Nonepileptiform Abnormalities Research Articles

Towards trustworthy seizure onset detection using workflow notes

A major barrier to deploying healthcare AI is trustworthiness. One form of trustworthiness is a model’s robustness across subgroups: while models may exhibit expert-level performance on aggregate metrics, they often rely on non-causal features, leading to errors in hidden subgroups. To take a step closer towards trustworthy seizure onset detection from EEG, we propose to leverage annotations that are produced by healthcare personnel in routine clinical workflows—which we refer to as workflow notes—that include multiple event descriptions beyond seizures. Using workflow notes, we first show that by scaling training data to 68,920 EEG hours, seizure onset detection performance significantly improves by 12.3 AUROC (Area Under the Receiver Operating Characteristic) points compared to relying on smaller training sets with gold-standard labels. Second, we reveal that our binary seizure onset detection model underperforms on clinically relevant subgroups (e.g., up to a margin of 6.5 AUROC points between pediatrics and adults), while having significantly higher FPRs (False Positive Rates) on EEG clips showing non-epileptiform abnormalities (+19 FPR points). To improve model robustness to hidden subgroups, we train a multilabel model that classifies 26 attributes other than seizures (e.g., spikes and movement artifacts) and significantly improve overall performance (+5.9 AUROC points) while greatly improving performance among subgroups (up to +8.3 AUROC points) and decreasing false positives on non-epileptiform abnormalities (by 8 FPR points). Finally, we find that our multilabel model improves clinical utility (false positives per 24 EEG hours) by a factor of 2×.

How soon should urgent EEG be performed following a first epileptic seizure?

PurposePatients with a first unprovoked epileptic seizure are often seen in emergency services. Electroencephalography (EEG) is indicated for diagnosing epilepsy, but the optimal time to perform this test has not been defined. This study aimed to determine the time interval following a seizure within which EEG has the greatest diagnostic yield. MethodsWe conducted a retrospective study of all adult patients with a first unprovoked seizure who had undergone emergency EEG (July 2014–December 2019). Data collection included demographics, seizure type, time interval to EEG study, EEG pattern identified, and the prescription after emergency assessment. An optimal cut-off point for time to EEG was obtained, and an adjusted regression model was performed to establish associations with the presence of epileptiform abnormalities. ResultsA total of 170 patients were included (mean age: 50.7 years, 40.6% women). Epileptiform discharges were identified in 34.1% of recordings, nonepileptiform abnormalities in 46.5%, and normal findings in 19.4%. A lower latency from seizure to EEG was associated with a higher probability of finding epileptiform discharges (median: 12.7 in the epileptiform EEGs vs. 20 h in the nonepileptiform EEGs, p < 0.001). The time interval associated with the highest probability of detecting an epileptiform EEG pattern was within the first 16 h after seizure onset: 52.1% of recordings performed before the 16-h cut-off showed these abnormal patterns compared with 20.2% performed after (p < 0.001). These findings were not related to the presence of an epileptogenic lesion in neuroimaging or to other clinical variables. The finding of epileptiform abnormalities was followed by a greater prescription of antiseizure drugs (96.4% vs. 66% in nonepileptiform patterns, p < 0.001). ConclusionThe diagnostic yield of EEG following a first unprovoked epileptic seizure is highest when this test is performed within the first 16 h after onset of the event.

Smartphone EEG and remote online interpretation for children with epilepsy in the Republic of Guinea: Quality, characteristics, and practice implications

Purpose: Children with epilepsy in low-income countries often go undiagnosed and untreated. We examine a portable, low-cost smartphone-based EEG technology in a heterogeneous pediatric epilepsy cohort in the West African Republic of Guinea. Methods: Children with epilepsy were recruited at the Ignace Deen Hospital in Conakry, 2017. Participants underwent sequential EEG recordings with an app-based EEG, the Smartphone Brain Scanner-2 (SBS2) and a standard Xltek EEG. Raw EEG data were transmitted via Bluetooth™ connection to an Android™ tablet and uploaded for remote EEG specialist review and reporting via a new, secure web-based reading platform, crowdEEG. The results were compared to same-visit Xltek 10–20 EEG recordings for identification of epileptiform and non-epileptiform abnormalities. Results: 97 children meeting the International League Against Epilepsy’s definition of epilepsy (49 male; mean age 10.3 years, 29 untreated with an antiepileptic drug; 0 with a prior EEG) were enrolled. Epileptiform discharges were detected on 21 (25.3%) SBS2 and 31 (37.3%) standard EEG recordings. The SBS2 had a sensitivity of 51.6% (95%CI 32.4%, 70.8%) and a specificity of 90.4% (95%CI 81.4%, 94.4%) for all types of epileptiform discharges, with positive and negative predictive values of 76.2% and 75.8% respectively. For generalized discharges, the SBS2 had a sensitivity of 43.5% with a specificity of 96.2%. Conclusions: The SBS2 has a moderate sensitivity and high specificity for the detection of epileptiform abnormalities in children with epilepsy in this low-income setting. Use of the SBS2+crowdEEG platform permits specialist input for patients with previously poor access to clinical neurophysiology expertise.

O-20 Epileptiform and non-epileptiform EEG abnormalities in Autism Spectrum Disorder relate to opposite excitation-inhibition balance disturbances

Background Autism spectrum disorder (ASD) has been associated to balance alterations between neuronal excitation and inhibition (E/I). Part of this association is built upon the frequent concurrence of ASD with epilepsy and epileptiform abnormalities. Non-epileptiform EEG abnormalities are also frequent, yet their clinical relevance remains unclear. We hypothesize that subjects with EEG abnormalities – epileptiform and non-epileptiform – represent different physiological subgroups within the autism spectrum and have different effects on E/I balance. Using a novel algorithm involving critical-state dynamics we show that fluctuations in neuronal oscillations can be used to estimate E/I balance of neuronal networks. Here, we use this method to test the relationship between epileptiform and non-epileptiform abnormalities and E/I balance disturbance in ASD. Methods We investigated 64-channel EEG recordings of 100 unmedicated children with ASD and 29 typically developing children (age range 7–15 years). We assessed for EEG abnormalities according to Luders and Noachtar’s atlas and classification of electroencephalography and estimated the E/I ratio based on alpha oscillations. Results ASD subjects with EEG abnormalities (ASDabn, 46%) had lower E/I ratios than the group without abnormalities (p = 0.00035). From the ASDabn group, subjects with slowing of activity showed the lowest global E/I ratios (p = 0.0001), while epileptiform abnormalities were related to localized high E/I (p = 0.0063). Conclusions Epileptiform and non-epileptiform EEG abnormalities showed opposite excitation-inhibition balance disturbances identifying two subgroups within ASD – one with global increase of inhibition and one with locally increased excitation. This neurophysiological distinction may be determinant for targeting future treatment decisions.

Diagnostic Performance of the Electroencephalogram in the Elderly Manifesting With Episodes of Unresponsiveness.

This study aimed to determine diagnostic performance of the EEG in diagnosing seizures and to ascertain risk factors associated with seizures in the elderly presenting with episodes of unresponsiveness compared with younger patients. This is a cross-sectional study. Only EEGs requested with indication of transient unresponsiveness (TUR) were included. Patients were divided into 2 groups, younger (aged 18-49 years) and elderly (aged >60 years). The EEG was an index test. Reference standard, independently verified by 2 board-certified neurologists, was a clinical diagnosis of presence or absence of epilepsy/probable seizures. Univariate and multivariate analyses were performed to ascertain associated risk factors for epilepsy/probable seizures. Among 2187 total EEG recordings, 244 (11.16%) recordings were requested with indication of TUR. A total of 156 patients (50 younger and 106 elderly) were recruited for analysis. Prevalence of epilepsy/probable seizures in patients with TUR was 26.9%. Prevalence of interictal epileptiform discharges was 16% in the younger and 12.3% in the elderly. Overall diagnostic performance was poorer in the elderly. Associated risk factors for having seizures were presence of intermittent slow waves in the younger and presence of positive motor signs as well as presence of nonepileptiform abnormalities in the elderly. Prevalence of seizures and interictal epileptiform discharges was low in the elderly who manifests with TUR. Overall diagnostic performance of the EEG in diagnosing seizures was poorer in the elderly mainly due to low sensitivity. When we encounter patients with TUR, thorough and detailed history is still a mainstay of a diagnosis of seizures, not the EEG results.

Limited role for routine EEG in the assessment of staring in children with autism spectrum disorder

AimThe assessment of staring episodes in children with autism spectrum disorder (ASD) is difficult due to the range of diagnostic possibilities, the increased frequency of epileptiform activity on electroencephalogram (EEG),...

Diagnostic Yield of Electroencephalography in a General Inpatient Population

Objective: To determine the frequency and clinical predictors of seizures and markers of epileptiform activity in a non-critically ill general inpatient population. Patients and Methods: We performed a retrospective cohort study of patients 18 years and older who underwent inpatient electroencephalography (EEG) between January 1, 2005, and December 31, 2010, for an indication of spells or altered mental status. The EEGs and reports were reviewed for ictal activity, interictal epileptiform abnormalities, and nonepileptiform abnormalities. Demographie and clinical data were gathered from the electronic medical record to determine seizure predictors. Results: Of 2235 patients screened, 1048 met the inclusion criteria, of which 825 (78.7%) had an abnormal EEG finding. Seizures occurred in 78 of 1048 patients (7.4%), and interictal epileptiform discharges were noted in 194 of 1048 patients (18.5%). An intracranial mass and spells as the indication for the EEG were independently associated with the group of patients experiencing seizures in a multivariate logistic regression model (adjusted for age, sex, EEG indication, intracranial mass, stroke, and history of epilepsy). Ninety-seven percent of patients (69 of 71) experienced their first seizure within 24 hours of monitoring, and the presence of seizures was associated with a lower likelihood of being discharged (odds ratio, 0.45; 95% CI, 0.27–0.76). Conclusion: Seizures occurred at a high frequency in hospitalized patients with spells and altered mental status. The EEG may be an underused investigative tool in the hospital with the potential to identify treatable causes of these common disoreders.

Blume's Atlas of Pediatric and Adult Electroencephalographyedited by BlumeWarren T., HollowayGiannina M., KaibaraMasakoand YoungG. Bryan, 704 pp., Lippincott Williams &amp; Wilkins, 2011, $293.04

edited by Warren T. Blume, Giannina M. Holloway, Masako Kaibara, and G. Bryan Young, 704 pp., Lippincott Williams & Wilkins, 2011, $293.04 Blume's Atlas of Pediatric and Adult Electroencephalography is a stellar addition to the armamentarium of EEG atlases currently available and stands out as a comprehensive, valuable teaching tool for the learner at any stage in his or her career. It would be an excellent addition to the references on the bookshelf of a neurology resident, clinical neurophysiology fellow, pediatric neurologist, experienced electroencephalographer, or EEG technologist. This new second addition complements and updates the highly acclaimed first edition published in 1995, which only covered adult EEG. This second edition covers both adult and pediatric EEG, greatly expands upon information provided in chapters from the first edition, includes numerous new EEG examples, and adds new chapters, including EEG in the intensive care unit, the role of EEG in some pediatric and adult problems, and aspects of recording technique (which is helpful for EEG technologists or those opening up an EEG laboratory). Other chapters that were previously included in the first edition and also included in this edition but expanded upon are introduction, artifacts, normal EEG, epileptiform phenomena, and nonepileptiform abnormalities. Indeed, the second edition is 128 pages longer than the first and also includes online access to the hardcover edition as an e-book and provides access to online resources via the publisher's Web site using a unique access code. The e-book allows one to have access to the entire atlas on a laptop or tablet computer at any time with the ability to search for key terms and highlight text. The online resources consist of 282 additional EEG tracings covering the topics of polarity, seizures, and spikes. These online EEGs are accompanied by a detailed explanation within accompanying figure legends, and the EEG tracing itself can be saved as a .jpg or .pdf file. This online access to additional EEGs allows the depiction of the same waveforms on different montages, shows the evolution of seizure discharges over sequential samples of EEG, and includes a large sampling of spike-like artifacts compared to true epileptiform spikes.

Diagnostic Yield of Electroencephalography in a General Inpatient Population

ObjectiveTo determine the frequency and clinical predictors of seizures and markers of epileptiform activity in a non–critically ill general inpatient population. Patients and MethodsWe performed a retrospective cohort study of patients 18 years and older who underwent inpatient electroencephalography (EEG) between January 1, 2005, and December 31, 2010, for an indication of spells or altered mental status. The EEGs and reports were reviewed for ictal activity, interictal epileptiform abnormalities, and nonepileptiform abnormalities. Demographic and clinical data were gathered from the electronic medical record to determine seizure predictors. ResultsOf 2235 patients screened, 1048 met the inclusion criteria, of which 825 (78.7%) had an abnormal EEG finding. Seizures occurred in 78 of 1048 patients (7.4%), and interictal epileptiform discharges were noted in 194 of 1048 patients (18.5%). An intracranial mass and spells as the indication for the EEG were independently associated with the group of patients experiencing seizures in a multivariate logistic regression model (adjusted for age, sex, EEG indication, intracranial mass, stroke, and history of epilepsy). Ninety-seven percent of patients (69 of 71) experienced their first seizure within 24 hours of monitoring, and the presence of seizures was associated with a lower likelihood of being discharged (odds ratio, 0.45; 95% CI, 0.27-0.76). ConclusionSeizures occurred at a high frequency in hospitalized patients with spells and altered mental status. The EEG may be an underused investigative tool in the hospital with the potential to identify treatable causes of these common disorders.

045 EEG phase coupling and network properties are abnormal in idiopathic generalised epilepsy patients and their relatives

Idiopathic generalised epilepsies (IGE) have a complex inheritance pattern. Previous studies support the genetic basis of EEG traits, with an increased prevalence of epileptiform and non-epileptiform abnormalities in unaffected relatives...

Non-epileptiform EEG abnormalities: an overview

More than 80 years after its introduction by Hans Berger, the electroencephalogram (EEG) remains as an important supplementary examination in the investigation of neurological disorders and gives valuable and accurate information about cerebral function. Abnormal EEG findings may include ictal patterns, interictal epileptiform activity and non-epileptiform abnormalities. The aim of this study is to make an overview on the main non-epileptiform EEG abnormalities, emphasizing the pathologic findings and the importance of their recognition, excluding periodic patterns and EEG physiologic changes. Scientific articles were selected from MEDLINE and PubMed database. The presence of non-epileptiform EEG abnormalities provide evidence of brain dysfunction that are not specific to a particular etiology and may be related to a number of disorders affecting the brain. Although these abnormalities are not specific, they can direct attention to the diagnostic possibilities and guide the best treatment choice.

Prognostic Significance of Interictal Epileptiform Discharges in Newly Diagnosed Seizure Disorders

EEGs performed for new-onset seizures show epileptiform discharge in approximately 18% to 56% of children and 12% to 50% of adults. An EEG after sleep deprivation improves detection of epileptiform abnormalities, showing discharge in 13% to 35% of patients whose standard EEG findings were normal. Some studies have also shown a higher yield with EEG performed within 24 hours after the seizure. The EEG is a useful diagnostic study in this clinical setting for a number of reasons. First, specific EEG abnormalities help characterize the seizure type and epilepsy syndrome, which allows more informed decisions regarding therapy and more accurate prediction of seizure control and ultimate remission. Second, in certain cases, the EEG may detect more subtle seizures, including absence, myoclonic, or partial seizures. Third, specific EEG patterns may alert the clinician to the presence of a focal cerebral lesion. Fourth, most studies have shown that an epileptiform discharge is predictive of seizure recurrence, particularly in patients with idiopathic epilepsy. In the presence of epileptiform discharge, the recurrence risk is approximately double what would be predicted after a normal EEG. The predictive value of nonepileptiform abnormalities is not clearly established.

Interobserver reliability of visual interpretation of electroencephalograms in children with newly diagnosed seizures

The reliability of visual interpretation of electroencephalograms (EEG) is of great importance in assessing the value of this diagnostic tool. We prospectively obtained 50 standard EEGs and 61 EEGs after partial sleep deprivation from 93 children (56 males, 37 females) with a mean age of 6 years 10 months (SE 5 mo; range 4 mo-15 y 7 mo) with one or more newly diagnosed, unprovoked seizures. Two clinical neurophysiologists independently classified the background pattern and the presence of epileptiform discharges or focal non-epileptiform abnormalities of each EEG. The agreement was substantial for the interpretation of the EEG as normal or abnormal (kappa 0.66), almost perfect for the presence of epileptiform discharges (kappa 0.83), substantial for the occurrence of an abnormal background pattern (kappa 0.73), and moderate for the presence of focal non-epileptiform discharges (kappa 0.54). In conclusion, the reliability of the visual interpretation of EEGs in children is almost perfect as regards the presence of epileptiform abnormalities, and moderate to substantial for the presence of other abnormalities.

The diagnostic yield of a second EEG after partial sleep deprivation: a prospective study in children with newly diagnosed seizures.

To assess the diagnostic yield of a repeated EEG (REPEEG) after partial sleep deprivation (SD) in children and adolescents with one or more seizures who previously had had a standard EEG (STDEEG) without epileptiform abnormalities (EAs). In the literature, 32-75% of such REPEEGs after SD were reported to show EA. In a prospective, multicentred study, we selected children aged 1 month to 16 years with one or more idiopathic or remote symptomatic newly diagnosed seizures. A REPEEG was recorded in children without EAs in their STDEEG. Of 552 children and adolescents who entered the study, 243 (44%) had a STDEEG without EAs. In 177 (73% of eligible children), REPEEGs were recorded after SD. We found EAs in 61 (34.5%) REPEEGs and new nonepileptiform abnormalities in five (1%). In 552 children in the total cohort, the REPEEG thus added 11% with EAs to the 56% with EAs in the STDEEG. Of REPEEGs, 81% included sleep compared with 20% of STDEEGs. In about half the REPEEGs, EAs occurred during sleep only. One child had tonic-clonic seizures probably related to the SD. One third of REPEEGs yielded new diagnostic information. Partial, age-dependent SD was highly effective in inducing sleep, which is important because in many cases EAs were found only during EEG recording in sleep. The procedure was safe and convenient.

Electroencephalography as a Localizing Tool

1. The immature brain is not a miniature adult brain, and adult-epilepsy concepts and protocols do not necessarily apply to infant-epilepsy surgery. 2. The epileptogenic zone is conceptually, and often also topographically, distinct from both the ictal-onset zone and the irritative zone (interictal spiking). This distinction is especially important in pediatric evaluations. 3. In partial seizure disorders, the best strategy at present for demarcating the epileptogenic zone seems to be a weighted confluence of an epileptogenic lesion (if present), the ictal-onset zone, and the irritative zone. If these zones are not perfectly convergent (as is usually the case), they are given relative weights according to individual patient factors and variations in philosophy among epilepsy centers. This approach leaves much to be desired, especially in extratemporal nonlesional cases; it is hoped that future research will identify more reliable interictal markers for the epileptogenic zone. 4. Some cases of catastrophic generalized epilepsies or multifocal independent partial seizures are due to localized pathology (ZCA) and respond to resection of the offending lesion (often multilobar or hemispheric). In this context, the epileptogenic zone is coincident with the epileptogenic lesion and is best demarcated by the confluence of interictal indicators of structural pathology. The most reliable localizing aspects of the EEG are, therefore, interictal nonepileptiform abnormalities traditionally associated with structural lesions.

Intracerebral recordings: organization of the human epileptogenic region.

Intracranial recordings from patients with medically refractory partial epilepsy have demonstrated that the concept of a discrete epileptic focus, as derived from experiments with animals, does not exist in this human condition. Furthermore, an EEG spike focus, as defined by electroencephalographers, does not faithfully identify the site of ictal onset. Rather, the boundaries of an epileptogenic region, which is necessary and sufficient for generation of habitual spontaneous seizures, must be approximated by knowledge of the spatial distribution of interictal spike discharges, and ictal onset, as well as the location of an epileptogenic lesion demonstrated by structural imaging, and the location and extent of nonepileptic focal functional deficits. Delineation of an epileptogenic region is confounded by the fact that epileptically and nonepileptically abnormal brain tissue also exists beyond this area and may even involve the contralateral hemisphere. Investigations of epileptic patients during the course of surgical treatment have led to an improved understanding of the persistent neuronal disturbances that predispose to the generation of spontaneous seizures, as well as disturbances in adjacent and distant tissues that receive ictal propagation and demonstrate interictal epileptiform and nonepileptiform abnormalities. The results of such studies will greatly enhance our ability to interpret scalp-recorded EEG transients and to devise better, noninvasive, diagnostic tools to accurately define the epileptogenic region.

ERRATA

In "Autistic and Dysphasic Children. II: Epilepsy" (Pediatrics. 1991;88:1219-1225), by Tuchman et al, the sentence beginning on line 6 of the second paragraph under "Electroencephalographic Data" (p 1222) should read: "In the autistic group with EEG data and without epilepsy (N = 141), 25 (18%) had an abnormal EEC including 11 children (8%) with epileptiform and 14 children (10%) with nonepileptiform abnormalities." Under "Regression of Language and Behavior and Epilepsy" (p 1222), the sentence beginning on line 3 of the first paragraph should read: "Epilepsy was present in 20% (20 of 100) of autistic and 36% (4 of 11) of dysphasic patients with regression."

Neuropsychological correlates of the electroencephalogram in epileptics: III. Generalized nonepileptiform abnormalities.

A broad battery of neuropsychological tests was given to 111 adult epileptics treated primarily with phenytoin. The patients were grouped according to degree of generalized nonepileptiform EEG abnormalities in waking EEGs. In patients classified as None/Mild, Moderate, or Marked in terms of these EEG abnormalities performances on 15 neuropsychological measures were contrasted. Statistically significant differences across the groups were found on 13 of the 15 neuropsychological variables. An orderly decrease in performance occurred as a correlate of an increase in EEG abnormalities. These results were compared to a similar analysis of the correlates of topographic distribution of epileptiform discharges (Absent, Focal, Generalized). It was discovered that degrees of generalized slow wave EEG abnormalities were more potent in differentiating levels of neuropsychological performance than epileptiform discharges. Nevertheless, when discharges and degree of nonepileptiform abnormalities were simultaneously considered, a maximal level of correlation was reached.