Continuous Electroencephalographic Monitoring Research Articles

Seizures Are Associated With Brain Injury in Infants Undergoing Extracorporeal Membrane Oxygenation.

Determine seizure frequency and association with neurologic outcomes in infants undergoing extracorporeal membrane oxygenation. Identify patient or clinical factors associated with seizures or brain injury on imaging. Retrospective, single-center study including infants less than 1 year of age, who underwent extracorporeal membrane oxygenation between 2012 and 2017. A total of 104 infants met study criteria including 45 patients with continuous electroencephalographic (EEG) monitoring during their extracorporeal membrane oxygenation run and 59 infants without EEG. Seizures (electrographic-only or electro-clinical) were identified in 18 of the 45 (40%). Among the 18 infants with seizures, 14 (78%) had moderate to severe brain injury, whereas only 44% of those without seizures (12 of 27) on EEG had moderate to severe brain injury (P = .03). Cardiopulmonary resuscitation prior to extracorporeal membrane oxygenation (ECPR), mode of extracorporeal membrane oxygenation, length of stay, survival to discharge, and congenital heart disease were not associated with seizures. One of 10 patients with cyanotic congenital heart disease due to hypoplastic left heart syndrome had seizures compared with 7 of 10 patients with non-hypoplastic left heart syndrome lesions (P = .02). Seizures were associated with moderate to severe brain injury, after adjusting for ECPR and congenital heart disease (P = .04). Electrographic seizures were common in patients undergoing extracorporeal membrane oxygenation and higher than previously reported. Seizures were associated with moderate to severe abnormalities on imaging, after adjusting for ECPR and congenital heart disease. This study adds to recent literature describing the risk of seizures in patients on extracorporeal membrane oxygenation and highlights the presence of brain injuries that may be identified by routine EEG surveillance.

Evaluación de la idoneidad y aplicabilidad clínica de diferentes electrodos para la monitorización aEEG/cEEG en el niño prematuro extremo

IntroductionMonitoring of brain function using continuous electroencephalography (aEEG/cEEG) is an essential tool in the standard care of the term infant, and its use is growing in the premature infant as a biomarker of lesion and brain maturity. However, the placing of the electrodes is a great challenge, particularly in the extremely premature infant, which often discourages neuromonitoring. The aim of this study is to assess the different electrodes available, to select the one that best suits the peculiarities of the extremely premature infant, and evaluate its applicability in clinical practice. Population and methodsWith the aim of designing a neuromonitoring study protocol using aEEG/cEEG in<28 weeks premature infants, an analysis was made of our experience with the type of electrodes available. The electrode that was considered most suitable for this population was chosen by assessing: the need of preparing the scalp, speed in positioning the electrodes, if the application was invasive or not, the possibility of repositioning, risk of skin injuries, sterility of the technique, and durability. The electrode chosen was used for continuous electroencephalographic monitoring started in the first 24h of life, and maintained until at least 72h of life. ResultsThe electrodes evaluated were: subdermal needles, silver cups, and 2 types of self-adhesive electrodes (solid hydrogel and wet gel). The wet gel electrodes were chosen. They were used on 41 neonates with a mean gestational age of 25.8±1.1 weeks. Good stable impedance was rapidly obtained, without the need of excessive manipulations, and no skin injuries were observed. The satisfaction of the staff involved in positioning them was very high. ConclusionThe self-adhesive disposable electrodes with wet gel and integrated cable enabled the electrodes to be positioned rapidly and provided continuous non-invasive and good quality aEEG/cEEG monitoring in the extremely premature infant.

Part 1: Executive Summary: 2020 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care.

Part 1: Executive Summary: 2020 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care.

Validation of a model to predict electroencephalographic seizures in critically ill children.

Electroencephalographic seizures (ESs) are common in encephalopathic critically ill children, but identification requires extensive resources for continuous electroencephalographic monitoring (CEEG). In a previous study, we developed a clinical prediction rule using three clinical variables (age, acute encephalopathy category, clinically evident seizure[s] prior to CEEG initiation) and two electroencephalographic (EEG) variables (EEG background category and interictal discharges within the first 30minutes of EEG) to identify patients at high risk for ESs for whom CEEG might be essential. In the current study, we aimed to validate the ES prediction model using an independent cohort. The prospectively acquired validation cohort consisted of 314 consecutive critically ill children treated in the Pediatric Intensive Care Unit of a quaternary care referral hospital with acute encephalopathy undergoing clinically indicated CEEG. We calculated test characteristics using the previously developed prediction model in the validation cohort. As in the generation cohort study, we selected a 0.10 cutpoint to emphasize sensitivity. The incidence of ESs in the validation cohort was 22%. The generation and validation cohorts were alike in most clinical and EEG characteristics. The ES prediction model was well calibrated and well discriminating in the validation cohort. The model had a sensitivity of 90%, specificity of 37%, positive predictive value of 28%, and negative predictive value of 93%. If applied, the model would limit 31% of patients from undergoing CEEG while failing to identify 10% of patients with ESs. The model had similar performance characteristics in the generation and validation cohorts. A model employing five readily available clinical and EEG variables performed well when validated in a new consecutive cohort. Implementation would substantially reduce CEEG utilization, although some patients with ESs would not be identified. This model may serve a critical role in targeting limited CEEG resources to critically ill children at highest risk for ESs.

Letter: Using Continuous Intracranial Electroencephalography Monitoring to Manage Epilepsy Patients During COVID-19.

Letter: Using Continuous Intracranial Electroencephalography Monitoring to Manage Epilepsy Patients During COVID-19.

Continuous Electroencephalographic Training for Neuroscience Intensive Care Unit Nurses: A Feasibility Study

Use of continuous electroencephalographic (cEEG) monitoring has more than doubled at our institution for the last 4 years. Although intensive care unit cEEG is reviewed remotely by board-certified epileptologists every 4 to 6 hours, there are inherent delays between occurrence, recognition, and treatment of epileptiform activity. Neuroscience intensive care unit (NSICU) nurses are uniquely positioned to monitor cEEG in real time yet do not receive formal training. The purpose of this study was to evaluate the effectiveness of an education program to teach nurses to monitor cEEG, identify a burst suppression pattern, and measure the duration of suppression. We performed a retrospective analysis of pretest and posttest data. All NSICU nurses (40) were invited to complete the pretest (PT-0), with 25 participating. Learning style/preference, demographics, comfort with cEEG, and knowledge of EEG fundamentals were assessed. A convenience cohort of NSICU nurses (13) were selected to undergo EEG training. Posttests evaluating EEG fundamental knowledge were completed immediately after training (PT-1), at 3 months (PT-3), and at 6 months (PT-6). The cohort also completed a burst suppression module after the training, which assessed ability to quantify the duration of suppression. Mean cohort test scores significantly improved after the training (P < .001). All nurses showed improvement in test scores, and 76.9% passed PT-1 (a score of 80% or higher). Reported mean comfort level with EEG also significantly improved after the training (P = .001). There was no significant difference between mean cohort scores between PT-1, PT-3, and PT-6 (all 88.6%; P = 1.000). Mean cohort score from the bust suppression module was 73%, with test scores ranging from 31% to 93%. NSICU nurses can be taught fundamentals of cEEG, to identify a burst suppression pattern, and to quantify the duration of suppression. Further research is needed to determine whether this knowledge can be translated into clinical competency and affect patient care.

Development and Validation of a Seizure Prediction Model in Neonates After Cardiac Surgery

Electroencephalographic seizures (ESs) after neonatal cardiac surgery are often subclinical and have been associated with poor outcomes. An accurate ES prediction model could allow targeted continuous electroencephalographic monitoring (CEEG) for high-risk neonates. ES prediction models were developed and validated in a multicenter prospective cohort where all postoperative neonates who underwent cardiopulmonary bypass (CPB) also underwent CEEG. ESs occurred in 7.4% of neonates (78 of 1053). Model predictors included gestational age, head circumference, single-ventricle defect, deep hypothermic circulatory arrest duration, cardiac arrest, nitric oxide, extracorporeal membrane oxygenation, and delayed sternal closure. The model performed well in the derivation cohort (c-statistic, 0.77; Hosmer-Lemeshow, P= .56), with a net benefit (NB) over monitoring all and none over a threshold probability of 2% in decision curve analysis (DCA). The model had good calibration in the validation cohort (Hosmer-Lemeshow, P= .60); however, discrimination was poor (c-statistic, 0.61), and in DCA there was no NB of the prediction model between the threshold probabilities of 8% and 18%. By using a cut point that emphasized negative predictive value in the derivation cohort, 32% (236 of 737) of neonates would not undergo CEEG, including 3.5% (2 of 58) of neonates with ESs (negative predictive value, 99%; sensitivity, 97%). In this large prospective cohort, a prediction model of ESs in neonates after CPB had good performance in the derivation cohort, with an NB in DCA. However, performance in the validation cohort was weak, with poor discrimination, poor calibration, and no NB in DCA. These findings support CEEG of all neonates after CPB.

Seizure Risk in Patients Undergoing 30-Day Readmission After Continuous EEG Monitoring.

The use of continuous electroencephalographic (cEEG) monitoring has improved the understanding of the seizure risk during acute hospitalization. However, the immediate posthospitalization seizure risk in these patients remains unknown. Patients undergoing 30-day readmission after initial cEEG monitoring were analyzed to fill this knowledge gap. A prospectively maintained cEEG database (January 1, 2015-December 31, 2015) was used to identify adults who underwent a repeat cEEG during their 30-day readmission after cEEG during their index hospitalization (index cEEG). Various demographical, clinical, and cEEG variables were extracted including indication for cEEG: altered mental status and clinical seizure-like events. A total of 57 of the 2,485 (2.3%) adults undergoing index cEEG during the study period had concerns for seizures and underwent repeat cEEG during a 30-day readmission. These patients were almost three times more likely to have suffered electrographic seizure on the index admission (odds ratio, 2.82; 95% confidence interval, 1.54-5.15; P < 0.001) compared with non-readmitted patients. Seizure-like events led to the readmission of 40.4% patients. Close to one in five (19.3%) readmitted patients were found to have an electrographic seizure. Only variable predictive of seizure on readmission was seizure-like events (odds ratio, 6.4; 95% confidence interval, 1.2-33.0; P = 0.02). A small percentage of patients have clinical presentation concerning for seizures with in 30 days after index cEEG. The risk of electrographic seizures in this patient population is higher than patients who have cEEG monitoring but do not undergo a 30-day readmission requiring repeat cEEG. Future research on early identification of patients at risk of 30-day readmission because of concerns for seizure is needed.

Evaluation of an Activity Tracker to Detect Seizures Using Machine Learning.

Currently, the tracking of seizures is highly subjective, dependent on qualitative information provided by the patient and family instead of quantifiable seizure data. Usage of a seizure detection device to potentially detect seizure events in a population of epilepsy patients has been previously done. Therefore, we chose the Fitbit Charge 2 smart watch to determine if it could detect seizure events in patients when compared to continuous electroencephalographic (EEG) monitoring for those admitted to an epilepsy monitoring unit. A total of 40 patients were enrolled in the study that met the criteria between 2015 and 2016. All seizure types were recorded. Twelve patients had a total of 53 epileptic seizures. The patient-aggregated receiver operating characteristic curve had an area under the curve of 0.58 [0.56, 0.60], indicating that the neural network models were generally able to detect seizure events at an above-chance level. However, the overall low specificity implied a false alarm rate that would likely make the model unsuitable in practice. Overall, the use of the Fitbit Charge 2 activity tracker does not appear well suited in its current form to detect epileptic seizures in patients with seizure activity when compared to data recorded from the continuous EEG.

Epileptiform Discharge and Electrographic Seizures during the Hypothermia Phase as Predictors of Rewarming Seizures in Children after Resuscitation.

The aim of this study was to determine the frequency, timing, and predictors of rewarming seizures in a cohort of children undergoing therapeutic hypothermia after resuscitation. We retrospectively reviewed consecutive pediatric patients undergoing therapeutic hypothermia after resuscitation admitted to our pediatric intensive care unit between January 2000 and December 2019. Continuous electroencephalographic monitoring was performed during hypothermia (24 h for cardiac aetiologies and 72 h for asphyxial aetiologies), rewarming (72 h), and then an additional 12 h of normothermia. Thirty comatose children undergoing therapeutic hypothermia after resuscitation were enrolled, of whom 10 (33.3%) had rewarming seizures. Two (20%) of these patients had their first seizure during the rewarming phase. Four (40%) patients had electroclinical seizures, and six (60%) had nonconvulsive seizures. The median time from starting rewarming to the onset of rewarming seizures was 37.3 h (range 6 to 65 h). The patients with interictal epileptiform activity and electrographic seizures during the hypothermia phase were more likely to have rewarming seizures compared to those without interictal epileptiform activity or electrographic seizures (p = 0.019 and 0.019, respectively). Therefore, in high-risk patients, continuous electroencephalographic monitoring for a longer duration may help to detect rewarming seizures and guide clinical management.

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

ObjectiveThe clinical implementation of continuous electroencephalography (CEEG) monitoring in critically ill patients is hampered by the substantial burden of work that it entails for clinical neurophysiologists. Solutions that might reduce this burden, including by shortening the duration of EEG to be recorded, would help its widespread adoption. Our aim was to validate a recently described algorithm of time-dependent electro-clinical risk stratification for electrographic seizure (ESz) (TERSE) based on simple clinical and EEG features. MethodsWe retrospectively reviewed the medical records and EEG recordings of consecutive patients undergoing CEEG between October 1, 2015 and September, 30 2016 and assessed the sensitivity of TERSE for seizure detection, as well as the reduction in EEG time needed to be reviewed. ResultsIn a cohort of 407 patients and compared to full CEEG review, the model allowed the detection of 95% of patients with ESz and 97% of those with electrographic status epilepticus. The amount of CEEG to be recorded to detect ESz was reduced by two-thirds, compared to the duration of CEEG taht was actually recorded. ConclusionsTERSE allowed accurate time-dependent ESz risk stratification with a high sensitivity for ESz detection, which could substantially reduce the amount of CEEG to be recorded and reviewed, if applied prospectively in clinical practice. SignificanceTime-dependent electro-clinical risk stratification, such as TERSE, could allow more efficient practice of CEEG and its more widespread adoption. Future studies should aim to improve risk stratification in the subgroup of patients with acute brain injury and absence of clinical seizures.

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.

Implementation and Early Evaluation of a Quantitative Electroencephalography Program for Seizure Detection in the PICU.

To describe implementation and early evaluation of using quantitative electroencephalography for electrographic seizure detection by PICU clinician staff. Prospective observational study of electrographic seizure detection by PICU clinicians in patients monitored with quantitative electroencephalography. Quantitative electroencephalography program implementation included a continuous education and training package. Continuous quantitative electroencephalography monitoring consisted of two-channel amplitude-integrated electroencephalography, color density spectral array, and raw-electroencephalography. PICU. Children less than 18 years old admitted to the PICU during the 14-month study period and deemed at risk of electrographic seizure. None. Real time electrographic seizure detection by a PICU team was analyzed for diagnostic accuracy and promptness, against electrographic seizure identification by a trained neurophysiologist, retrospectively reading the same quantitative electroencephalography and blinded to patient details. One-hundred one of 1,510 consecutive admissions (6.7%) during the study period underwent quantitative electroencephalography monitoring. Status epilepticus (35%) and suspected hypoxic-ischemic injury (32%) were the most common indications for quantitative electroencephalography. Electrographic seizure was diagnosed by the neurophysiologist in 12% (n = 12) of the cohort. PICU clinicians correctly diagnosed all 12 patients (100% sensitivity and negative predictive value). An additional eleven patients had a false-positive diagnosis of electrographic seizure (false-positive rate = 52% [31-73%]) leading to a specificity of 88% (79-94%). Median time to detect seizures was 25 minutes (5-218 min). Delayed recognition of electrographic seizure (> 1 hr from onset) occurred in five patients (5/12, 42%). Early evaluation of quantitative electroencephalography program to detect electrographic seizure by PICU clinicians suggested good sensitivity for electrographic seizure detection. However, the high false-positive rate is a challenge. Ongoing work is needed to reduce the false positive diagnoses and avoid electrographic seizure detection delays. A comprehensive training program and regular refresher updates for clinical staff are key components of the program.

1147 Sleep Correlates With Improved Functional Outcome In The Intensive Care Unit

Abstract Introduction Disruption of sleep may have significant implications in acute brain injury, functional recovery, and critical illness. Few data exist characterizing sleep architecture in patients admitted to an intensive care unit (ICU). We aim to describe sleep and clinical characteristics in patients with acute brain injury and critical illness. Methods Retrospective analysis was performed in ICU patients who underwent continuous electroencephalographic (EEG) monitoring from 2018-2019. Sleep was scored based on AASM-defined EEG criteria. Clinical variables, EEG characteristics, and modified Ranking Scale (mRS) were collected. Good outcome was defined as mRS&amp;lt;3. Differences were assessed using chi-square analysis and t-test. Results 205 patients were reviewed with a mean age of 57 years (range 18-91) and a majority (57%) were male. Patients carried a primary neurologic/neurosurgical (61%) or medical/surgical (39%) diagnosis. Status epilepticus, subdural hemorrhage, traumatic brain injury, encephalopathy and cardiac arrest accounted for the majority of diagnoses encountered. Only 58 patients (28%) achieved N1 sleep; of these 76.4% achieved N2, 2.8% N3, and none achieved REM. Of those achieving any sleep, 43% had good outcomes versus only 23% in those who did not (t=-7.45, p&amp;lt;0.001). Neurological patients were more likely to attain sleep compared to those with other primary diagnoses (χ 2 (1)=7.08, p=0.008). Centrally acting anesthetics did not account for sleep differences between neurologic and non-neurologic patients (χ² (1)=2.01, p=0.16). However, those with primary brain injury reached sleep more often in the absence of anesthetic use (χ 2 (1)=4.82, p=0.03). The overall mortality was 32% in this cohort. Conclusion Most critically ill patients do not achieve electrophysiologic sleep. Of those who do, N1/N2 stages are seen most often. Neurological patients were more likely to sleep, and achieving any sleep was associated with improved functional outcome. Further studies are needed to determine whether sleep augmentation in the critically ill impacts functional outcome. Support N/A.

Targeted temperature management for postcardiac arrest syndrome

Neurocritical care management to improve neurologic outcome for postcardiac arrest syndrome (PCAS) has focused considerably on targeted temperature management (TTM). TTM attenuates the destructive processes following ischemia/reperfusion in PCAS. The principal indication of TTM is a patient with sustained coma after return of spontaneous circulation (ROSC). TTM can be strongly recommended with a target temperature between 32°C and 36°C for patients with shockable rhythm and out-of-hospital cardiac arrest (OHCA) and weakly recommended for patients with initial asystole or pulseless electrical activity with OHCA and those with in-hospital cardiac arrest. TTM is induced and maintained using a cooling device with body temperature feedback under appropriate analgosedation. It requires the intensive management of various systemic respiratory, circulatory, and metabolic parameters that control shivering to prevent secondary brain damage. Considering the cerebral perfusion pressure, it is suggested that the mean arterial pressure should be particularly maintained over 80 mm Hg. Seizure management, including continuous electroencephalography monitoring, is also needed. Finally, we must continue the above mentioned care during and after the rewarming phase, because high fever and shivering may appear again during this period. Furthermore, neurological prognostication should be performed at least 72 hours after ROSC through clinical investigations and multimodal testing without sedation. Key Words: Hypothermia, induced; Critical care; Neurology; Shivering; Brain injuries

Post-acute symptomatic seizure (PASS) clinic: A continuity of care model for patients impacted by continuous EEG monitoring.

ObjectiveWe present a model for the outpatient care of patients undergoing continuous electroencephalography (cEEG) monitoring during a hospitalization, named the post‐acute symptomatic seizure (PASS) clinic. We investigated whether establishing this clinic led to improved access to epileptologist care.MethodsAs part of the PASS clinic initiative, electronic health record (EHR) provides an automated alert to the inpatient care team discharging adults on first time antiepileptic drug (AED) after undergoing cEEG monitoring. The alert explains the rationale and facilitates scheduling for a PASS clinic appointment, three‐month after discharge, along with a same‐day extended (75 minutes) EEG. We compared the initial epilepsy clinic visits by patients undergoing cEEG in 2017, before (“Pre‐PASS” period and cohort) and after (“PASS” period and cohort) the alert went live in the EHR.ResultsOf the 170 patients included, 68 (40%) suffered a seizure during the mean follow‐up of 20.9 ± 10 months. AEDs were stopped or reduced in 66 out of 148 (44.6%) patients discharged on AEDs. Pre‐PASS cohort included 45 patients compared to 145 patients in the PASS cohort, accounting for 5.8% and 9.9% of patients, respectively, who underwent cEEG during the corresponding periods (odds ratio [OR] = 1.8, 95% CI = 1.26‐2.54, P = .001). The two cohorts did not differ in terms of electrographic or clinical seizures. The PASS cohort was significantly more likely to be followed up within 1‐6 months of discharge (OR = 4.6, 95% CI = 2.1‐10.1, P < .001) and have a pre‐clinic EEG (51.2% vs 11.1%; OR = 8.39, 95% CI = 3.1‐22.67, P < .001).SignificancePASS clinic, a unique outpatient transition of care model for managing patients at risk of acute symptomatic seizure led to an almost twofold increase in access to an epileptologist. Future research should address the wide knowledge gap about the best post‐hospital discharge management practices for these patients.

Electrode-associated Skin Lesions after Continuous Electroencephalography Monitoring

Electrode-associated Skin Lesions after Continuous Electroencephalography Monitoring

Efficacy and economic evaluation of delivery of care with tele-continuous EEG in critically ill patients: a multicentre, randomised controlled trial (Tele-cRCT) study protocol

IntroductionSome critically ill patients are confirmed by continuous electroencephalography (cEEG) monitoring that non-convulsive seizure (NCS) and/or non-convulsive status epilepticus (NCSE) are causes of their depressed level of consciousness. Shortage of...

Usefulness of continuous electroencephalographic monitoring for psychogenic non-epileptic seizure

Usefulness of continuous electroencephalographic monitoring for psychogenic non-epileptic seizure

Continuous electroencephalography (cEEG) monitoring in critically ill patients

Critically ill patients need to be treated quickly, carefully, precisely and comprehensively. Neurological status should be concerned because of the severe impact that occurs if it is not immediately handled, that is permanent disability or death. The neurologic problem like non convulsive seizures may not distinctly clear. It is difficult to assess non-convulsive seizure only by clinical physical examination. Continuous Electroencephalography (cEEG) is a useful to measure electrical brain activity. It could be used to identify seizure, cerebral ischemic, monitor and evaluate the medication, and prognostic of the diseases. This equipment is noninvasive and could give real-time information about the patient condition. The complex procedure and availability of skilled technician and competent reviewer, is the barrier to optimize this tools. Besides the benefit and challenge, cEEG is recommended to be used in critically ill patients’ management.&#x0D; Abbreviations used: ADR – Alpha to Delta Ratio; CBF – Cerebral Blood Flow; cEEG – continuous Electroencephalography; CNS – Central Nervous System; CT – Computed Tomography; DCI – Delayed Cerebral Ischemia; EEG – electroencephalography; GCS – Glasgow Coma Scale; ICU – Intensive Care Unit; MRI - Magnetic Resonance Imaging; NCS – Non-Convulsive Seizures; NCSE – Non-Convulsive Status Epilepticus; PAV – Percent Alpha Variability; PEDs – Periodic Epileptiform Discharges; RAV – Relative Alpha Variability; TBI – Traumatic Brain Injury; TCD – Transcranial Doppler&#x0D; Received: 19 Oct 2018Reviewed: 28 Oct 2018Accepted: 5 Nov 2018&#x0D; Citation: Purwandhono A, Hidayati HB, Machin A, Islamiyah WR. Continuous electroencephalography (cEEG) monitoring in critically ill patients. Anaesth Pain &amp; Intensive Care 2018;22 Suppl 1:S46-S52