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
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.
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.
High doses of dextromethorphan (20-42 mg/kg/day) were given to four critically ill children with seizures and frequent epileptiform abnormalities in the EEG that were refractory to antiepileptic drugs. Their acute diseases (hypoxia, head trauma and hypoxia, neurodegenerative disease, hypoglycaemia) were thought to be due in part to N-methyl-D-aspartate (NMDA) receptor mediated processes. Treatment with dextromethorphan, an NMDA receptor antagonist, was started between 48 hours and 14 days after the critical incident. In three patients the EEG improved considerably within 48 hours and seizures ceased within 72 hours. In the patient with neurodegenerative disease the effect on the EEG was impressive, but the seizures were not controlled. Despite the improvement of the EEG the clinical outcome was poor in all children: three died in the critical period or due to the progressing disease; the patient with hypoglycaemia survived with severe neurological sequelae. Plasma concentrations of dextromethorphan varied between 74-1730 ng/ml and its metabolite dextrorphan varied between 349-3790 ng/ml. In one patient corresponding concentrations in CSF were lower than those in plasma. The suppression of epileptic discharges by the doses of dextromethorphan given suggests that such doses are sufficient to block NMDA receptors.
Methods: MEG and simultaneous EEG were recorded with a 204 channel whole head MEG system. Ten habitual seizures occurred during the acquisition, which was done twice. The equivalent current dipoles (ECDs) for ictal discharges on MEG were calculated using a single dipole model. The ECDs were superimposed on a magnetic resonance image.
Results: During the seizures, EEG showed prolonged bursts of 5–6 Hz high voltage slow waves with spike components, dominantly in the bilateral frontal region. MEG showed epileptiform discharges corresponding to the ictal EEG. Ictal discharges on MEG were dominant in the frontal area in the initial portion, and then spread in the bilateral temporal area in the middle of the seizure. ECDs obtained from the spikes of the initial portion were clustered in the medial frontal lobe.
Conclusions: The source of the ictal MEG was localised in the medial frontal lobe. The findings suggest that the mechanism underlying epilepsy in this case might be similar to medial frontal lobe epilepsy. Ictal MEG is a valuable tool for detecting the site of seizure onset.
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
To develop standardized definitions for classification of partial seizure symptoms for use in genetic research on the epilepsies, and evaluate inter-rater reliability of classifications based on these definitions.
The authors developed the Partial Seizure Symptom Definitions (PSSD), which include standardized definitions of 41 partial seizure symptoms within the sensory, autonomic, aphasic, psychic, and motor categories. Based on these definitions, two epileptologists independently classified partial seizures in 75 individuals from 34 families selected because one person had ictal auditory symptoms or aphasia. The data used for classification consisted of standardized diagnostic interviews with subjects and family informants, and medical records obtained from treating neurologists. Agreement was assessed by kappa.
Agreement between the two neurologists using the PSSD was “substantial” or “almost perfect” for most symptom categories.
Use of standardized definitions for classification of partial seizure symptoms such as those in the Partial Seizure Symptom Definitions should improve reliability and accuracy in future genetic studies of the epilepsies.
Electroencephalography (EEG) has an important role in the diagnosis and classification of epilepsy. It can provide information for predicting the response to antiseizure drugs and to identify the surgically remediable epilepsies. In temporal lobe epilepsy (TLE) seizures could originate in the medial or lateral neocortical temporal region, and many of these patients are refractory to medical treatment. However, majority of patients have had excellent results after surgery and this often relies on the EEG and magnetic resonance imaging (MRI) data in presurgical evaluation. If the scalp EEG data is insufficient or discordant, invasive EEG recording with placement of intracranial electrodes could identify the seizure focus prior to surgery. This paper highlights the general information regarding the use of EEG in epilepsy, EEG patterns resembling epileptiform discharges, and the interictal, ictal and postictal findings in mesial temporal lobe epilepsy using scalp and intracranial recordings prior to surgery. The utility of the automated seizure detection and computerized mathematical models for increasing yield of non-invasive localization is discussed. This paper also describes the sensitivity, specificity, and predictive value of EEG for seizure recurrence after withdrawal of medications following seizure freedom with medical and surgical therapy.
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
In the diagnostic microbiology laboratory, interpretation of Gram-stained slides of vaginal swab specimens is used to support the clinical diagnosis of bacterial vaginosis. The reproducibility with which technologists interpret these Gram-stained slides was evaluated by presenting, in coded fashion, 80 original slides and 80 duplicate slides of vaginal swab specimens to three technologists. They each interpreted the original slide twice and the duplicate slide from the same specimen once. Intraobserver and interobserver agreement was assessed by use of the weighted kappa statistic. Semiquantitation of Lactobacillus and Gardnerella morphotypes and a diagnosis of bacterial vaginosis showed the greatest intraobserver agreement, with kappa values ranging from 0.772 to 1.000. Interobserver agreement was also high for rating Lactobacillus morphotypes and clue cells (kappa values between 0.735 and 0.869) but decreased slightly for Gardnerella morphotypes and a diagnosis of bacterial vaginosis (kappa values between 0.656 and 0.800). These results indicate that there is good agreement for the interpretation of Gram-stained slides of vaginal swab specimens and that this method alone, without culture, can be used reliably to support the clinical diagnosis of bacterial vaginosis.
Functional hemispherectomy is effective in carefully selected patients, resulting in a reduction of seizure burden up to complete resolution, improvement of intellectual development, and developmental benefit despite possible additional neurological deficit. Despite apparent hemispheric pathology on brain magnetic resonance imaging (MRI) or other imaging tests, scalp electroencephalography (EEG) could be suggestive of bilateral ictal onset or even ictal onset contralateral to the dominant imaging abnormality. We aimed to investigate the role of scalp EEG lateralization pre-operatively in predicting outcome.
We retrospectively reviewed 54 patients who underwent hemispherectomy between 1991 and 2009 at Medical College of Georgia (1991–2006) and Cincinnati Children’s Hospital Medical Center (2006–2009) and had at least one year post-operative follow-up. All preoperative EEGs were reviewed, and classified as either lateralizing or nonlateralizing, for both ictal and interictal EEG recordings.
Of 54 patients, 42 (78%) became seizure free. Twenty-four (44%) of 54 had a nonlateralizing ictal or interictal EEG. Further analysis was based on etiology of epilepsy, including malformation of cortical development (MCD), Rasmussen syndrome (RS), and stroke (CVA). EEG nonlateralization did not predict poor outcome in any of the etiology groups evaluated.
Scalp EEG abnormalities in contralateral or bilateral hemispheres do not, in isolation, predict a poor outcome from hemispherectomy. Results of other non-invasive and invasive evaluations should be used to determine candidacy.
Hemispherectomy; Hemispherotomy; EEG; Bilateral; Outcome
We examined 114 segments in 23 patients' lumbar spine plain radiographs affected by disc degeneration. Two consultant orthopaedic surgeons, two consultant radiologists, and one spine nurse practitioner made independent observations on the radiographs. MRI scan films of the corresponding 114 segments were used as a gold standard. Kappa coefficients were used to evaluate the interobserver error, and the error between the independent observers and the MRI scanning reports. The systematic differences between the observers for the diagnosis of the disc degeneration at each segment level was recorded.
There was significant interobserver error between the independent observers. The pairwise interobserver agreement ranged from fair to substantial on the plain radiograph observations [Weighted kappa coefficient, mean: 0.517 (CI=0.388-0.646)]. The pairwise interobserver agreement between the independent observers and the MRI scan ranged from fair to moderate [Weighted kappa coefficient, mean: 0.388 (CI=0.259-0.518)].
There is significant error in interpretation of the plain radiographs for the diagnosis of lumbar disc degeneration. MRI may be more accurate in the diagnosis of lumbar disc degeneration.
It has been proposed that intensive care unit (ICU)-acquired weakness (ICUAW) should be assessed using the sum of manual muscle strength test scores in 12 muscle groups (the sum score). This approach has been tested in patients with Guillain-Barré syndrome, yet little is known about the feasibility or test characteristics in other critically ill patients. We studied the feasibility and interobserver agreement of this sum score in a mixed cohort of critically ill and injured patients.
We enrolled patients requiring more than 3 days of mechanical ventilation. Two observers performed systematic strength assessments of each patient. The primary outcome measure was interobserver agreement of weakness as a binary outcome (ICUAW is sum score less than 48; "no ICUAW" is a sum score greater than or equal to 48) using the Cohen's kappa statistic.
We identified 135 patients who met the inclusion criteria. Most were precluded from study participation by altered mental status or polytrauma. Thirty-four participants were enrolled, and 30 of these individuals completed assessments conducted by both observers. Six met the criteria for ICUAW recorded by at least one observer. The observers agreed on the diagnosis of ICUAW for 93% of participants (Cohen's kappa = 0.76; 95% confidence interval (CI), 0.44 to 1.0). Observer agreement was fair in the ICU (Cohen's kappa = 0.38), and agreement was perfect after ICU discharge (Cohen's kappa = 1.0). Absolute values of sum scores were similar between observers (intraclass correlation coefficient 0.83; 95% CI, 0.67 to 0.91), but they differed between observers by six points or more for 23% of the participants.
Manual muscle testing (MMT) during critical illness was not possible for most patients because of coma, delirium and/or injury. Among patients who were able to participate in testing, we found that interobserver agreement regarding ICUAW was good, particularly when evaluated after ICU discharge. MMT is insufficient for early detection of ICU-acquired neuromuscular dysfunction in most patients and may be unreliable during critical illness.
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.
Objectives: To identify predictive factors for the seizure-free outcome of vagus nerve stimulation (VNS).
Methods: All 47 patients who had undergone VNS implantation at one centre and had at least one year of follow up were studied. They underwent complete presurgical evaluation including detailed clinical history, magnetic resonance imaging, and long term video-EEG with ictal and interictal recordings. After implantation, adjustment of stimulation parameters and concomitant antiepileptic drugs were at the discretion of the treating physician.
Results: Mean (SD) age of the patients was 22.7 (11.6) years (range 7 to 53). Six patients (13%) became seizure-free after the VNS implantation. Only two variables showed a significant association with the seizure-free outcome: absence of bilateral interictal epileptiform discharges (IED) and presence of malformation of cortical development (MCD). Epilepsy duration showed a non-significant trend towards a negative association with outcome. By logistic regression analysis, only absence of bilateral IED correlated independently with successful VNS treatment (p<0.01, odds ratio = 29.2 (95% confidence interval, 2.4 to 353)). Bilateral IED (independent or bilateral synchronous) was found in one of six seizure-free patients and in 33 of 41 non-seizure-free patients. When bilateral IED were absent, the sensitivity for seizure-free outcome was 0.83 (0.44 to 0.97), and the specificity was 0.80 (0.66 to 0.90).
Conclusions: Bilateral IED was independently associated with the outcome of VNS. These results are preliminary because they were based on a small patient population. They may facilitate prospective VNS studies enrolling larger numbers of patients to confirm the results.
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
Epileptic seizures are due to abnormal synchronized neuronal discharges. Techniques measuring electrical changes are commonly used to analyze seizures. Neuronal activity can be also defined by concomitant hemodynamic and metabolic changes. Simultaneous electroencephalogram (EEG)-functional MRI (fMRI) measures noninvasively with a high-spatial resolution BOLD changes during seizures in the whole brain. Until now, only a static image representing the whole seizure was provided. We report in 10 focal epilepsy patients a new approach to dynamic imaging of seizures including the BOLD time course of seizures and the identification of brain structures involved in seizure onset and discharge propagation. The first activation was observed in agreement with the expected location of the focus based on clinical and EEG data (three intracranial recordings), thus providing validity to this approach. The BOLD signal preceded ictal EEG changes in two cases. EEG-fMRI may detect changes in smaller and deeper structures than scalp EEG, which can only record activity form superficial cortical areas. This method allowed us to demonstrate that seizure onset zone was limited to one structure, thus supporting the concept of epileptic focus, but that a complex neuronal network was involved during propagation. Deactivations were also found during seizures, usually appearing after the first activation in areas close or distant to the activated regions. Deactivations may correspond to actively inhibited regions or to functional disconnection from normally active regions. This new noninvasive approach should open the study of seizure generation and propagation mechanisms in the whole brain to groups of patients with focal epilepsies.
PMID: 19507156 CAMSID: cams3405
seizure; epilepsy; imaging; fMRI; EEG
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.
In this study, we investigated the possibility of differential diagnosis of patients with epileptic seizures (ES) and patients with psychogenic non-epileptic seizures (PNES) by an advanced analysis of dynamics of the patients' scalp electroencephalograms (EEG). The underlying principle was the presence of resetting of brain's pre-ictal spatiotemporal entrainment following onset of ES and the absence of resetting following PNES. Long-term (days) scalp EEGs recorded from five ES and six PNES patients were analyzed. It was found that: (a) Pre-ictal entrainment of brain sites was reset by epileptic seizures (p<0.05) in 4 out of the 5 patients with ES, and not reset (p=0.28) in the fifth patient. (b) Resetting did not occur (p>0.1) in any of the 6 patients with PNES. These preliminary results in patients with ES are in agreement with our previous findings from intracranial EEG recordings on resetting of brain dynamics at ES and it is expected to constitute the basis for the development of a reliable and supporting tool in the differential diagnosis between ES and PNES. Finally, we believe that these results shed a novel light on the electrophysiology of psychogenic epilepsy by showing that occurrence of PNES does not assist patients to overcome a pathological entrainment of brain dynamics.
Epileptic Seizures; Psychogenic Non-Epileptic Seizures; Electroencephalography; Spatiotemporal Brain Dynamics; Seizure Resetting
Neonatal seizures are common, often require electroencephalographic (EEG) monitoring for diagnosis and management, may be associated with worse neurodevelopmental outcome, and can often be treated with existing anticonvulsants. A neonatal electrographic seizure is defined as a sudden, repetitive, evolving and stereotyped event of abnormal electrographic pattern with amplitude of at least two microvolts and a minimum duration of ten seconds. The diagnosis of neonatal seizures relies heavily on the neurophysiologist’s interpretation of EEG. Consideration of specific criteria for the definition of a neonatal seizure, including seizure duration, location, morphology, evolution, semiology, and overall seizure burden, have utility for both the clinician and researcher. We review the importance of EEG in the diagnosis and management of neonatal seizures, the electrographic characteristics of neonatal seizures, the impact of neonatal seizures on outcome, and tools to aid in the identification of neonatal seizures.
seizures; neonate; electroencephalography; status epilepticus
Interictal FDG-PET (iPET) is a core tool for localizing the epileptogenic focus, potentially before structural MRI, that does not require rare and transient epileptiform discharges or seizures on EEG. The visual interpretation of iPET is challenging and requires years of epilepsy-specific expertise. We have developed an automated computer-aided diagnostic (CAD) tool that has the potential to work both independent of and synergistically with expert analysis. Our tool operates on distributed metabolic changes across the whole brain measured by iPET to both diagnose and lateralize temporal lobe epilepsy (TLE). When diagnosing left TLE (LTLE) or right TLE (RTLE) vs. non-epileptic seizures (NES), our accuracy in reproducing the results of the gold standard long term video-EEG monitoring was 82% [95% confidence interval (CI) 69–90%] or 88% (95% CI 76–94%), respectively. The classifier that both diagnosed and lateralized the disease had overall accuracy of 76% (95% CI 66–84%), where 89% (95% CI 77–96%) of patients correctly identified with epilepsy were correctly lateralized. When identifying LTLE, our CAD tool utilized metabolic changes across the entire brain. By contrast, only temporal regions and the right frontal lobe cortex, were needed to identify RTLE accurately, a finding consistent with clinical observations and indicative of a potential pathophysiological difference between RTLE and LTLE. The goal of CADs is to complement – not replace – expert analysis. In our dataset, the accuracy of manual analysis (MA) of iPET (∼80%) was similar to CAD. The square correlation between our CAD tool and MA, however, was only 30%, indicating that our CAD tool does not recreate MA. The addition of clinical information to our CAD, however, did not substantively change performance. These results suggest that automated analysis might provide clinically valuable information to focus treatment more effectively.
epilepsy; computer-aided diagnosis; mutual information; temporal lobe epilepsy; PET; fluoro-deoxyglucose positron emission tomography; machine learning
Epileptogenic zones can be localized by F-18 fluorodeoxyglucose positron emission tomography (FDG PET) and ictal single-photon emission computed tomography(SPECT). In medial temporal lobe epilepsy, the diagnostic sensitivity of FDG PET or ictal SPECT is excellent, however, the sensitivity of MRI is so high that the incremental sensitivity by FDG PET or ictal SPECT has yet to be proven. When MRI findings are ambiguous or normal, or discordant with those of ictal EEG, FDG PET and ictal SPECT are helpful for localization without the need for invasive ictal EEG. In neocortical epilepsy, the sensitivities of FDG PET or ictal SPECT are fair. However, because almost a half of the patients are normal on MRI, FDG PET and ictal SPECT are helpful for localization or at least for lateralization in these non-lesional epilepsies in order to guide the subdural insertion of electrodes. Interpretation of FDG PET has been recently advanced by voxel-based analysis and automatic volume of interest analysis based on a population template. Both analytical methods confirmed the performance of previous visual interpretation results. Ictal SPECT was analyzed using subtraction methods(coregistered to MRI) and voxel-based analysis. Rapidity of injection of tracers, HMPAO versus ECD, and repeated ictal SPECT, which remain the technical issues of ictal SPECT, are detailed.
Epilepsy is a common, chronic neurological disorder that affects more than 40 million people worldwide. Epilepsy is characterized by interictal and ictal functional disturbances. The presence of interictal epileptiform discharges (IEDs) can help to confirm a clinical diagnosis of epilepsy, and their location and characteristics can help to identify the epileptogenic zone or suggest a particular epilepsy syndrome. The aim of this study is to determine the factors that affect IEDs.
Materials and Methods:
Poisson marginal model was done on 60 epileptic patients who were referred to Shefa Neurological Research Center, Tehran, for Video-Electroencephalogram (V-EEG) monitoring from 2007 to 2011. The frequency of IEDs was assessed by visual analysis of interictal EEG samples for 2 h.
The results show that among age, epilepsy duration, gender, seizure frequency and two common anti-epileptic drugs (Valproic acid and Carbamazepine), only age and epilepsy duration had statistical significant effect on IED frequency.
Investigating the factors affecting IED is not only of theoretical importance, but may also have clinical relevance as understanding the evolution of interictal epileptogenesis may lead to the development of therapeutic interventions. Generalized estimating equation is a valid statistical technique for studying factors that affect on IED. This research demonstrates epilepsy duration has positive and age has negative effect on IED which means that IED increases with epilepsy duration and decreases with increasing age. So for monitoring IED, we should consider both age and epilepsy duration of each patient.
Epilepsy; interictal epileptiform discharge; electroencephalogram; Poisson marginal model; generalized estimating equations
Traumatic brain injury and generalized convulsive status epilepticus (GCSE) are conditions that require aggressive management. Barbiturates are used to lower intracranial pressure or to stop epileptiform activity, with the aim being to improve neurological outcome. Dosing of barbiturates is usually guided by the extent of induced burst-suppression pattern on the electroencephalogram (EEG). Dosing beyond the point of burst suppression may increase the risk for complications without offering further therapeutic benefit. For this reason, careful monitoring of EEG parameters is mandatory. A prospective study was conducted to evaluate the usefulness of the bispectral index suppression ratio for monitoring barbiturate coma.
A prospective observational pilot study was performed at a paediatric (surgical) intensive care unit, including all children with barbiturate-induced coma after traumatic brain injury or GCSE. The BIS™ (Bispectral™ index) monitor expresses a suppression ratio, which represents the percentage of epochs per minute in which the EEG was suppressed. Suppression ratios from the BIS monitor were compared with suppression ratios of full-channel EEG as assessed by quantitative visual analysis.
Five patients with GCSE and three patients after traumatic brain injury (median age 11.6 years, range 4 months to 15 years) were included. In four patients the correlation between the suppression ratios of the BIS and EEG could be determined; the average correlation was 0.68. In two patients, suppression ratios were either high or low, with no intermediate values. This precluded determination of correlation values, as did the isoelectric EEG in a further two patients. In the latter patients, the mean ± standard error BIS suppression ratio was 95 ± 1.6.
Correlations between suppression ratios of the BIS and EEG were found to be only moderate. In particular, asymmetrical EEGs and EEGs with short bursts (less than 1 second) may result in aberrant BIS suppression ratios. The BIS monitor potentially aids monitoring of barbiturate-induced coma because it provides continuous data on EEG suppression between full EEG registrations, but it should be used with caution.