Underwent EEG Monitoring Research Articles

Neurophysiological insights into sequential decision-making: exploring the secretary problem through ERPs and TBR dynamics.

Decision-making under uncertainty, a cornerstone of human cognition, is encapsulated by the "secretary problem" in optimal stopping theory. Our study examines this decision-making challenge, where participants are required to sequentially evaluate and make irreversible choices under conditions that simulate cognitive overload. We probed neurophysiological responses by engaging 27 students in a secretary problem simulation while undergoing EEG monitoring, focusing on Event-Related Potentials (ERPs) P200 and P400, and Theta to Beta Ratio (TBR) dynamics.Results revealed a nuanced pattern: the P200 component's amplitude declined from the initial to the middle offers, suggesting a diminishing attention span as participants grew accustomed to the task. This attenuation reversed at the final offer, indicating a heightened cognitive processing as the task concluded. In contrast, the P400 component's amplitude peaked at the middle offer, hinting at increased cognitive evaluation, and tapered off at the final decision. Additionally, TBR dynamics illustrated a fluctuation in attentional control and emotional regulation throughout the decision-making sequence, enhancing our understanding of the cognitive strategies employed.The research elucidates the dynamic interplay of cognitive processes in high-stakes environments, with neurophysiological markers fluctuating significantly as participants navigated sequential choices. By correlating these fluctuations with decision-making behavior, we provide insights into the evolving strategies from heightened alertness to strategic evaluation. Our findings offer insights that could inform the use of neurophysiological data in the development of decision-making frameworks, potentially contributing to the practical application of cognitive research in real-life contexts.

Changes In Objective Characteristics In Brain Electrical Activity In Newborns As A Function Of Birth Weight

The aim of the present study was to detect characteristic features of oscillatory electrical activity of the brain in the first day of postnatal life depending on the weight of newborns. Material — Eighteen neonates of conditionally normal gestational age (37.7±1.5 weeks) weighing 2500±720 g were included in the study. All neonates were children of first births of mothers aged 18-35 years, all pregnancies were physiologic, conventionally normal, without significant complications. The height of the newborns was 47±4.643 cm and head circumference was33.0±2.908 cm. The Apgar score at delivery was 7-9 points. All newborns were divided among groups 1 (weight: 2850-4000 gr), 2 (weight: 2000-2800 gr) and 3 (1200-2000 gr). Each newborn underwent EEG monitoring (EEG, monopolar recording, channels C3 and C4) for 40 minutes during the first 12 hours after birth. Methods — Automatic processing of EEG was performed without separating the monitoring records into sleep and wakefulness stages. Oscillatory patterns were calculated for each EEG channel based on the continuous wavelet transform method. Statistical estimations of the number and duration of oscillatory patterns developing in different EEG frequency ranges were performed. Results — A strong correlation was found between neonatal birth weight and integral characteristics of the number \ duration of oscillatory patterns in the low-frequency band [4; 6] Hz (r=-0.878\0.920). Practically healthy newborns with different birth weights show statistically different EEG characteristics in the [4; 6] Hz band in the first 12 hours after birth (p-value≤0.005). Conclusion — Electrical activity of the brain varies significantly depending on the weight of newborns immediately after birth. Monitoring of EEG signals according to the proposed algorithm may become the basis for the development of additional tools for early detection of possible disorders of neurological development of the newborn.

Analysis of the relationship between EEG burst suppression and poor prognosis in children under general anaesthesia: study protocol for a prospective, observational, single-centre study

BackgroundEmergence delirium (ED) in children refers to the immediate postoperative period when children experience decreased perception of their surroundings, accompanied by disorientation and altered perception. Burst suppression (BS) is recognised as periods longer than 0.50 s during which the EEG does not exceed approximately + 5.0 mV, which is an electroencephalographic state associated with profound inactivation of the brain. Our primary objective was to determine the association between BS on electroencephalogram (EEG) under general anaesthesia with postoperative wake-up delirium and multiple adverse outcomes, such as prolonged awakening and extubation.MethodsIn this prospective, observational cohort study at Beijing Children’s Hospital, Capital Medical University, Beijing, China, children aged 6 months to 9 years who underwent surgery under general anaesthesia and underwent EEG monitoring between January 2022 and January 2023 were included. Patients’ prefrontal EEGs were recorded intraoperatively as well as analysed for the occurrence and duration of BS and scored postoperatively for delirium by the PAED scale, with a score of no less than 10 considered as having developed wake-up delirium.DiscussionThis study identified a relationship between EEG BS and postoperative awakening delirium under general anaesthesia in children and provides a novel preventive strategy for postoperative awakening delirium and multiple adverse outcomes in paediatric patients.Trial registrationChinese Clinical Trial Registry, ChiCTR2200055256. Registered on January 5, 2022.

Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation.

Seizures (SZs) and other SZ-like patterns of brain activity can harm the brain and contribute to in-hospital death, particularly when prolonged. However, experts qualified to interpret EEG data are scarce. Prior attempts to automate this task have been limited by small or inadequately labeled samples and have not convincingly demonstrated generalizable expert-level performance. There exists a critical unmet need for an automated method to classify SZs and other SZ-like events with expert-level reliability. This study was conducted to develop and validate a computer algorithm that matches the reliability and accuracy of experts in identifying SZs and SZ-like events, known as "ictal-interictal-injury continuum" (IIIC) patterns on EEG, including SZs, lateralized and generalized periodic discharges (LPD, GPD), and lateralized and generalized rhythmic delta activity (LRDA, GRDA), and in differentiating these patterns from non-IIIC patterns. We used 6,095 scalp EEGs from 2,711 patients with and without IIIC events to train a deep neural network, SPaRCNet, to perform IIIC event classification. Independent training and test data sets were generated from 50,697 EEG segments, independently annotated by 20 fellowship-trained neurophysiologists. We assessed whether SPaRCNet performs at or above the sensitivity, specificity, precision, and calibration of fellowship-trained neurophysiologists for identifying IIIC events. Statistical performance was assessed by the calibration index and by the percentage of experts whose operating points were below the model's receiver operating characteristic curves (ROCs) and precision recall curves (PRCs) for the 6 pattern classes. SPaRCNet matches or exceeds most experts in classifying IIIC events based on both calibration and discrimination metrics. For SZ, LPD, GPD, LRDA, GRDA, and "other" classes, SPaRCNet exceeds the following percentages of 20 experts-ROC: 45%, 20%, 50%, 75%, 55%, and 40%; PRC: 50%, 35%, 50%, 90%, 70%, and 45%; and calibration: 95%, 100%, 95%, 100%, 100%, and 80%, respectively. SPaRCNet is the first algorithm to match expert performance in detecting SZs and other SZ-like events in a representative sample of EEGs. With further development, SPaRCNet may thus be a valuable tool for an expedited review of EEGs. This study provides Class II evidence that among patients with epilepsy or critical illness undergoing EEG monitoring, SPaRCNet can differentiate (IIIC) patterns from non-IIIC events and expert neurophysiologists.

Evaluation of pre- and post-transplant electroencephalographic examination in hematopoietic stem cell transplant patients

<p>Haemato&shy;poietic stem cell transplantation (HSCT) is one of the most effective treatment methods for many malignant and non-malignant diseases. In this study, we aimed to detect electroencephalographic (EEG) anomalies at an early stage in patients who underwent allogeneic and autologous HSCT and required the management of potentially life-threatening non-convulsive seizures.</p>. <p>The study was conducted with 53 patients. The age, gender, HSCT type (allogeneic or autologous), and treatment regimens applied before and after HSCT were recorded. All patients underwent EEG monitoring twice, once on the first day of hospitalization and again one week after conditioning regimens began and HSCT was performed.</p>. <p>When the pre-transplant EEG findings were examined, 34 (64.2%) patients had normal EEGs and 19 (35.8%) had abnormal EEGs. After transplantation, 27 (50.9%) had normal EEG findings, 16 (30.2%) had a basic activity disorder, 6 (11.3%) had a focal anomaly, and 4 (7.5%) had a ge&shy;ne&shy;ra&shy;lised anomaly. In the allogeneic group, the anomaly rate in post-transplant EEGs was significantly higher than that in the auto&shy;lo&shy;gous group (p&lt;0.05).</p>. <p>It is important to consider the likelihood of epileptic seizures in the clinical follow-up of HSCT patients. EEG monitoring is crucial for the early diagnosis and treat&shy;ment of such non-convulsive clinical ma&shy;ni&shy;festations.&nbsp;</p>.

High incidence of epileptiform activity in adults undergoing extracorporeal membrane oxygenation

ObjectiveThe prevalence of seizures and other types of epileptiform brain activity in patients undergoing extracorporeal membrane oxygenation (ECMO) is unknown. We aimed to estimate the prevalence of seizures and ictal-interictal continuum patterns in patients undergoing electroencephalography (EEG) during ECMO. MethodsRetrospective review of a prospective ECMO registry from 2011-2018 in a university-affiliated academic hospital. Adult subjects who had decreased level of consciousness and underwent EEG monitoring for seizure screening were included. EEG classification followed the American Clinical Neurophysiology Society criteria. Poor neurological outcome was defined as a Cerebral Performance Category of 3–5 at hospital discharge. ResultsThree hundred and ninety-five subjects had ECMO, and one hundred and thirteen (28.6%) had EEG monitoring. Ninety-two (23.3%) subjects had EEG performed during ECMO and were included in the study (average EEG duration 54 h). Veno-arterial ECMO was the most common cannulation strategy (83%) and 26 (28%) subjects had extracorporeal cardiopulmonary resuscitation. Fifty-eight subjects (63%) had epileptiform activity or ictal-interictal continuum patterns on EEG, including three (3%) subjects with nonconvulsive status epilepticus, 33 (36%) generalized periodic discharges, and 4 (5%) lateralized periodic discharges. Comparison between subjects with or without epileptiform activity showed comparable in-hospital mortality (57% vs. 47%, p = 0.38) and poor neurological outcome (and 56% and 36%, p = 0.23). Twenty-seven subjects (33%) had acute neuroimaging abnormalities (stroke N = 21). ConclusionsSeizures and ictal-interictal continuum patterns are commonly observed in patients managed with ECMO. Further studies are needed to evaluate whether epileptiform activity is an actionable target for interventions. SignificanceEpileptiform and ictal-interictal continuum abnormalities are frequently observed in patients supported with ECMO undergoing EEG monitoring.

Disorders of Neuronal Migration/Organization Convey the Highest Risk of Neonatal Onset Epilepsy Compared With Other Congenital Brain Malformations

BackgroundAlthough seizures in neonates are common and often due to acute brain injury, 10-15% are unprovoked from congenital brain malformations. A better understanding of the risk of neonatal-onset epilepsy by the type of brain malformation is essential for counseling and monitoring. MethodsIn this retrospective cohort study, we evaluated 132 neonates with congenital brain malformations and their risk of neonatal-onset epilepsy. Malformations were classified into one of five categories based on imaging patterns on prenatal or postnatal imaging. Infants were monitored with continuous video EEG (cEEG) for encephalopathy and paroxysmal events in addition to abnormal neuroimaging. ResultsSeventy-four of 132 (56%) neonates underwent EEG monitoring, and 18 of 132 (14%) were diagnosed with neonatal-onset epilepsy. The highest prevalence of epilepsy was in neonates with disorders of neuronal migration/organization (9/34, 26%; 95% confidence interval [CI] = 13-44%), followed by disorders of early prosencephalic development (6/38, 16%; 95% CI = 6-31%), complex total brain malformations (2/16, 13%; 95% CI = 2-38%), and disorders of midbrain/hindbrain malformations (1/30, 3%; 95% CI = 0-17%). Of neonates with epilepsy, 5 of 18 (28%) had only electrographic seizures, 13 of 18 (72%) required treatment with two or more antiseizure medicines (ASMs), and 7 of 18 (39%) died within the neonatal period. ConclusionOur results demonstrate that disorders of neuronal migration/organization represent the highest-risk group for early-onset epilepsy. Seizures are frequently electrographic only, require treatment with multiple ASMs, and portend a high mortality rate. These results support American Clinical Neurophysiology Society recommendations for EEG monitoring during the neonatal period for infants with congenital brain malformations.

Development and validation of Auto-Neo-electroencephalography (EEG) to estimate brain age and predict report conclusion for electroencephalography monitoring data in neonatal intensive care units

BackgroundElectroencephalography (EEG) monitoring is widely used in neonatal intensive care units (NICUs). However, conventional EEG report generation processes are time-consuming and labor-intensive. Therefore, an automatic, objective, and comprehensive pipeline for brain age estimation and EEG report conclusion prediction is urgently needed to assist clinician’s decision-making.MethodsWe recruited patients who underwent EEG monitoring from the NICU at Children’s Hospital of Fudan University from Jan. 2016 to Mar. 2018. A total of 1,851 subjects were enrolled, including the patient’s conceptional age (CA) and the clinical EEG report conclusion (normal, slightly abnormal, moderately abnormal, or severely abnormal). A total of 1,591 subjects were used to generate predictive models and 260 were used as the validation dataset. We developed Auto-Neo-EEG (an automatic prediction system to assist clinical neonatal EEG report generation), including signal feature extraction, supervised machine learning realized by gradient boosted models, to estimate brain age and predict EEG report conclusion.ResultsThe predicted results from the validation dataset were compared with the clinical observations to assess the performance. In the independent validation dataset, the model could achieve accordance 0.904 on estimating brain age for neonates with normal clinical EEG report conclusion, and differences between the predicted and observed brain age were strongly related with EEG report conclusion abnormality. Further, as for the EEG report conclusion prediction, the model could achieve area under the curve (AUC) of 0.984 for severely abnormal situations, and 0.857 for moderately abnormal ones.ConclusionsThe Auto-Neo-EEG has the high accuracy of estimating brain age and EEG report conclusion, which can potentially greatly accelerate the EEG report generation processes assist in clinical decision making.

Electrocerebral Signature of Cardiac Death.

Electroencephalography (EEG) findings following cardiovascular collapse in death are uncertain. We aimed to characterize EEG changes immediately preceding and following cardiac death. We retrospectively analyzed EEGs of patients who died from cardiac arrest while undergoing standard EEG monitoring in an intensive care unit. Patients with brain death preceding cardiac death were excluded. Three events during fatal cardiovascular failure were investigated: (1) last recorded QRS complex on electrocardiogram (QRS0), (2) cessation of cerebral blood flow (CBF0) estimated as the time that blood pressure and heart rate dropped below set thresholds, and (3) electrocerebral silence on EEG (EEG0). We evaluated EEG spectral power, coherence, and permutation entropy at these time points. Among 19 patients who died while undergoing EEG monitoring, seven (37%) had a comfort-measures-only status and 18 (95%) had a do-not-resuscitate status in place at the time of death. EEG0 occurred at the time of QRS0 in five patients and after QRS0 in two patients (cohort median - 2.0, interquartile range - 8.0 to 0.0), whereas EEG0 was seen at the time of CBF0 in six patients and following CBF0 in 11 patients (cohort median 2.0min, interquartile range - 1.5 to 6.0). After CBF0, full-spectrum log power (p < 0.001) and coherence (p < 0.001) decreased on EEG, whereas delta (p = 0.007) and theta (p < 0.001) permutation entropy increased. Rarely may patients have transient electrocerebral activity following the last recorded QRS (less than 5min) and estimated cessation of cerebral blood flow. These results may have implications for discussions around cardiopulmonary resuscitation and organ donation.

Status epilepticus and other EEG findings in patients with COVID-19: A case series

PurposeNeurological manifestations of COVID-19 infection include impaired consciousness, strokes, and seizures. Limited reports describing EEG abnormalities in patients with COVID-19 have been published. These articles reported nonspecific encephalopathic patterns, epileptiform discharges, and rarely seizures. Our primary aim was to assess EEG abnormalities in patients with COVID-19 and evaluate for epileptiform activity or seizures. MethodsWe identified five critically ill adult patients with COVID-19 who underwent EEG monitoring. All patients had Ceribell™ rapid response EEG initially and two continued with conventional long-term video EEG. ResultsAll 5 patients had encephalopathy and 3 also had seizure-like movements, thus prompting EEG monitoring. EEGs all showed nonspecific markers of encephalopathy including diffuse slowing and generalized rhythmic delta activity. Two also had epileptiform discharges reaching 2−3 Hz at times, with one patient in nonconvulsive status epilepticus and the other developing clinical status epilepticus with myoclonic movements. EEG and clinical symptoms improved with anti-seizure medications. ConclusionStatus epilepticus was present in 2 out of our cohort of 5 critically ill patients who underwent EEG monitoring. These findings highlight the importance of EEG monitoring in high-risk patients with COVID-19 and encephalopathy. EEG recordings in such patients can identify pathological patterns that will benefit from treatment with anti-seizure medications.

EEG Findings in Coronavirus Disease.

Neurologic manifestations of coronavirus disease (COVID-19) such as encephalopathy and seizures have been described. To our knowledge, detailed EEG findings in COVID-19 have not yet been reported. This report adds to the scarce body of evidence. We identified eight COVID-19 positive patients who underwent EEG monitoring in our hospital system. EEGs were most commonly ordered for an altered level of consciousness, a nonspecific neurologic manifestation. We observed generalized background slowing in all patients and generalized epileptiform discharges with triphasic morphology in three patients. Focal electrographic seizures were observed in one patient with a history of focal epilepsy and in another patient with no such history. Five of eight patients had a previous diagnosis of epilepsy, suggesting that pre-existing epilepsy can be a potential risk factor for COVID-19-associated neurological manifestations. Five of eight patients who underwent EEG experienced a fatal outcome of infection. Our findings underscore previous observations that neurologic manifestations are common in severe cases. COVID-19 patients with epilepsy may have an increased risk of neurological manifestations and abnormal EEG.

Non-convulsive seizures in the encephalopathic critically ill cancer patient does not necessarily portend a poor prognosis

BackgroundNon-convulsive status epilepticus (NCSE) is present in 10–30% of ICU patients with altered mental status (AMS) and is associated to poor outcomes. To our knowledge, there is no data describing the prevalence and outcomes of critically ill cancer patients with AMS associated to non-convulsive seizures (NCS) or NCSE. We aim to describe the outcomes and risk factors of critically ill cancer patients with encephalopathy associated with non-convulsive seizures (NCS).MethodsThis is a 3-year prospective observational study in a mixed oncological ICU at MD Anderson Cancer Center. Data of ICU patients with moderate to severe encephalopathy (Glasgow Coma Score < 13) that underwent EEG monitoring to rule out NCS were collected. Multivariate logistic regression was performed to identify risk factors and outcomes.ResultsOf the 317 patients with encephalopathy who underwent EEG monitoring, 14.5% had NCS. Known risk factors such as sepsis, CNS infection, antibiotics, and cardiac arrest were not associated with increased risk of NCS. Patients with NCS were more likely to have received recent chemotherapy (41.3% vs 21.4%; p = 0.0036), have a CNS disease (39% vs 24.4%; p = 0.035), and abnormal brain imaging (60.9% vs 44.6%; p = 0.041). Patients with lower SOFA scores, normal renal function, and absence of shock were likely to have NCS as the cause of their encephalopathy (p < 0.03). After multivariate analysis, only abnormal brain imaging and absence of renal failure were associated with NCS. Mortality was significantly lower in patients with non-convulsive seizures when compared to those without seizures (45.7% vs 64%; p = 0.022); however, there was no significant association of seizures and mortality on a multivariable logistic regression analysis.ConclusionsNCS in critically ill cancer patients is associated with abnormalities on brain imaging and lower prevalence of organ failure. Diagnosis and treatment of NCS should be a priority in encephalopathic cancer patients, as they can have lower mortality than non-seizing patients. Opposite to other populations, NCS should not be considered a poor prognostic factor in critically ill encephalopathic cancer patients as they reflect a reversible cause for altered mentation.

Electroencephalographic patterns preceding cardiac arrest in neonates following cardiac surgery

AimTo identify EEG changes that could predict impending cardiac arrest (CA) in neonates with congenital heart disease undergoing postoperative continuous EEG monitoring. MethodsSingle-center observational study of neonates who underwent cardiac surgery and had CA postoperatively while undergoing EEG monitoring from 2012-2018. Clinical data were extracted from the medical record. EEG backgrounds were evaluated at defined time-points using standardized terminology. ResultsWe assessed 22 neonates. The median gestational age was 38.7 weeks (IQR 37.6, 39), the median age at surgery was 5 days (IQR 2, 8), 12 patients (55%) underwent repair for hypoplastic left heart syndrome, and the median time from cardiac intensive care unit arrival postoperatively to CA was 9.5 h (IQR 7, 23). The initial EEG background was abnormal in 15 (68%). All 22 neonates (100%) had worsening of the EEG background prior to initiation of chest compressions for CA at a median of 3 min (IQR 1.5, 3). Eighteen neonates (82%) had an EEG change more than 1 min prior to chest compressions. The EEG backgrounds immediately prior to CA were continuous low voltage in 1 (5%), excessive discontinuity in 8 (36%), burst-suppression in 2 (9%), and low voltage suppression in 11 (50%). ConclusionEEG background was abnormal in 68% of neonates at EEG monitoring onset and worsened in all minutes before CA. EEG background changes may be an early sign of impending CA and indicative of developing cerebral dysfunction. Further study is needed to determine whether rapid identification of EEG changes could drive implementation of interventions to prevent CA.

Timing and modes of death after pediatric out-of-hospital cardiac arrest resuscitation

AimTo determine the timing and modes of death of children admitted to a pediatric critical care unit (PICU) of a tertiary care center after an out-of-hospital cardiac arrest (OHCA). MethodsThis is a retrospective descriptive study at a tertiary care PICU of all consecutive patients <18 years old who received ≥1 min of chest compressions, had return of spontaneous circulation (ROSC) for ≥20 min, and were admitted to the PICU after an OHCA. Modes of death were classified as brain death (BD), withdrawal due to neurologic prognosis (W/D-neuro), withdrawal for refractory circulatory failure (W/D-RCF), and re-arrest without ROSC (RA). Results191 consecutive patients were admitted to the PICU from February 2005 to May 2013 after an OHCA. Eighty-six(45%) patients died prior to discharge: BD in 47%(40/86), W/D-neuro in 34%(29/86), W/D-RCF in 10%(9/86), and RA in 9%(8/86). Time to death was longer for patients with W/D-neuro: 4 days [1, 5] and BD 4 days [1, 5](p < 0.01) as opposed to those with W/D-RCF (1 day[1, 2]) and RA(1 day[0.5, 1]). Of patients who underwent W/D-neuro, 9/29(31%) died within 3 days of PICU admission and 20/29(69%) ≥3 days. Of patients who died after W/D-neuro, 12/29(41%) received therapeutic hypothermia, 27/29(93%) underwent EEG monitoring, 21/29(72%) had a brain CT, and 13/29(45%) had a brain MRI. All MRIs showed signs of hypoxic-ischemic injury. ConclusionNeurologic injury was the most common mode of death post-resuscitation care OHCA after in a tertiary care center PICU. Neurologic prognostication impacts the outcome of a large proportion of patients after OHCA, and further studies are warranted to improve its reliability.

Non-convulsive seizures and electroencephalography findings as predictors of clinical outcomes at a tertiary intensive care unit in Saudi Arabia

ObjectiveElectroencephalography (EEG) in the intensive care unit (ICU) is often done to detect non-convulsive seizures (NCS). The outcome of ICU patients with NCS strongly depends on the underlying etiology. The implication of NCS and other EEG findings on clinical outcome independent from their etiology is not well understood and our aim to investigate it. Patients and MethodsWe retrospectively identified all adult patients in the ICU who underwent EEG monitoring between January 2008 and December 2011. The main goals were to define the rate of NCS or non-convulsive status epilepticus (NCSE) occurrence in our center among patients who underwent EEG monitoring and to examine if NCS/NCSE are associated with poor outcome [defined as death or dependence] with and without adjustment for underlying etiology. The rate of poor outcome among different EEG categories were also investigated. ResultsDuring the study period, 177 patients underwent EEG monitoring in our ICU. The overall outcome was poor in 62.7% of those undergoing EEG. The rate of occurrence of NCS/NCSE was 8.5% and was associated with poor outcome in 86.7% with an odds ratio (OR) of 5.1 (95% confidence interval [CI] 1.09–23.8). This association was maintained after adjusting for underlying etiologies with OR 5.6 (95% CI 1.05–29.6). The rate of poor outcome was high in the presence of periodic discharges and sharp and slow waves of 75% and 61.5%, respectively. ConclusionsOur cohort of ICU patients undergoing EEGs had a poor outcome. Those who developed NCS/NCSE experienced an even worse outcome regardless of the underlying etiology.

F16. Standardizing neonatal EEG report based on expert consensus report

Introduction Continuous EEG monitoring provides valuable diagnostic and prognostic information for critically ill neonates that are at high risk of adverse neurological sequelae. The EEG of a developing brain is unique. The parameters used to monitor brain electrophysiological functions in neonates are different from fully mature brains. The EEG report should contain all these essential parameters. Consensus guidelines were published in 2013(1) to standardize the neonatal EEG reports for neurophysiologist, bedside physicians, and clinical researchers. In this study, we compared our neonatal EEG reports to consensus guidelines and attempted to modify it to include all clinically relevant information. Methods We retrospectively reviewed EEG reports of ten (10) neonates that underwent EEG monitoring at Duke Neonatal ICU from March 2017 to June 2017. We compared these reports to consensus guideline published in 2013. We then created a new standardized neonatal EEG report. It included all of the parameters suggested in the guidelines such as characteristics of background activity, awake and sleep state, normal graphoelements, EEG transient patterns and seizure burden. The updated EEG template was implemented in July 2017. We conducted another chart review from September 2017 to November 2017 for ten (10) more EEG reports. We compared the results of both chart reviews. Results From the consensus statement, we identified 20 parameters that were deemed essential for a neonatal EEG report The first chart review from March 2017 to June 2017 of ten patient suggested that majority of reports were inadequate or missing critical information. Out of 20 parameters, only three were regularly documented. After three months of implementing the updated EEG report, the quality of documentation improved. Out of 20 parameters, 18 were consistently documented (100%). One parameter was documented 80% of the time, and only one parameter was reported 20% of the time. Conclusion Our chart review suggested documentation for neonatal EEG reports was lagging behind what was recommended in the consensus report. We made a template in the electronic health record to include all of the important parameters suggested by ACNS guidelines. On a follow-up chart review we noted a marked improvement in our documentation of neonatal EEGs. The standardize EEG reports for neonates will improve patient care, caregiver communication, and future clinical research.The neonatal EEG template made can be used in other institutions to achieve similar results.

Clinical and Electrographic Correlates of Bilateral Independent Periodic Discharges.

Periodic discharges (PDs) are EEG patterns denoting brain dysfunction and ictal tendency. Their exact meaning regarding etiology and outcomes is not well known. In particular, bilateral independent PDs (BIPDs) are poorly described. We performed a retrospective, multicenter study evaluating neuroimaging, epileptic, clinical, and EEG correlates of BIPDs. Twenty-five patients studied with a mean Glasgow Coma Scale 6.5 and modified Rankin scale 3.9 who underwent EEG monitoring, mean duration 287 hours (range 0.75-3,216). Most common causes of BIPDs were cardiac arrest, Central Nervous System infections, and acute/chronic ischemic/hemorrhagic stroke. Most had subcortical and cortical injuries on neuroimaging. Most of the PDs ranged from 0.5 to 2 Hz in frequency, were of multiple phase types, and localized to the frontal head regions. Eighteen of 25 patients had clinical or electrographic seizures. There was a trend toward seizures in those with BIPDs with a history of epilepsy (P = 0.08) and acute metabolic dysfunction (P = 0.08), particularly with coincident acute structural lesions (P = 0.05). Seizures were predicted by bilaterally symmetric frequencies (P = 0.02) and trended toward higher likelihood with PD frequency <2 Hz (P = 0.08). Two of 25 patients survived past discharge with modified Rankin scale <3. Cardiac arrest was associated with withdrawal of life-sustaining therapy (P < 0.001). BIPDs arise from acute and chronic neurologic injuries, often associated with metabolic dysfunction. Outcomes are poor in this population. Seizures are common, particularly in patients with PDs that are of a lower frequency or are symmetric in frequency. Further study is warranted to evaluate the association between BIPDs and seizures, as well as functional and longer term outcomes.

Equivocal significance of post-ictal generalized EEG suppression as a marker of SUDEP risk

PurposeOur objective was to determine the significance of PGES as a possible EEG marker of increased risk for SUDEP and explore factors that influence PGES. MethodsWe identified 17 patients who died due to definite or probable SUDEP and 52 living control patients with drug resistant focal epilepsy who underwent EEG monitoring and least one seizure recorded on EEG. We reviewed 305 seizures on EEG and when available, on video, for presence or absence of PGES, the duration of PGES immediately after seizure end, seizure type, state seizure occurred (sleep vs. wake), tonic duration and time from seizure onset to initial nursing intervention. We noted that majority (93% in SUDEP group and 83% living controls) with PGES had additional brief bursts of suppression. We measured the time from the end of seizure to end of last brief suppression to determine the time to final PGES. ResultsSUDEP patients had statistically significant shorter PGES duration compared to living controls (unadjusted: −32.8s, 95%CI[−54.5, −11.2], adjusted: −39.5s, 95% CI[−59.4, −19.6]). SUDEP status was associated with longer time to final PGES compare to living controls, but this was not statistically significant. Earlier nursing intervention was associated with shorter seizure duration. PGES occurred only after GCS. Time to nursing intervention, tonic duration or state did not have a statistically significant effect on PGES. ConclusionsPGES is an equivocal marker of increased SUDEP risk. Earlier nursing intervention is associated with shorter seizure duration and may play a role in reducing risk of SUDEP.

Seizures and Epileptiform Discharges in Patients With Acute Subdural Hematoma.

Subdural hematomas (SDH) are associated with seizures and epileptiform discharges, but little is known about the prevalence and impact of seizures, status epilepticus (SE), and epileptiform discharges on outcomes in patients with isolated acute SDH (aSDH). Continuous EEG reports from 76 adult patients admitted to Rush University Medical Center with aSDH between January 2009 and March 2012 were reviewed. Clinical and radiographic findings, comorbidities, treatment, and outcome parameters, such as mortality, discharge destination, need for tracheostomy/percutaneous endoscopic gastrostomy placement, and length of stay (LOS), were assessed. Univariate and multivariate analyses were performed to assess the impact of clinical seizures, SE, and epileptiform EEG on outcomes. Of 76 patients with aSDH who underwent EEG monitoring, 74 (97.4%) received antiseizure prophylaxis. Thirty-two (41.1%) patients had seizures, most of which were clinical seizures. Twenty-four (32%) patients had epileptiform EEG findings. Clinical or nonconvulsive SE was diagnosed in 12 (16%) patients. Clinical seizures were not associated with outcome parameters. Epileptiform EEG findings were independently associated with longer hospital LOS (13 vs. 8 days, P = 0.04) and intensive care unit LOS (10 vs. 4 days, P = 0.002). The SE also predicted longer intensive care unit LOS (10 vs. 4 days, P = 0.002). Neither epileptiform EEG nor SE was significantly related to mortality, discharge destination, or need for tracheostomy/percutaneous endoscopic gastrostomy placement. Seizures and epileptiform EEG findings are very common in patients with aSDH despite antiseizure prophylaxis. While clinical seizures did not affect outcomes, the presence of epileptiform EEG findings and SE was independently associated with longer intensive care unit LOS and hospital LOS.

Electrographic Seizures after Convulsive Status Epilepticus in Children and Young Adults: A Retrospective Multicenter Study

To describe the prevalence, characteristics, and predictors of electrographic seizures after convulsive status epilepticus (CSE). This was a multicenter retrospective study in which we describe clinical and electroencephalographic (EEG) features of children (1 month to 21 years) with CSE who underwent continuous EEG monitoring. Ninety-eight children (53 males) with CSE (median age of 5 years) underwent subsequent continuous EEG monitoring after CSE. Electrographic seizures (with or without clinical correlate) were identified in 32 subjects (33%). Eleven subjects (34.4%) had electrographic-only seizures, 17 subjects (53.1%) had electroclinical seizures, and 4 subjects (12.5%) had an unknown clinical correlate. Of the 32 subjects with electrographic seizures, 15 subjects (46.9%) had electrographic status epilepticus. Factors associated with the occurrence of electrographic seizures after CSE were a previous diagnosis of epilepsy (P = .029) and the presence of interictal epileptiform discharges (P < .0005). The median (p25-p75) duration of stay in the pediatric intensive care unit was longer for children with electrographic seizures than for children without electrographic seizures (9.5 [3-22.5] vs 2 [2-5] days, Wilcoxon test, Z = 3.916, P = .0001). Four children (4.1%) died before leaving the hospital, and we could not identify a relationship between death and the presence or absence of electrographic seizures. After CSE, one-third of children who underwent EEG monitoring experienced electrographic seizures, and among these, one-third experienced entirely electrographic-only seizures. A previous diagnosis of epilepsy and the presence of interictal epileptiform discharges were risk factors for electrographic seizures.