Prediction of rhythmic and periodic EEG patterns and seizures on continuous EEG with early epileptiform discharges

The Role of Routine Electroencephalography in the Diagnosis of Seizures in Medical Intensive Care Units

Seizures should be diagnosed and treated to ensure optimal health outcomes in critically ill patients admitted in the medical intensive care unit (MICU). Continuous electroencephalography is still infrequently used in the MICU. We investigated the effectiveness of routine EEG (rEEG) in detecting seizures in the MICU. A total of 560 patients admitted to the MICU between October 2018 and March 2023 and who underwent rEEG were reviewed. Seizure-related rEEG constituted 47% of all rEEG studies. Totally, 39% of the patients experienced clinical seizures during hospitalization; among them, 48% experienced the seizure, and 13% experienced their first seizure after undergoing an rEEG study. Seventy-seven percent of the patients had unfavorable short-term outcomes. Patients with cardiovascular diseases were the most likely to have the suppression/burst suppression (SBS) EEG pattern and the highest mortality rate. The rhythmic and periodic patterns (RPPs) and electrographic seizure (ESz) EEG pattern were associated with seizures within 24 h after rEEG, which was also related to unfavorable outcomes. Significant predictors of death were age > 59 years, the male gender, the presence of cardiovascular disease, a Glasgow Coma Scale score ≤ 5, and the SBS EEG pattern, with a predictive performance of 0.737 for death. rEEG can help identify patients at higher risk of seizures. We recommend repeated rEEG in patients with ESz or RPP EEG patterns to enable a more effective monitoring of seizure activities.

Epileptic seizure focus detection from interictal electroencephalogram: a survey

Electroencephalogram (EEG) is one of most effective clinical diagnosis modalities for the localization of epileptic focus. Most current AI solutions use this modality to analyze the EEG signals in an automated manner to identify the epileptic seizure focus. To develop AI system for identifying the epileptic focus, there are many recently-published AI solutions based on biomarkers or statistic features that utilize interictal EEGs. In this review, we survey these solutions and find that they can be divided into three main categories: (i) those that use of biomarkers in EEG signals, including high-frequency oscillation, phase-amplitude coupling, and interictal epileptiform discharges, (ii) others that utilize feature-extraction methods, and (iii) solutions based upon neural networks (an end-to-end approach). We provide a detailed description of seizure focus with clinical diagnosis methods, a summary of the public datasets that seek to reduce the research gap in epilepsy, recent novel performance evaluation criteria used to evaluate the AI systems, and guidelines on when and how to use them. This review also suggests a number of future research challenges that must be overcome in order to design more efficient computer-aided solutions to epilepsy focus detection.

Incidence, risk factors, and outcomes in electroencephalographic seizures after mechanical circulatory support: A systematic review and meta-analysis

Purpose

To estimate the overall incidence, risk factors, and clinical outcomes of electroencephalographic (EEG) seizures for adults and children after mechanical circulatory support (MCS).

Method and measurements

This systematic review and meta-analysis were carried out in accordance with the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidance document. MEDLINE EMBASE and CENTRAL were investigated for relevant studies. The related information was retrieved by two independent reviewers and all analyses were conducted by STATA (version 16.0; Stata Corporation, College Station, TX, United States).

Result

Sixty studies including 36,191 adult and 55,475 pediatric patients with MCS were enrolled for evaluation. The study showed that the overall incidence of EEG seizures in adults was 2% (95%CI: 1–3%), in which 1% (95%CI: 1–2%) after cardiopulmonary bypass (CPB), and 3% (95%CI: 1–6%) after extracorporeal membrane oxygenation (ECMO). For pediatrics patients, the incidence of EEG seizures was 12% (95%CI: 11–14%), among which 12% (9–15%) after CPB and 13% (11–15%) after ECMO. The major risk factors of EEG seizures after MCS in adults were redo surgery (coefficient = 0.0436, p = 0.044), and COPD (coefficient = 0.0749, p = 0.069). In addition, the gestational week of CPB (coefficient = 0.0544, p = 0.080) and respiratory failure of ECMO (coefficient = –0.262, p = 0.019) were also indicated to be associated with EEG seizures in pediatrics.

Conclusion

EEG seizures after MCS were more common in pediatrics than in adults. In addition, the incidence of EEG seizure after ECMO was higher than CPB both in adults and children. It is expected that appropriate measures should be taken to control modifiable risk factors, thus improving the prognosis and increasing the long-term survival rate of MCS patients.

Systematic Review Registration

[https://www.crd.york.ac.uk/prospero], identifier [CRD42021287288].

Epidemiology, management and outcome of status epilepticus in adults: single-center Italian survey

The official variations of status epilepticus (SE) International League Against Epilepsy (ILAE, 2015) diagnostic criteria and the non-convulsive SE (NCSE) Salzburg Consensus Criteria (2013), impose the collection of updated population-based epidemiological Italian data. In this study, we aimed at evaluating (a) the frequency of SE in our hospital adopting the new ILAE 2015 SE diagnostic criteria and NCSE Salzburg Consensus Criteria, (b) the frequency of adherence to current treatment guidelines for SE and their relationship with patients' outcome, and (c) reliability of standardized prognostic scales (Status Epilepticus Severity Score-STESS-and modified STESS) for short-term outcome prediction in the setting of the newest diagnostic criteria for SE and NCSE. Detailed clinical and electrophysiological data collected in a 1-year retrospective hospital-based single-center survey on SE at Parma Hospital, Northern Italy are provided. Non-adherence to current treatment guidelines was recorded in around 50% cases, but no relation to outcome was appreciated. Mortality in our cohort increased from 30 to 50% when follow-up was extended to 30 days. STESS score was strongly correlated with short-term mortality risk (OR 18.9, 2.2-163.5, CI), and we confirm its role as easy-to-use tool for outcome evaluation also when the new ILAE diagnostic SE criteria are applied.

Seizures and epilepsy after intracerebral hemorrhage: an update

Seizures are common after intracerebral hemorrhage, occurring in 6-15% of the patients, mostly in the first 72 h. Their incidence reaches 30% when subclinical or non-convulsive seizures are diagnosed by continuous electroencephalogram. Several risk factors for seizures have been described including cortical location of intracerebral hemorrhage, presence of intraventricular hemorrhage, total hemorrhage volume, and history of alcohol abuse. Seizures after intracerebral hemorrhage may theoretically be harmful as they can lead to sudden blood pressure fluctuations, increased intracranial pressure, and neuronal injury due to increased metabolic demand. Some recent studies suggest that acute symptomatic seizures (occurring within 7 days of stroke) are associated with worse functional outcome and increased risk of death despite accounting for other known prognostic factors such as age and baseline hemorrhage volume. However, the impact of seizures on prognosis is still debated and it remains unclear if treating or preventing seizures might lead to improved clinical outcome. Thus, the currently available scientific evidence does not support the routine use of antiseizure medication as primary prevention among patients with intracerebral hemorrhage. Only prospective adequately powered randomized-controlled trials will be able to answer whether seizure prophylaxis in the acute or longer term settings is beneficial or not in patients with intracerebral hemorrhage.

Quantitative Infrared Pupillometry in Nonconvulsive Status Epilepticus

BACKGROUND

Nonconvulsive status epilepticus (NCSE) is a frequent disorder in neurocritical care and diagnosing it can be challenging. NCSE patients often show altered pupil function, but nature and extent may vary. Infrared pupillometry allows detection of subtle changes of pupil function. The neurological pupil index (NPi) is considered a surrogate marker of global pupil function which is supposed to be independent of absolute parameters such as the pupil diameter.

OBJECTIVE

Cross-sectional observational study to assess whether NPi is altered in NCSE.

METHODS

128 consecutive adult emergency patients who had experienced a suspected seizure, have not reached their prior functional level regarding level of consciousness, mental status or focal deficits, had no obvious clinical signs of status epilepticus and had an EEG indication as determined by the treating clinician for exclusion of NCSE were examined by routine EEG and pupillometry. Exclusion criteria were ocular comorbidity (n = 21) and poor EEG quality (n = 4). Pupillometry was performed once directly before the beginning of EEG recording. NCSE diagnosis (no NCSE, possible NCSE and confirmed NCSE) was established according to Salzburg consensus criteria blinded to pupillometry results. Group comparison was performed for right NPi, left NPi, lowest NPi of both sides (minNPi) and the absolute difference of both sides (diffNPi) applying non-parametric testing. In post-hoc analysis, receiver operating characteristics (ROC) of NCSE diagnosis (combined confirmed NCSE and possible NCSE) were performed for minNPi and diffNPi.

RESULTS

From 103 patients included in the final analysis, 5 (4.9%) had confirmed NCSE, 7 (6.8%) had possible NCSE. Right NPi (p = 0.002), left NPi (p < 0.001) and minNPi (p < 0.001) were significantly lower in "confirmed NCSE" and "possible NCSE" compared to "no NCSE"; diffNPi was significantly higher in "confirmed NCSE" and "possible NCSE" compared to "no NCSE" (p < 0.001). There was no significant difference of minNPi and diffNPi between "confirmed NCSE" and "possible NCSE". ROC analysis showed an optimal cut-off of minNPi for NCSE diagnosis of 4.0 (AUC = 0.93, 95% CI 0.86-0.99). Optimal ROC analysis cut-off of diffNPi for NCSE diagnosis was 0.2 (AUC = 0.89, 95% CI 0.80-0.99).

CONCLUSIONS

NPi was significantly reduced and the difference between left and right NPi was significantly higher in confirmed NCSE. An NPi < 4.0 on either side as well as an NPi difference of both sides > 0.2 may be potential indicators of NCSE. Infrared pupillometry may be a helpful diagnostic tool in the assessment of NCSE and should be studied further in larger populations.

Electrographic seizures and ictal-interictal continuum (IIC) patterns in critically ill patients

Critical care long-term continuous electroencephalogram (cEEG) monitoring has expanded dramatically in the last several decades spurned by technological advances in EEG digitalization and several key clinical findings: 1-Seizures are relatively common in the critically ill-large recent observational studies suggest that around 20% of critically ill patients placed on cEEG have seizures. 2-The majority (~75%) of patients who have seizures have exclusively "electrographic seizures", that is, they have no overt ictal clinical signs. Along with the discovery of the unexpectedly high incidence of seizures was the high prevalence of EEG patterns that share some common features with archetypical electrographic seizures but are not uniformly considered to be "ictal". These EEG patterns include lateralized periodic discharges (LPDs) and generalized periodic discharges (GPDs)-patterns that at times exhibit ictal-like behavior and at other times behave more like an interictal finding. Dr. Hirsch and colleagues proposed a conceptual framework to describe this spectrum of patterns called the ictal-interictal continuum (IIC). In the following years, investigators began to answer some of the key pragmatic clinical concerns such as which patients are at risk of seizures and what is the optimal duration of cEEG use. At the same time, investigators have begun probing the core questions for critical care EEG-what is the underlying pathophysiology of these patterns, at what point do these patterns cause secondary brain injury, what are the optimal treatment strategies, and how do these patterns affect clinical outcomes such as neurological disability and the development of epilepsy. In this review, we cover recent advancements in both practical concerns regarding cEEG use, current treatment strategies, and review the evidence associating IIC/seizures with poor clinical outcomes.

How and Whom to Monitor for Seizures in an ICU: A Systematic Review and Meta-Analysis

OBJECTIVES

To pool prevalence of nonconvulsive seizure, nonconvulsive status epilepticus, and epileptiform activity detected by different electroencephalography types in critically ills and to compare detection rates among them.

DATA SOURCES

MEDLINE (via PubMed) and SCOPUS (via Scopus) STUDY SELECTION:: Any type of study was eligible if studies were done in adult critically ill, applied any type of electroencephalography, and reported seizure rates. Case reports and case series were excluded.

DATA EXTRACTION

Data were extracted independently by two investigators. Separated pooling of prevalence of nonconvulsive seizure/nonconvulsive status epilepticus/epileptiform activity and odds ratio of detecting outcomes among different types of electroencephalography was performed using random-effect models. This meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and also adhered to the Meta-analyses Of Observational Studies in Epidemiology guidelines. Quality of evidence was assessed with the Newcastle-Ottawa Quality Assessment Scale for observational studies and Cochrane methods for randomized controlled trial studies.

DATA SYNTHESIS

A total of 78 (16,707 patients) and eight studies (4,894 patients) were eligible for pooling prevalence and odds ratios. For patients with mixed cause of admission, the pooled prevalence of nonconvulsive seizure, nonconvulsive status epilepticus, either nonconvulsive seizure or nonconvulsive status epilepticus detected by routine electroencephalography was 3.1%, 6.2%, and 6.3%, respectively. The corresponding prevalence detected by continuous electroencephalography monitoring was 17.9%, 9.1%, and 15.6%, respectively. In addition, the corresponding prevalence was high in post convulsive status epilepticus (33.5%, 20.2%, and 32.9%), CNS infection (23.9%, 18.1%, and 23.9%), and post cardiac arrest (20.0%, 17.3%, and 22.6%). The pooled conditional log odds ratios of nonconvulsive seizure/nonconvulsive status epilepticus detected by continuous electroencephalography versus routine electroencephalography from studies with paired data 2.57 (95% CI, 1.11-5.96) and pooled odds ratios from studies with independent data was 1.57 (95% CI, 1.00-2.47).

CONCLUSIONS

Prevalence of seizures detected by continuous electroencephalography was significantly higher than with routine electroencephalography. Prevalence was particularly high in post convulsive status epilepticus, CNS infection, and post cardiac arrest.

Early Epileptiform Discharges and Clinical Signs Predict Nonconvulsive Status Epilepticus on Continuous EEG

BACKGROUND

Critical care continuous electroencephalography (CCEEG) represents the gold standard for detection of nonconvulsive status epilepticus (NCSE) in neurological critical care patients. It is unclear which findings on short-term routine EEG and which clinical parameters predict NCSE during subsequent CCEEG reliably. The aim of the present study was to assess the prognostic significance of changes within the first 30 min of EEG as well as of clinical parameters for the occurrence of NCSE during subsequent CCEEG.

METHODS

Systematic analysis of the first 30 min and the remaining segments of prospective CCEEG recordings according to the ACNS Standardized Critical Care EEG Terminology and according to recently proposed NCSE criteria as well as review of clinical parameters of 85 consecutive neurological critical care patients. Logistic regression and binary classification tests were used to determine the most useful parameters within the first 30 min of EEG predicting subsequent NCSE.

RESULTS

The presence of early sporadic epileptiform discharges (SED) and early rhythmic or periodic EEG patterns of "ictal-interictal uncertainty" (RPPIIIU) (OR 15.51, 95% CI 2.83-84.84, p = 0.002) and clinical signs of NCS (OR 18.43, 95% CI 2.06-164.62, p = 0.009) predicted NCSE on subsequent CCEEG. Various combinations of early SED, early RPPIIIU, and clinical signs of NCS showed sensitivities of 79-100%, specificities of 49-89%, and negative predictive values of 95-100% regarding the incidence of subsequent NCSE (p < 0.001).

CONCLUSIONS

Early SED and early RPPIIIU within the first 30 min of EEG as well as clinical signs of NCS predict the occurrence of NCSE during subsequent CCEEG with high sensitivity and high negative predictive value and may be useful to select patients who should undergo CCEEG.

Poststroke seizure: optimising its management

Seizure after stroke or poststroke seizure (PSS) is a common and very important complication of stroke. It can be divided into early seizure and late seizure, depending on seizure onset time after the stroke. It has been reported that ischaemic and haemorrhagic stroke accounts for about 11% of all adult epilepsy cases and 45% of epilepsy cases over 60 years of age. However, there are no reliable guidelines in clinical practice regarding most of the fundamental issues of PSS management. In recent years there has been an increased interest in the study of PSS which may give clinical practitioners a better picture of how to optimise PSS management. Studies have indicated two peaks in PSS occurrence—the first day and 6–12 months after a stroke. Haemorrhagic stroke, cortical involvement, severity of initial neurological deficit, younger patients (<65 years of age), family history of seizures and certain genetic factors carry a higher risk of PSS. The use of continuous electroencephalogram has demonstrated significant benefits in capturing interictal or ictal abnormalities, especially in cases of non-convulsive seizures and non-convulsive status epilepticus. Current available data indicated that there was no significant difference in antiepileptic efficacy among most of the antiepileptic drugs (AEDs) in PSS. Levetiracetam and lamotrigine are the most studied newer generation AEDs and have the best drug tolerance. The purpose of this review is to summarise the recent advances in PSS research and focus on the most important practice issues of PSS management.

Continuous Electroencephalography in the Critically Ill: Clinical and Continuous Electroencephalography Markers for Targeted Monitoring

PURPOSE

Electrographic seizures detected by continuous electroencephalography (CEEG) in critically ill patients with altered mental status is becoming increasingly recognized. Data guiding the appropriate selection of patients to be monitored on CEEG are lacking. The aims of this article were to study the incidence of seizures in the critical care setting and to evaluate for clinical predictors to improve the efficiency of CEEG monitoring.

METHODS

Retrospective review of the CEEG and clinical data on 1,123 consecutive patients who had continuous video EEG over a 24-month period.

RESULTS

Seizures were recorded in 215 patients on CEEG monitoring (19.1%). In total, 89.3% of these seizures occurred without clinical signs. Patients who were in a coma were more likely to have EEG seizures (odds ratio, 3.64; 95% confidence interval, 2.23-5.95) compared with those awake. The incidence of seizures was overrepresented in patients with extra-axial tumors (41.9%), multiple sclerosis (35.7%), and intra-axial tumors (33.0%). Lateralized periodic discharges were predictive (odds ratio, 8.27; 95% confidence interval, 5.52-12.46) of seizure occurrence compared with those with no epileptiform patterns. Only generalized periodic discharges with triphasic morphology had no increased odds of seizure (odds ratio, 1.02; 95% confidence interval, 0.24-3.03). When present, electroencephalography seizures were detected within 24 hours in 92% of monitored patients.

CONCLUSIONS

Continuous electroencephalography monitoring in the critical care setting demonstrates a linear increase in seizure incidence with declining mental status. Recognizing clinical conditions and electroencephalography markings may help in the appropriate selection of critically ill patients for CEEG monitoring.

Early EEG predicts poststroke epilepsy

OBJECTIVE

Electroencephalography (EEG) can identify biomarkers of epileptogenesis and ictogenesis. However, few studies have used EEG in the prediction of poststroke seizures. Our primary aim was to evaluate whether early EEG abnormalities can predict poststroke epilepsy.

METHODS

A prospective study of consecutive acute anterior circulation ischemic stroke patients, without previous epileptic seizures, who were admitted to a stroke unit over 24 months and followed for 1 year. All patients underwent standardized clinical and diagnostic assessment during the hospital stay and after discharge. Video-EEG was performed in the first 72 h (first EEG), daily for the first 7 days, in case of neurological deterioration, at discharge, and at 12 months after stroke. The occurrence of epileptic seizures in the first year after stroke (primary outcome) was evaluated clinically and neurophysiologically during the hospital stay and at 12 months. A telephone interview was also performed at 6 months. The primary outcome was the occurrence of at least one unprovoked seizure (poststroke epilepsy). Secondary outcomes were the occurrence of at least one acute symptomatic seizure and (interictal and/or ictal) epileptiform activity on at least one EEG during the hospital stay for acute stroke. The first EEG variables were defined using international criteria/terminology. Bivariate and multivariate analyses with adjustment for age, admission National Institutes of Health Stroke Scale (NIHSS) score, and Alberta Stroke Program Early CT Score (ASPECTS) were performed.

RESULTS

A total of 151 patients were included; 38 patients (25.2%) had an acute symptomatic seizure and 23 (16%) had an unprovoked seizure.The first EEG background activity asymmetry and first EEG with interictal epileptiform activity were independent predictors of poststroke epilepsy during the first year after stroke (P = 0.043 and P = 0.043, respectively). No EEG abnormality independently predicted acute symptomatic seizures. However, the presence of periodic discharges on the first EEG was an independent predictor of epileptiform activity (p = 0.009) during the hospital stay.

SIGNIFICANCE

An early poststroke EEG can predict epilepsy in the first year after stroke, independently from clinical and imaging-based infarct severity.

Applicability of NeuroTrend as a bedside monitor in the neuro ICU

OBJECTIVE

To assess whether ICU caregivers can correctly read and interpret continuous EEG (cEEG) data displayed with the computer algorithm NeuroTrend (NT) with the main attention on seizure detection and determination of sedation depth.

METHODS

120 screenshots of NT (480h of cEEG) were rated by 18 briefly trained nurses and biomedical analysts. Multirater agreements (MRA) as well as interrater agreements (IRA) compared to an expert opinion (EXO) were calculated for items such as pattern type, pattern location, interruption of recording, seizure suspicion, consistency of frequency, seizure tendency and level of sedation.

RESULTS

MRA as well as IRA were almost perfect (80-100%) for interruption of recording, spike-and-waves, rhythmic delta activity and burst suppression. A substantial agreement (60-80%) was found for electrographic seizure patterns, periodic discharges and seizure suspicion. Except for pattern localization (70.83-92.26%), items requiring a precondition and especially those who needed interpretation like consistency of frequency (47.47-79.15%) or level of sedation (41.10%) showed lower agreements.

CONCLUSIONS

The present study demonstrates that NT might be a useful bedside monitor in cases of subclinical seizures. Determination of correct sedation depth by ICU caregivers requires a more detailed training.

SIGNIFICANCE

Computer algorithms may reduce the workload of cEEG analysis in ICU patients.

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50 Hz from raw EEG recordings. Raw EEGs were segmented into 1 s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The non-linear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70 % for normal-pre-ictal, 99.70 % for normal-epileptic and 99.85 % for pre-ictal-epileptic.