spectrograms to detect focal, focal with secondarily generalized, and generalized onset seizures.

METHODS

A cohort of 562 seizures from 58 patients was analyzed. Successive recordings with 2 or more seizures during continuous EEG monitoring for clinical indications in the ICU or EMU between July 2016 and January 2017 were included. Patient ages ranged from 5 to 64 years (mean = 36 years). There were 125 focal seizures, 187 secondarily generalized and 250 generalized seizures from 58 patients analyzed. Seizures were identified and classified independently by two epileptologists. A correlate to the seizure pattern in the raw EEG was sought in the QEEG spectrograms in 4-6 h EEG epochs surrounding the identified seizures. A given spectrogram was interpreted as indicating a seizure, if at the time of a seizure it showed a visually significant departure from the pre-event baseline. Sensitivities for seizure detection using each spectrogram were determined for each seizure subtype.

RESULTS

Overall sensitivities of the QEEG spectrograms for detecting seizures ranged from 43% to 72%, with highest sensitivity (402/562,72%) by the seizure detection trend. The asymmetry spectrogram had the highest sensitivity for detecting focal seizures (117/125,94%). The FFT spectrogram was most sensitive for detecting secondarily generalized seizures (158/187, 84%). The seizure detection trend was the most sensitive for generalized onset seizures (197/250,79%).

CONCLUSIONS

Our study suggests that different seizure types have specific patterns in the Persyst QEEG spectrograms. Identifying these patterns in the EEG can significantly increase the sensitivity for seizure identification.

Cognitive and Neurophysiological Recovery Following Electroconvulsive Therapy: A Study Protocol

Electroconvulsive therapy (ECT) employs the elective induction of generalizes seizures as a potent treatment for severe psychiatric illness. As such, ECT provides an opportunity to rigorously study the recovery of consciousness, reconstitution of cognition, and electroencephalographic (EEG) activity following seizures. Fifteen patients with major depressive disorder refractory to pharmacologic therapy will be enrolled (Clinicaltrials.gov, NCT02761330). Adequate seizure duration will be confirmed following right unilateral ECT under etomidate anesthesia. Patients will then undergo randomization for the order in which they will receive three sequential treatments: etomidate + ECT, ketamine + ECT, and ketamine + sham ECT. Sessions will be repeated in the same sequence for a total of six treatments. Before each session, sensorimotor speed, working memory, and executive function will be assessed through a standardized cognitive test battery. After each treatment, the return of purposeful responsiveness to verbal command will be determined. At this point, serial cognitive assessments will begin using the same standardized test battery. The presence of delirium and changes in depression severity will also be ascertained. Sixty-four channel EEG will be acquired throughout baseline, ictal, and postictal epochs. Mixed-effects models will correlate the trajectories of cognitive recovery, clinical outcomes, and EEG metrics over time. This innovative research design will answer whether: (1) time to return of responsiveness will be prolonged with ketamine + ECT compared with ketamine + sham ECT; (2) time of restoration to baseline function in each cognitive domain will take longer after ketamine + ECT than after ketamine + sham ECT; (3) postictal delirium is associated with delayed restoration of baseline function in all cognitive domains; and (4) the sequence of reconstitution of cognitive domains following the three treatments in this study is similar to that occurring after an isoflurane general anesthetic (NCT01911195). Sub-studies will assess the relationships of cognitive recovery to the EEG preceding, concurrent, and following individual ECT sessions. Overall, this study will lead the development of biomarkers for tailoring the cogno-affective recovery of patients undergoing ECT.

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.

Neurologic Outcomes Following Care in the Pediatric Intensive Care Unit

Purpose of review

With increasing survival of children requiring admission to pediatric intensive care units (PICU), neurodevelopmental outcomes of these patients are an area of increased attention. Our goal was to systematically review recently published literature on neurologic outcomes of PICU patients.

Recent Findings

Decline in neurofunctional status occurs in 3%-20% of children requiring PICU care. This proportion varies based on primary diagnosis and severity of illness, with children admitted for primary neurologic diagnosis, children who suffer cardiac arrest or who require invasive interventions during the PICU admission, having worse outcomes. Recent research focuses on early identification and treatment of modifiable risk factors for unfavorable outcomes, and on long-term follow-up that moves beyond global cognitive outcomes and is increasingly including tests assessing multidimensional aspects of neurodevelopment.

Summary

The pediatric critical care research community has shifted focus from survival to survival with favorable neurologic outcomes of children admitted to the PICU.

Time-dependent risk of seizures in critically ill patients on continuous electroencephalogram

OBJECTIVE

Find the optimal continuous electroencephalographic (CEEG) monitoring duration for seizure detection in critically ill patients.

METHODS

We analyzed prospective data from 665 consecutive CEEGs, including clinical factors and time-to-event emergence of electroencephalographic (EEG) findings over 72 hours. Clinical factors were selected using logistic regression. EEG risk factors were selected a priori. Clinical factors were used for baseline (pre-EEG) risk. EEG findings were used for the creation of a multistate survival model with 3 states (entry, EEG risk, and seizure). EEG risk state is defined by emergence of epileptiform patterns.

RESULTS

The clinical variables of greatest predictive value were coma (31% had seizures; odds ratio [OR] = 1.8, p < 0.01) and history of seizures, either remotely or related to acute illness (34% had seizures; OR = 3.0, p < 0.001). If there were no epileptiform findings on EEG, the risk of seizures within 72 hours was between 9% (no clinical risk factors) and 36% (coma and history of seizures). If epileptiform findings developed, the seizure incidence was between 18% (no clinical risk factors) and 64% (coma and history of seizures). In the absence of epileptiform EEG abnormalities, the duration of monitoring needed for seizure risk of <5% was between 0.4 hours (for patients who are not comatose and had no prior seizure) and 16.4 hours (comatose and prior seizure).

INTERPRETATION

The initial risk of seizures on CEEG is dependent on history of prior seizures and presence of coma. The risk of developing seizures on CEEG decays to <5% by 24 hours if no epileptiform EEG abnormalities emerge, independent of initial clinical risk factors. Ann Neurol 2017;82:177-185.

Continuous electroencephalographic-monitoring in the ICU: an overview of current strengths and future challenges

PURPOSE OF REVIEW

In ICUs, numerous physiological parameters are continuously monitored and displayed. Yet, functional monitoring of the organ of primary concern, the brain, is not routinely performed. Despite the benefits of ICU use of continuous electroencephalographic (EEG)-monitoring (cEEG) is increasingly recognized, several issues nevertheless seem to hamper its widespread clinical implementation.

RECENT FINDINGS

Utilization of ICU cEEG has significantly improved detection and characterization of cerebral pathology, prognostication and clinical management in specific patient groups. Potential solutions to several remaining challenges are currently being established. Descriptive EEG-terminology is evolving, whereas logistical issues are dealt with using telemedicine and quantitative EEG trends, training of nonexpert personnel and development of specialized detection algorithms. These concerted solutions are advancing cEEG-registration towards cEEG-monitoring. Notwithstanding these advances, obstacles such as ambiguous EEG-interpretation and differences in treatment based on EEG-findings need yet to be overcome.

SUMMARY

In selected critically ill patient groups, ICU cEEG has clear benefits over (repeated) standard EEG or no functional brain monitoring at all and if available, cEEG should be used. However, several issues preventing optimal ICU cEEG usage persist and should be further explored.

Neurophysiological assessment of brain dysfunction in critically ill patients: an update

The aim of this review was to provide up-to-date information about the usefulness of clinical neurophysiology testing in the management of critically ill patients. Evoked potentials (EPs) and electroencephalogram (EEG) are non-invasive clinical neurophysiology tools that allow an objective assessment of the central nervous system's function at the bedside in intensive care unit (ICU). These tests are quite useful in diagnosing cerebral complications, and establishing the vital and functional prognosis in ICU. EEG keeps a particularly privileged importance in detecting seizures phenomena such as subclinical seizures and non-convulsive status epilepticus. Quantitative EEG (QEEG) analysis techniques commonly called EEG Brain mapping can provide obvious topographic displays of digital EEG signals characteristics, showing the potential distribution over the entire scalp including filtering, frequency, and amplitude analysis and color mapping. Evidences of usefulness of QEEG for seizures detection in ICU are provided by several recent studies. Furthermore, beyond detection of epileptic phenomena, changes of some QEEG panels are early warning indicators of sedation level as well as brain damage or dysfunction in ICU. EPs offer the opportunity for assessing brainstem's functional integrity, as well as subcortical and cortical brain areas. A multimodal use, combining EEG and various modalities of EPs is recommended since this allows a more accurate functional exploration of the brain and helps caregivers to tailor therapeutic measures according to neurological worsening trends and to anticipate the prognosis in ICU.