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 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
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 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
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.
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.
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
Electroencephalographic (EEG) features may provide objective data regarding prognosis in children resuscitated from cardiac arrest (CA), but therapeutic hypothermia (TH) may impact its predictive value. We aimed to determine whether specific EEG features were predictive of short-term outcome in children treated with TH after CA, both during hypothermia and after return to normothermia.
Thirty-five children managed with a standard clinical TH algorithm after CA were prospectively enrolled. EEG recordings were scored in a standardized manner and categorized. EEG category 1 consisted of continuous and reactive tracings. EEG category 2 consisted of continuous but unreactive tracings. EEG category 3 included those with any degree of discontinuity, burst suppression, or lack of cerebral activity. The primary outcome was unfavorable short-term outcome defined as Pediatric Cerebral Performance Category score of 4–6 (severe disability, vegetative, death) at hospital discharge. Univariate analyses of the association between EEG category and outcome was performed using logistic regression.
For tracings obtained during hypothermia, patients with EEGs in categories 2 or 3 were far more likely to have poor outcome than those in category 1 (OR 10.7, P = 0.023 and OR 35, P = 0.004, respectively). Similarly, for tracings obtained during normothermia, patients with EEGs in categories 2 or 3 were far more likely to have poor outcomes than those in category 1 (OR 27, P = 0.006 and OR 18, P = 0.02, respectively).
A simple EEG classification scheme has predictive value for short-term outcome in children undergoing TH after CA.
Therapeutic hypothermia; Outcome; Pediatric; Hypoxic ischemic encephalopathy; Heart arrest; Prognosis
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.
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
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.
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
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
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
Generalized tonic-clonic seizures (GTCS) are the commonest seizure type associated with Sudden Unexplained Death in Epilepsy (SUDEP). This study examines semiological and electroencephalographic differences (EEG) in the GTCS of adults as compared to children. The rationale lies in epidemiological observations that have noted a ten-fold higher incidence of SUDEP in adults. We analyzed video-EEG data of 105 GTCS in 61 consecutive patients (12 children, 23 seizures and 49 adults, 82 seizures) recruited from the Epilepsy Monitoring Unit. Semiological, EEG and 3-channel EKG features were studied. Peri-ictal seizure phase durations were analyzed including tonic, clonic, total seizure, post-ictal EEG suppression (PGES) and recovery phases. Heart rate variability (HRV) measures including RMSSD (root mean square successive difference of R-R intervals), SDNN (standard deviation of NN intervals) and SDSD (standard deviation of differences) were analyzed (including low frequency/high frequency power ratios) during pre-ictal baseline, ictal and post-ictal phases. Generalized estimating equations (GEE) were used to find associations between electro-clinical features. Separate subgroup analyses were carried out on adult and pediatric age groups as well as medication groups (no anti-epileptic medication cessation versus unchanged or reduced medication) during admission. Major differences were seen in adult and pediatric seizures with total seizure duration, tonic phase, PGES and recovery phases being significantly shorter in children (p<0.01). GEE analysis using tonic phase duration as the dependent variable, found age to correlate significantly (p<0.001) and this remained significant during subgroup analysis (adults and children) such that each 0.12 second increase in tonic phase duration correlated with a 1 second increase in PGES duration. PGES durations were on average 28 seconds shorter in children. With cessation of medication, total seizure duration was significantly increased by a mean value of 8 seconds in children and 11 seconds in adults (p<0.05). Tonic phase duration also significantly increased with medication cessation and although PGES durations increased, this was not significant. RMSSD was negatively correlated with PGES duration (longer PGES durations were associated with decreased vagally mediated heart rate variability; p<0.05) but not with tonic phase duration. This study clearly points out identifiable electro-clinical differences between adult and pediatric GTCS that may be relevant in explaining lower SUDEP risk in children. The findings suggest that some prolonged seizure phases and prolonged PGES duration may be electro-clinical markers of SUDEP risk and merit further study.
Generalized tonic-clonic seizures; Age-specific; SUDEP; PGES
Electroencephalography (EEG) has a central role in the outcome prognostication in subjects with anoxic/hypoxic encephalopathy following a cardiac arrest (CA). Continuous EEG monitoring (cEEG) has been consistently developed and studied; however, its yield as compared to repeated standard EEG (sEEG) is unknown.
We studied a prospective cohort of comatose adults treated with therapeutic hypothermia (TH) after a CA. cEEG data regarding background activity and epileptiform components were compared to two 20-minute sEEGs extracted from the cEEG recording (one during TH, and one in early normothermia).
Thirty-four recordings were studied. During TH, the agreement between cEEG and sEEG was 97.1% (95% CI: 84.6 to 99.9%) for background discontinuity and reactivity evaluation, while it was 94.1% (95% CI 80.3 to 99.2%) regarding epileptiform activity. In early normothermia, we did not find any discrepancies. Thus, concordance results were very good during TH (kappa 0.83), and optimal during normothermia (kappa = 1). The median delay between CA and the first EEG reactivity testing was 18 hours (range: 4.75 to 25) for patients with perfect agreement and 10 hours (range: 5.75 to 10.5) for the three patients with discordant findings (P = 0.02, Wilcoxon).
Standard intermittent EEG has comparable performance with continuous EEG both for variables important for outcome prognostication (EEG reactivity) and identification of epileptiform transients in this relatively small sample of comatose survivors of CA. This finding has an important practical implication, especially for centers where EEG resources are limited.
The aim of this study was to establish the relationship between background amplitude and interictal abnormalities in routine electroencephalography (EEG).
This retrospective audit was conducted between July 2006 and December 2009 at the Department of Clinical Physiology at Sultan Qaboos University Hospital (SQUH) in Muscat, Oman. A total of 1,718 electroencephalograms (EEGs) were reviewed. All EEGs were from patients who had been referred due to epilepsy, syncope or headaches. EEGs were divided into four groups based on their amplitude: group one ≤20 μV; group two 21–35 μV; group three 36–50 μV, and group four >50 μV. Interictal abnormalities were defined as epileptiform discharges with or without associated slow waves. Abnormalities were identified during periods of resting, hyperventilation and photic stimulation in each group.
The mean age ± standard deviation of the patients was 27 ± 12.5 years. Of the 1,718 EEGs, 542 (31.5%) were abnormal. Interictal abnormalities increased with amplitude in all four categories and demonstrated a significant association (P <0.05). A total of 56 EEGs (3.3%) had amplitudes that were ≤20 μV and none of these showed interictal epileptiform abnormalities.
EEG amplitude is an important factor in determining the presence of interictal epileptiform abnormalities in routine EEGs. This should be taken into account when investigating patients for epilepsy. A strong argument is made for considering long-term EEG monitoring in order to identify unexplained seizures which may be secondary to epilepsy. It is recommended that all tertiary institutions provide EEG telemetry services.
Electroencephalography, abnormalities; Epilepsy
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) 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
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.
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.
There has been a dramatic change in hospital care of cardiac arrest survivors in recent years, including the use of target temperature management (hypothermia). Clinical signs of recovery or deterioration, which previously could be observed, are now concealed by sedation, analgesia, and muscle paralysis. Seizures are common after cardiac arrest, but few centers can offer high-quality electroencephalography (EEG) monitoring around the clock. This is due primarily to its complexity and lack of resources but also to uncertainty regarding the clinical value of monitoring EEG and of treating post-ischemic electrographic seizures. Thanks to technical advances in recent years, EEG monitoring has become more available. Large amounts of EEG data can be linked within a hospital or between neighboring hospitals for expert opinion. Continuous EEG (cEEG) monitoring provides dynamic information and can be used to assess the evolution of EEG patterns and to detect seizures. cEEG can be made more simple by reducing the number of electrodes and by adding trend analysis to the original EEG curves. In our version of simplified cEEG, we combine a reduced montage, displaying two channels of the original EEG, with amplitude-integrated EEG trend curves (aEEG). This is a convenient method to monitor cerebral function in comatose patients after cardiac arrest but has yet to be validated against the gold standard, a multichannel cEEG. We recently proposed a simplified system for interpreting EEG rhythms after cardiac arrest, defining four major EEG patterns. In this topical review, we will discuss cEEG to monitor brain function after cardiac arrest in general and how a simplified cEEG, with a reduced number of electrodes and trend analysis, may facilitate and improve care.
We describe and characterize the performance of microEEG compared to that of a commercially available and widely used clinical EEG machine. microEEG is a portable, battery-operated, wireless EEG device, developed by Bio-Signal Group to overcome the obstacles to routine use of EEG in emergency departments (EDs).
The microEEG was used to obtain EEGs from healthy volunteers in the EEG laboratory and ED. The standard system was used to obtain EEGs from healthy volunteers in the EEG laboratory, and studies recorded from patients in the ED or ICU were also used for comparison. In one experiment, a signal splitter was used to record simultaneous microEEG and standard EEG from the same electrodes.
EEG signal analysis techniques indicated good agreement between microEEG and the standard system in 66 EEGs recorded in the EEG laboratory and the ED. In the simultaneous recording the microEEG and standard system signals differed only in a smaller amount of 60 Hz noise in the microEEG signal. In a blinded review by a board-certified clinical neurophysiologist, differences in technical quality or interpretability were insignificant between standard recordings in the EEG laboratory and microEEG recordings from standard or electrode cap electrodes in the ED or EEG laboratory. The microEEG data recording characteristics such as analog-to-digital conversion resolution (16 bits), input impedance (>100MΩ), and common-mode rejection ratio (85 dB) are similar to those of commercially available systems, although the microEEG is many times smaller (88 g and 9.4 × 4.4 × 3.8 cm).
Our results suggest that the technical qualities of microEEG are non-inferior to a standard commercially available EEG recording device. EEG in the ED is an unmet medical need due to space and time constraints, high levels of ambient electrical noise, and the cost of 24/7 EEG technologist availability. This study suggests that using microEEG with an electrode cap that can be applied easily and quickly can surmount these obstacles without compromising technical quality.
Electroencephalography (EEG); EEG technology; EEG machine; Signal analysis; Emergency department
This study examines electroencephalographic (EEG) changes in children with medication resistant epilepsy treated with the ketogenic diet (KD).
Routine EEGs were obtained prior to KD initiation, then one month and three months later. Changes in EEG background slowing and frequency of interictal epileptiform discharges (IEDs) were evaluated using power spectrum analysis and manual determination of spike index. KD responders were compared to non-responders to determine if baseline or early EEG characteristics predicted treatment response (>50% seizure reduction) at three months.
Thirty-seven patients were evaluated. No differences in baseline EEG features were found between responder groups. Frequency of IEDs declined in 65% of patients as early as one month, by a median of 13.6% (IQR 2-33). Those with a ten percent or greater improvement in IED frequency at one month were greater than six times more likely to be KD responders (OR 6.5 95% CI 0.85 to 75. p=0.03). Qualitative and quantitative measures of EEG background slowing improved in the whole cohort, but did not predict responder status.
Baseline predictors of KD response remain elusive. Most patients experienced a reduction in IEDs and improvement in EEG background slowing after KD initiation. Reduction of IEDs at one month strongly predicted KD responder status at three months.
Ketogenic Diet; EEG; spike index; power spectrum analysis