Electroencephalography (EEG) is widely used to assess neurological prognosis in patients who are comatose after cardiac arrest, but its value is limited by varying definitions of pathological patterns and by inter-rater variability. The American Clinical Neurophysiology Society (ACNS) has recently proposed a standardized EEG-terminology for critical care to address these limitations.
In the Target Temperature Management (TTM) trial, a large international trial on temperature management after cardiac arrest, EEG-examinations were part of the prospective study design. The main objective of this study is to evaluate EEG-data from the TTM-trial and to identify malignant EEG-patterns reliably predicting a poor neurological outcome.
In the TTM-trial, 399 post cardiac arrest patients who remained comatose after rewarming underwent a routine EEG. The presence of clinical seizures, use of sedatives and antiepileptic drugs during the EEG-registration were prospectively documented.
After the end of the trial, the EEGs were retrieved to form a central EEG-database.
The EEG-data will be analysed using the ACNS EEG terminology. We designed an electronic case record form (eCRF). Four EEG-specialists from different countries, blinded to patient outcome, will independently classify the EEGs and report through the eCRF. We will describe the prognostic values of pre-specified EEG patterns to predict poor as well as good outcome. We hypothesise three patterns to always be associated with a poor outcome (suppressed background without discharges, suppressed background with continuous periodic discharges and burst-suppression). Inter- and intra-rater variability and whether sedation or level of temperature affects the prognostic values will also be analyzed.
A well-defined terminology for interpreting post cardiac arrest EEGs is critical for the use of EEG as a prognostic tool.
The results of this study may help to validate the ACNS terminology for assessing post cardiac arrest EEGs and identify patterns that could reliably predict outcome.
The TTM-trial is registered at ClinicalTrials.gov (NCT01020916).
EEG; Prognosis; Interrater variability; Brain injury; Out-of-hospital cardiac arrest; Hypothermia; Resuscitation
Hypoxic ischemic brain injury secondary to pediatric cardiac arrest (CA) may result in acute symptomatic seizures. A high proportion of seizures may be nonconvulsive, so accurate diagnosis requires continuous EEG monitoring. We aimed to determine the safety and feasibility of long-term EEG monitoring, to describe electroencephalographic background and seizure characteristics, and to identify background features predictive of seizures in children undergoing therapeutic hypothermia (TH) after CA.
Nineteen children underwent TH after CA. Continuous EEG monitoring was performed during hypothermia (24 hours), rewarming (12–24 hours), and then an additional 24 hours of normothermia. The tolerability of these prolonged studies and the EEG background classification and seizure characteristics were described in a standardized manner.
No complications of EEG monitoring were reported or observed. Electrographic seizures occurred in 47% (9/19), and 32% (6/19) developed status epilepticus. Seizures were nonconvulsive in 67% (6/9) and electrographically generalized in 78% (7/9). Seizures commenced during the late hypothermic or rewarming periods (8/9). Factors predictive of electrographic seizures were burst suppression or excessively discontinuous EEG background patterns, interictal epileptiform discharges, or an absence of the expected pharmacologically induced beta activity. Background features evolved over time. Patients with slowing and attenuation tended to improve, whereas those with burst suppression tended to worsen.
EEG monitoring in children undergoing therapeutic hypothermia after cardiac arrest is safe and feasible. Electrographic seizures and status epilepticus are common in this setting but are often not detectable by clinical observation alone. The EEG background often evolves over time, with milder abnormalities improving and more severe abnormalities worsening.
= burst suppression;
= cardiac arrest;
= cardiopulmonary resuscitation;
= developmental delay;
= hypoxic ischemic encephalopathy;
= nonconvulsive seizures;
= nonconvulsive status epilepticus;
= negative predictive value;
= periodic epileptiform discharge;
= pediatric intensive care unit;
= positive predictive value;
= status epilepticus;
= sudden infant death syndrome;
= therapeutic hypothermia;
= valproic acid;
= ventricular tachycardia.
To quantify the ictal subdural electroencephalogram (EEG) changes using spectral analysis, and to delineate the quantitatively defined ictal onset zones on high-resolution 3D MR images in children with intractable neocortical epilepsy.
Fourteen children with intractable neocortical epilepsy (age: 1–16 years) who had subsequent resective surgery were retrospectively studied. The subjects underwent a high-resolution MRI and prolonged subdural EEG recording. Spectral analysis was applied to 3 habitual focal seizures. After fast Fourier transformation of the EEG epoch at ictal onset, an amplitude spectral curve (square root of the power spectral curve) was created for each electrode. The EEG magnitude of ictal rhythmic discharges was defined as the area under the amplitude spectral curve within a preset frequency band including the ictal discharge frequency, and calculated for each electrode. The topography mapping of ictal EEG magnitude was subsequently displayed on a surface-rendered MRI. Finally, receiver operating characteristic (ROC) analysis was performed to evaluate the consistency between quantitatively and visually defined ictal onset zones.
The electrode showing the maximum of the averaged ictal EEG magnitude was part of the visually defined ictal onset zone in all cases. ROC analyses demonstrated that electrodes showing >30% of the maximum of the averaged ictal EEG magnitude had a specificity of 0.90 and a sensitivity of 0.74 for the concordance with visually defined ictal onset zones.
Quantitative ictal subdural EEG analysis using spectral analysis may supplement conventional visual inspection in children with neocortical epilepsy by providing an objective definition of the onset zone and its simple visualization on the patient’s MRI.
Clinical neurophysiology; Pediatric epilepsy surgery; Quantitative ictal intracranial electroencephalography; Focal cortical dysplasia; Tuberous sclerosis complex
Continuous EEG (cEEG) is increasingly used to monitor brain function in neuro-ICU patients. However, its value in patients with coma after cardiac arrest (CA), particularly in the setting of therapeutic hypothermia (TH), is only beginning to be elucidated. The aim of this study was to examine whether cEEG performed during TH may predict outcome.
From April 2009 to April 2010, we prospectively studied 34 consecutive comatose patients treated with TH after CA who were monitored with cEEG, initiated during hypothermia and maintained after rewarming. EEG background reactivity to painful stimulation was tested. We analyzed the association between cEEG findings and neurologic outcome, assessed at 2 months with the Glasgow-Pittsburgh Cerebral Performance Categories (CPC).
Continuous EEG recording was started 12 ± 6 hours after CA and lasted 30 ± 11 hours. Nonreactive cEEG background (12 of 15 (75%) among nonsurvivors versus none of 19 (0) survivors; P < 0.001) and prolonged discontinuous "burst-suppression" activity (11 of 15 (73%) versus none of 19; P < 0.001) were significantly associated with mortality. EEG seizures with absent background reactivity also differed significantly (seven of 15 (47%) versus none of 12 (0); P = 0.001). In patients with nonreactive background or seizures/epileptiform discharges on cEEG, no improvement was seen after TH. Nonreactive cEEG background during TH had a positive predictive value of 100% (95% confidence interval (CI), 74 to 100%) and a false-positive rate of 0 (95% CI, 0 to 18%) for mortality. All survivors had cEEG background reactivity, and the majority of them (14 (74%) of 19) had a favorable outcome (CPC 1 or 2).
Continuous EEG monitoring showing a nonreactive or discontinuous background during TH is strongly associated with unfavorable outcome in patients with coma after CA. These data warrant larger studies to confirm the value of continuous EEG monitoring in predicting prognosis after CA and TH.
To determine whether the absence of early epileptiform abnormalities predicts absence of later seizures on continuous EEG monitoring of hospitalized patients.
We retrospectively reviewed 242 consecutive patients without a prior generalized convulsive seizure or active epilepsy who underwent continuous EEG monitoring lasting at least 18 hours for detection of nonconvulsive seizures or evaluation of unexplained altered mental status. The findings on the initial 30-minute screening EEG, subsequent continuous EEG recordings, and baseline clinical data were analyzed. We identified early EEG findings associated with absence of seizures on subsequent continuous EEG.
Seizures were detected in 70 (29%) patients. A total of 52 patients had their first seizure in the initial 30 minutes of continuous EEG monitoring. Of the remaining 190 patients, 63 had epileptiform discharges on their initial EEG, 24 had triphasic waves, while 103 had no epileptiform abnormalities. Seizures were later detected in 22% (n = 14) of studies with epileptiform discharges on their initial EEG, vs 3% (n = 3) of the studies without epileptiform abnormalities on initial EEG (p < 0.001). In the 3 patients without epileptiform abnormalities on initial EEG but with subsequent seizures, the first epileptiform discharge or electrographic seizure occurred within the first 4 hours of recording.
In patients without epileptiform abnormalities during the first 4 hours of recording, no seizures were subsequently detected. Therefore, EEG features early in the recording may indicate a low risk for seizures, and help determine whether extended monitoring is necessary.
To demonstrate the significance of simultaneous electroencephalography (EEG) recording during 2-deoxy-2-[18F] fluoro-D-glucose (FDG)-positron emission tomography (PET) in childhood partial epilepsy.
Materials and Methods
We included 46 children with partial epilepsy who underwent simultaneous EEG during PET. We compared the epileptogenic area of several EEG features including epileptiform discharges, focal polymorphic slow waves, and electrographic seizures, with the abnormal metabolic region on PET. We also compared the epileptogenic area of simultaneous EEG and PET with findings on magnetic resonance imaging (MRI) and video/EEG, as well as the histopathological diagnosis of the resected cortical area, in eight patients who underwent surgical resection of the epileptogenic area.
Hypometabolic regions on interictal PET were concordant with epileptogenic areas of epileptiform discharges and focal polymorphic slow waves, according to their frequency and/or severity, with odds ratios of 1.35 and 1.81, respectively (p<0.05). Hypermetabolic PET was also concordant with epileptogenic areas of ictal events longer than 20 seconds during the period of FDG uptake. Among the eight patients who underwent surgical resection, six patients, including two with non-lesional MRI, had concordant EEG and PET findings, were confirmed pathologically, and became seizure-free after surgery.
Simultaneous EEG is useful in identifying epileptogenic areas due to a high concordance with abnormal PET metabolic areas. Moreover, simultaneous EEG may also prevent false lateralization of PET from postictal and mixed metabolism during ictal events, as well as abnormal hypermetabolism, during frequent interictal epileptiform discharges.
Positron-emission tomography; electroencephalography; epilepsy
Quantitatively evaluate whether screening with compressed spectral arrays (CSAs) is a practical and time-effective protocol for assisting expert review of continuous EEG (cEEG) studies in hospitalized adults.
Three neurophysiologists reviewed the reported findings of the first 30 minutes of 118 cEEGs, then used CSA to guide subsequent review (“CSA-guided review” protocol). Reviewers viewed 120 seconds of raw EEG data surrounding suspicious CSA segments. The same neurophysiologists performed independent page-by-page visual interpretation (“conventional review”) of all cEEGs. Independent conventional review by 2 additional, more experienced neurophysiologists served as a gold standard. We compared review times and detection rates for seizures and other pathologic patterns relative to conventional review.
A total of 2,092 hours of cEEG data were reviewed. Average times to review 24 hours of cEEG data were 8 (±4) minutes for CSA-guided review vs 38 (±17) minutes for conventional review (p < 0.005). Studies containing seizures required longer review: 10 (±4) minutes for CSA-guided review vs 44 (±20) minutes for conventional review (p < 0.005). CSA-guided review was sensitive for seizures (87.3%), periodic epileptiform discharges (100%), rhythmic delta activity (97.1%), focal slowing (98.7%), generalized slowing (100%), and epileptiform discharges (88.5%).
CSA-guided review reduces cEEG review time by 78% with minimal loss of sensitivity compared with conventional review.
Classification of evidence:
This study provides Class IV evidence that screening of cEEG with CSAs efficiently and accurately identifies seizures and other EEG abnormalities as compared with standard cEEG visual interpretation.
We evaluated the validity and inter-rater reliability of encephalographer interpretation of color density spectral array (CDSA) EEG for seizure identification in critically ill children and explored predictors of accurate seizure identification.
Conventional EEG tracings from 21 consecutive critically ill children were scored for electrographic seizures. Four two-hour long segments from each patient were converted to 8 channel CDSA displays, yielding 84 images. Eight encephalographers received CDSA training and circled elements thought to represent seizures. Images were reviewed in random order (Group A) or with information regarding seizure presence in the initial 30 minutes and with patient images in order (Group B). Sensitivity, specificity, and inter-rater reliability were calculated. Factors associated with CDSA seizure identification were assessed.
Seizure prevalence was 43% on conventional EEG. Specificity was significantly higher for Group A (92.3% versus 78.2%, p<0.00). Sensitivity was not significantly different between Groups A and B (64.8% versus 75%, p=0.22). Inter-rater reliability was moderate in both groups. Ten percent of images were falsely classified as containing a seizure. Seizure duration ≥2 minutes predicted identification (p<0.001).
CDSA may be a useful screening tool for seizure identification by encephalographers, but it does not identify all seizures and false positives occur.
Critical Care; EEG; Pediatric; Seizure; EEG monitoring
Four to ten percent of patients evaluated in emergency departments (ED) present with altered mental status (AMS). The prevalence of non-convulsive seizure (NCS) and other electroencephalographic (EEG) abnormalities in this population is unknown
To identify the prevalence of NCS and other EEG abnormalities in ED patients with AMS.
A prospective observational study at two urban ED. Inclusion: patients ≥13 years old with AMS. Exclusion: An easily correctable cause of AMS (e.g. hypoglycemia). A 30-minute standard 21-electrode EEG was performed on each subject upon presentation. Outcome: prevalence of EEG abnormalities interpreted by a board-certified epileptologist. EEGs were later reviewed by two blinded epileptologists. Inter-rater agreement (IRA) of the blinded EEG interpretations is summarized with kappa. A multiple logistic regression model was constructed to identify variables that could predict the outcome.
259 patients were enrolled (median age: 60, 54% female). Overall, 202/259 of EEGs were interpreted as abnormal (78%, 95% confidence interval [CI], 73–83%). The most common abnormality was background slowing (58%, 95%CI, 52–68%) indicating underlying encephalopathy. NCS (including non-convulsive status epilepticus [NCSE]) was detected in 5% (95%CI, 3–8%) of patients. The regression analysis predicting EEG abnormality showed a highly significant effect of age (p<0.001, adjusted odds ratio 1.66 [95%CI, 1.36–2.02] per 10-year age increment). IRA for EEG interpretations was modest (kappa: 0.45, 95% CI, 0.36–0.54).
The prevalence of EEG abnormalities in ED patients with undifferentiated AMS is significant. ED physicians should consider EEG in the evaluation of patients with AMS and a high suspicion of NCS/NCSE.
To evaluate the diagnostic accuracy of 2 quantitative EEG display tools, color density spectral array (CDSA) and amplitude-integrated EEG (aEEG), for seizure identification in the intensive care unit (ICU).
A set of 27 continuous EEG recordings performed in pediatric ICU patients was transformed into 8-channel CDSA and aEEG displays. Three neurophysiologists underwent 2 hours of training to identify seizures using these techniques. They were then individually presented with a series of CDSA and aEEG displays, blinded to the raw EEG, and asked to mark any events suspected to be seizures. Their performance was compared to seizures identified on the underlying conventional EEG.
The 27 EEG recordings contained 553 discrete seizures over 487 hours. The median sensitivity for seizure identification across all recordings was 83.3% using CDSA and 81.5% using aEEG. However, among individual recordings, the sensitivity ranged from 0% to 100%. Factors reducing the sensitivity included low-amplitude, short, and focal seizures. False-positive rates were generally very low, with misidentified seizures occurring once every 17–20 hours.
Both CDSA and aEEG demonstrate acceptable sensitivity and false-positive rates for seizure identification among critically ill children. Accuracy of these tools would likely improve during clinical use, when findings can be correlated in real-time with the underlying raw EEG. In the hands of neurophysiologists, CDSA and aEEG displays represent useful screening tools for seizures during continuous EEG monitoring in the ICU. The suitability of these tools for bedside use by ICU nurses and physicians requires further study.
= amplitude-integrated EEG;
= color density spectral array;
= fast-Fourier transformation;
= intensive care unit.
Background and Purpose:
This study aimed to determine whether preoperative or postoperative electroencephalography (EEG) can predict surgical outcome for corpus callosotomy.
We retrospectively reviewed the medical records of 16 patients enrolled. We compared postoperative seizure outcome according to seizure type, preoperative interictal EEG, preoperative ictal EEG, and postoperative interictal EEG. Seizure outcome was classified according to postoperative seizure reduction, i.e., seizure free, >90%, 50–90%, <50%, and no change or worsened. A seizure reduction of 50% or more was judged as a “favorable outcome”.
Most patients showed a favorable outcome (12 patients, 75%) and two patients became seizure free (13%). Atonic seizure was most responsive to corpus callosotomy. Preoperative interictal epileptiform discharge had 3 patterns; bilateral independent, generalized, and combination of independent and generalized. None of the preoperative interictal epileptiform discharge (EDs) had significant correlation with seizure outcome. The preoperative ictal rhythm did not predict seizure outcome. However disappearance of generalized EDs on postoperative EEG was correlated with favorable seizure outcome.
The presence of generalized EDs on postoperative interictal EEG predicted seizure outcome, whereas preoperative EEG did not.
Corpus callosum/surgery; Electroencephalography; Epilepsy/surgery; Child
The role of sharps and spikes, interictal epileptiform discharges (IEDs), in guiding epilepsy surgery in children remains controversial, particularly with intracranial EEG (IEEG). While ictal recording is the mainstay of localizing epileptic networks for surgical resection, current practice dictates removing regions generating frequent IEDs if they are near the ictal onset zone. Indeed, past studies suggest an inconsistent relationship between IED and seizure onset location, though these studies were based upon relatively short EEG epochs.
We employ a previously validated, computerized spike detector, to measure and localize IED activity over prolonged, representative segments of IEEG recorded from 19 children with intractable, mostly extra temporal lobe epilepsy. Approximately 8 hours of IEEG, randomly selected thirty-minute segments of continuous interictal IEEG per patient were analyzed over all intracranial electrode contacts.
When spike frequency was averaged over the 16-time segments, electrodes with the highest mean spike frequency were found to be within the seizure onset region in 11 of 19 patients. There was significant variability between individual 30-minute segments in these patients, indicating that large statistical samples of interictal activity were required for improved localization. Low voltage fast EEG at seizure onset was the only clinical factor predicting IED localization to the seizure onset region.
Our data suggest that automated IED detection over multiple representative samples of IEEG may be of utility in planning epilepsy surgery for children with intractable epilepsy. Further research is required to better determine which patients may benefit from this technique a priori.
Spike density; intracranial EEG; Seizure onset; Pediatric Epilepsy
Simultaneous electroencephalogram (EEG)-functional Magnetic Resonance Imaging (fMRI) recordings have seen growing application in the evaluation of epilepsy, namely in the characterization of brain networks related to epileptic activity. In EEG-correlated fMRI studies, epileptic events are usually described as boxcar signals based on the timing information retrieved from the EEG, and subsequently convolved with a hemodynamic response function to model the associated Blood Oxygen Level Dependent (BOLD) changes. Although more flexible approaches may allow a higher degree of complexity for the hemodynamics, the issue of how to model these dynamics based on the EEG remains an open question. In this work, a new methodology for the integration of simultaneous EEG-fMRI data in epilepsy is proposed, which incorporates a transfer function from the EEG to the BOLD signal. Independent component analysis of the EEG is performed, and a number of metrics expressing different models of the EEG-BOLD transfer function are extracted from the resulting time courses. These metrics are then used to predict the fMRI data and to identify brain areas associated with the EEG epileptic activity. The methodology was tested on both ictal and interictal EEG-fMRI recordings from one patient with a hypothalamic hamartoma. When compared to the conventional analysis approach, plausible, consistent, and more significant activations were obtained. Importantly, frequency-weighted EEG metrics yielded superior results than those weighted solely on the EEG power, which comes in agreement with previous literature. Reproducibility, specificity, and sensitivity should be addressed in an extended group of patients in order to further validate the proposed methodology and generalize the presented proof of concept.
BOLD; EEG-fMRI; epilepsy; ICA; heuristic
Retrospective studies have reported the occurrence of nonconvulsive seizures in critically ill children. We aimed to prospectively determine the incidence and risk factors of nonconvulsive seizures in critically ill children using predetermined EEG monitoring indications and EEG interpretation terminology.
Critically ill children (non-neonates) with acute encephalopathy underwent continuous EEG monitoring if they met institutional clinical practice criteria. Study enrollment and data collection were prospective. Logistic regression analysis was utilized to identify risk factors for seizure occurrence.
One hundred children were evaluated. Electrographic seizures occurred in 46 and electrographic status epilepticus occurred in 19. Seizures were exclusively nonconvulsive in 32. The only clinical risk factor for seizure occurrence was younger age (p = 0.03). Of patients with seizures, only 52% had seizures detected in the first hour of monitoring, while 87% were detected within 24 hours.
Seizures were common in critically ill children with acute encephalopathy. Most were nonconvulsive. Clinical features had little predictive value for seizure occurrence. Further study is needed to confirm these data in independent high-risk populations, to clarify which children are at highest risk for seizures so limited monitoring resources can be allocated optimally, and to determine whether seizure detection and management improves outcome.
Electroencephalography (EEG) occupies an important place for studying human brain activity in general, and epileptic processes in particular, with appropriate time resolution. Scalp EEG or intracerebral EEG signals recorded in patients with drug-resistant partial epilepsy convey important information about epileptogenic networks that must be localized and understood prior to subsequent therapeutic procedures. However, this information, often subtle, is ‘hidden’ in the signals. It is precisely the role of signal processing to extract this information and to put it into a ‘coherent and interpretable picture’ that can participate in the therapeutic strategy. Nowadays, the panel of available methods is very wide depending on the objectives such as, for instance, the detection of transient epileptiform events, the detection and/or prediction of seizures, the recognition and/or the classification of EEG patterns, the localization of epileptic neuronal sources, the characterization of neural synchrony, the determination of functional connectivity, among others. The intent of this paper is to focus on a specific category of methods providing relevant information about epileptogenic networks from the analysis of spatial properties of EEG signals in the time and frequency domain. These methods apply to either interictal or ictal recordings and share the common objective of localizing the subsets of brain structures involved in both types of paroxysmal activity. Most of these methods were developed by our group and are routinely used during pre-surgical evaluation. Examples are detailed. Results, as well as limitations of the methods, are also discussed.
electroencephalography; intracerebral; epilepsy; interictal; ictal; statistical signal processing
Electroencephalography (EEG) occupies an important place for studying human brain activity in general, and epileptic processes in particular, with appropriate time resolution. Scalp-EEG or intracerebral-EEG signals recorded in patients with drug-resistant partial epilepsy convey important information about epileptogenic networks that must be localized and understood prior to subsequent therapeutic procedure. However, this information, often subtle, is “hidden” into the signals. It is precisely the role of signal processing to extract this information and to put it into a “coherent and interpretable picture” that can participate into the therapeutic strategy. Nowadays, the panel of available methods is very wide depending on the objectives like, for instance, the detection of transient epileptiform events, the detection and/or prediction of seizures, the recognition and/or the classification of EEG patterns, the localization of epileptic neuronal sources, the characterization of neural synchrony, the determination of functional connectivity, among others. The intent of this paper is to focus on a specific category of methods providing relevant information about epileptogenic networks from the analysis of spatial properties of EEG signals in the time and frequency domain. These methods apply either to interictal or to ictal recordings and share the common objective of localizing the subsets of brain structures involved in both types of paroxysmal activity. Most of these methods were developed by our group and are routinely used during pre-surgical evaluation. Examples are detailed. Results, as well as limitations of the methods, are also discussed.
Algorithms; Amygdala; physiopathology; Automatic Data Processing; Brain; physiopathology; Electroencephalography; statistics & numerical data; Epilepsies, Partial; physiopathology; Hippocampus; physiopathology; Humans; Models, Anatomic; Nerve Net; Regression Analysis; Signal Processing, Computer-Assisted; Electroencephalography; intracerebral; epilepsy; interictal; ictal; statistical signal processing; spike detection; bivariate analysis; time-frequency analysis
Electroencephalography (EEG) is frequently ordered for patients with febrile seizures despite its unclear diagnostic value. We evaluated the prevalence of abnormal EEGs, the association between clinical findings and abnormal EEGs, and the predictive value of EEG for the recurrence of febrile seizures.
Data were collected on 230 children who were treated for febrile seizures at Kyung Hee University Medical Center from 2005 to 2009. EEGs were recorded after 1-2 days of hospitalization when children became afebrile. EEG patterns were categorized as normal, epileptiform, or nonspecific relative to abnormalities. The patients' medical records were reviewed, and telephone interviews with the families of the children were conducted to inquire about seizure recurrence. The relationships between clinical variables, including seizure recurrence, and EEG abnormalities were evaluated.
Of the 131 children included, 103 had simple and 28 had complex febrile seizures. EEG abnormalities were found in 41 children (31%). EEG abnormalities were more common in children with complex than simple febrile seizures (43% vs. 28%), but the difference was not statistically significant. Logistical regression analysis showed that having multiple seizures in a 24-hour period was significantly predictive of abnormal EEG (odds ratio, 2.98; 95% confidence interval, 1.0 to 88; P=0.048). The frequency of recurrence did not differ significantly in the normal (31%) and abnormal (23%) EEG groups.
Multiple seizures within 24 hours were predictive of abnormal EEG in children with febrile seizures. Abnormal EEG was not predictive of febrile seizure recurrence.
Febrile seizures; Postictal; Electroencephalography
Visual scoring of murine EEG signals is time-consuming and subject to low inter-observer reproducibility. The Racine scale for behavioral seizure severity does not provide information about interictal or sub-clinical epileptiform activity. An automated algorithm for murine EEG analysis was developed using total signal variation and wavelet decomposition to identify spike, seizure, and other abnormal signal types in single-channel EEG collected from kainic acid-treated mice. The algorithm was validated on multi-channel EEG collected from γ-butyrolacetone-treated mice experiencing absence seizures. The algorithm identified epileptiform activity with high fidelity compared to visual scoring, correctly classifying spikes and seizures with 99% accuracy and 91% precision. The algorithm correctly identifed a spike-wave discharge focus in an absence-type seizure recorded by 36 cortical electrodes. The algorithm provides a reliable and automated method for quantification of multiple classes of epileptiform activity within the murine EEG and is tunable to a variety of event types and seizure categories.
Continuous EEG (cEEG) monitoring is being used with increasing frequency in critically ill patients, most often to detect non-convulsive seizures. While cEEG is non-invasive and feasible in the critical care setting, it is also expensive and labor intensive, and there has been little study of its impact on clinical care. We aimed to determine prospectively the impact of cEEG on clinical management in critically ill children.
Critically ill children (non-neonates) with acute encephalopathy underwent cEEG. Study enrollment and data collection were prospective.
100 children were studied. EEG monitoring led to specific clinical management changes in 59 children. These included initiating or escalating anti-seizure medications in 43 due to seizure detection, demonstrating that a specific event (subtle movement or vital sign change) was not a seizure in 21, or obtaining urgent neuroimaging that led to a clinical change in 3. In the remaining 41 children, cEEG ruled out the presence of non-convulsive seizures but did not lead to a specific change in clinical management.
EEG monitoring led to changes in clinical management in the majority of patients, suggesting it may have an important role in management of critically ill children. Further study is needed to determine whether the management changes elicited by cEEG improve outcome.
Seizure; Status epilepticus; Pediatric; Critically Ill; Electroencephalogram; EEG monitoring
The intrarater and interrater reliability (I&IR) of EEG interpretation has significant implications for the value of EEG as a diagnostic tool. We measured both I&IR of EEG interpretation based on interpretation of complete EEGs into standard diagnostic categories and rater confidence in their interpretations, and investigated sources of variance in EEG interpretations. During two distinct time intervals six board-certified clinical neurophysiologists classified 300 EEGs into one or more of seven diagnostic categories, and assigned a subjective confidence to their interpretations. Each EEG was read by three readers. Each reader interpreted 150 unique studies, and 50 studies twice to generate intrarater data. A generalizability study assessed the contribution of subjects, readers, and the interaction between subjects and readers to interpretation variance. Five of the six readers had a median confidence of ≥ 99%, and the upper quartile of confidence values was 100% for all six readers. Intrarater Cohen’s kappa (κc) ranged from 0.33 to 0.73 with an aggregated value of 0.59. κc ranged from 0.29 to 0.62 for the 15 reader pairs, with an aggregated Fleiss kappa of 0.44 for interrater agreement. The κc were not significantly different across rater pairs (Chi-Square = 17.3, df=14, p = 0.24). Variance due to subjects (i.e. EEGs) was 65.3%, to readers was 3.9%, and to the interaction between readers and subjects was 30.8%. Experienced epileptologists have very high confidence in their EEG interpretations and low to moderate I&IR, a common paradox in clinical medicine. A necessary but insufficient condition to improve EEG interpretation accuracy is to increase intrarater and interrater reliability. This goal could be accomplished, for instance, with an automated on-line application integrated into a continuing medical education module that measures and reports EEG I&IR to individual users.
interrater reliability; intrarater reliability; confidence; EEG
The electroencephalography (EEG) signal has a high complexity, and the process of extracting clinically relevant features is achieved by visual analysis of the recordings. The interobserver agreement in EEG interpretation is only moderate. This is partly due to the method of reporting the findings in free-text format. The purpose of our endeavor was to create a computer-based system for EEG assessment and reporting, where the physicians would construct the reports by choosing from predefined elements for each relevant EEG feature, as well as the clinical phenomena (for video-EEG recordings). A working group of EEG experts took part in consensus workshops in Dianalund, Denmark, in 2010 and 2011. The faculty was approved by the Commission on European Affairs of the International League Against Epilepsy (ILAE). The working group produced a consensus proposal that went through a pan-European review process, organized by the European Chapter of the International Federation of Clinical Neurophysiology. The Standardised Computer-based Organised Reporting of EEG (SCORE) software was constructed based on the terms and features of the consensus statement and it was tested in the clinical practice. The main elements of SCORE are the following: personal data of the patient, referral data, recording conditions, modulators, background activity, drowsiness and sleep, interictal findings, “episodes” (clinical or subclinical events), physiologic patterns, patterns of uncertain significance, artifacts, polygraphic channels, and diagnostic significance. The following specific aspects of the neonatal EEGs are scored: alertness, temporal organization, and spatial organization. For each EEG finding, relevant features are scored using predefined terms. Definitions are provided for all EEG terms and features. SCORE can potentially improve the quality of EEG assessment and reporting; it will help incorporate the results of computer-assisted analysis into the report, it will make possible the build-up of a multinational database, and it will help in training young neurophysiologists.
Assessment; Database; Definitions; EEG; Semiology; Terms
To demonstrate the feasibility of using a computerized system to detect the onset of a seizure and, in response, initiate Vagus nerve stimulation (VNS) in patients with medically refractory epilepsy.
We designed and built a non-invasive, computerized system that automatically initiates VNS following the real-time detection of a pre-identified seizure or epileptiform discharge. The system detects these events through patient-specific analysis of the scalp electroencephalogram (EEG) and electrocardiogram (ECG) signals.
We evaluated the performance of the system on 5 patients (A–E). For patients A and B the computerized system initiated VNS in response to seizures; for patients C and D the system initiated VNS in response to epileptiform discharges; and for patient E neither seizures nor epileptiform discharges were observed during the evaluation period. During the 81 hour clinical test of the system on patient A, the computerized system detected 5/5 seizures and initiated VNS within 5 seconds of the appearance of ictal discharges in the EEG; VNS did not seem to alter the electrographic or behavioral characteristics of the seizures in this case. During the same testing session the computerized system initiated false stimulations at the rate of 1 false stimulus every 2.5 hours while the subject was at rest and not ambulating. During the 26 hour clinical test of the system on patient B, the computerized system detected 1/1 seizures and initiated VNS within 16 seconds of the appearance of ictal discharges; VNS did not alter the electrographic duration of the seizure but decreased anxiety and increased awareness during the post-seizure recovery phase. During the same testing session the computerized system did not declare any false detections.
Initiating Vagus nerve stimulation soon after the onset of a seizure may abort or ameliorate seizure symptoms in some patients; unfortunately, a significant number of patients cannot initiate VNS by themselves following the start of a seizure. A system that automatically couples automated detection of seizure onset to initiation of VNS may be helpful for seizure treatment.
Seizure detection; Vagus nerve stimulation
Background: The cerebral function monitor (CFM) is widely used to detect neonatal seizures, but there are very few studies comparing it with simultaneous electroencephalography (EEG).
Objective: To determine the accuracy of non-expert use of the CFM and to assess interobserver agreement of CFM seizure detection.
Patients: Babies admitted to the neonatal intensive care unit at King's College Hospital who were at high risk of seizure and had video-EEG monitoring.
Methods: Video-EEG was used to detect seizures. Each baby had CFM recordings at speeds of 6, 15, and 30 cm/h during the EEG. Four neonatologists, trained in CFM seizure recognition, independently rated one hour CFM samples at three speeds from each baby. Interobserver agreement was quantified using Cohen's κ.
Results: CFM traces from 19 babies with EEG seizures and 21 babies without EEG seizures were analysed. Overall non-expert interpretation of the CFM performed poorly as a seizure detector compared with simultaneous EEG (sensitivities 38% at 6 cm/h; 54% at 15 cm/h; 55% at 30 cm/h). Although babies with seizures were more likely to be correctly classified at higher speeds (p = 0.02), babies without seizures were also more likely to be misclassified (p < 0.001). Agreement between observers was not good at any speed (κ values from 0.01 to 0.39). The observers usually detected generalised seizures but often missed seizures that were focal, low amplitude, or lasted less than one minute.
Conclusion: Approximately half of all neonatal seizures may be missed using CFM alone. Neonatal seizures need to be diagnosed, characterised, and quantified first using EEG. The CFM may then be useful for long term monitoring.
We aimed to determine the incidence of electrographic seizures in children in the pediatric intensive care unit who underwent EEG monitoring, risk factors for electrographic seizures, and whether electrographic seizures were associated with increased odds of mortality.
Eleven sites in North America retrospectively reviewed a total of 550 consecutive children in pediatric intensive care units who underwent EEG monitoring. We collected data on demographics, diagnoses, clinical seizures, mental status at EEG onset, EEG background, interictal epileptiform discharges, electrographic seizures, intensive care unit length of stay, and in-hospital mortality.
Electrographic seizures occurred in 162 of 550 subjects (30%), of which 61 subjects (38%) had electrographic status epilepticus. Electrographic seizures were exclusively subclinical in 59 of 162 subjects (36%). A multivariable logistic regression model showed that independent risk factors for electrographic seizures included younger age, clinical seizures prior to EEG monitoring, an abnormal initial EEG background, interictal epileptiform discharges, and a diagnosis of epilepsy. Subjects with electrographic status epilepticus had greater odds of in-hospital death, even after adjusting for EEG background and neurologic diagnosis category.
Electrographic seizures are common among children in the pediatric intensive care unit, particularly those with specific risk factors. Electrographic status epilepticus occurs in more than one-third of children with electrographic seizures and is associated with higher in-hospital mortality.
To assess the diagnostic value of amplitude-integrated electroencephalography (EEG) in comparison to standard EEG in newborns without severe hypoxic-ischemic encephalopathy who were at risk for seizures.
The study included a consecutive series of 18 term newborns without severe hypoxic-ischemic encephalopathy, but with clinical signs suspicious of epileptic seizures, history of loss of social contact, disturbance of muscle tone, hyperirritability, and/or jitteriness. Amplitude-integrated and standard EEG tracings were assessed for background pattern, epileptiform activity, and sleep-wake cycling.
Amplitude-integrated EEG and standard EEG recordings of 15 newborns were suitable for analysis. Only two different background patterns were seen on amplitude-integrated EEG and standard EEG, with the absence of severely abnormal background patterns. Of 15 newborns, epileptiform discharges were present on amplitude-integrated EEG in 3 newborns, and on standard EEG in 6 newborns. Sensitivity of seizures discharges on amplitude-integrated EEG to correspond with epileptiform discharges on standard EEG was 50%; specificity 100%,positive predictive value 100%, and negative predictive value 75%. Of 4 newborns suspected of having sleep myoclonus, amplitude-integrated EEG correctly identified the newborn who had epileptiform activity on standard EEG.
The diagnostic value of amplitude-integrated EEG monitoring of term newborns without severe hypoxic-ischemic encephalopathy is limited, but could have a role in evaluating presence or absence of epileptiform activity and in differentiating non-epileptic movement from seizures.