EEG Variables Research Articles

Magnetic resonance imaging adds prognostic value to EEG after pediatric cardiac arrest

AimTo investigate how combined electrographic and radiologic data inform outcomes in children after cardiac arrest. MethodsRetrospective observational study of children admitted to the pediatric intensive care unit (PICU) of a tertiary children’s hospital with diagnosis of cardiac arrest from 2009 to 2016. The first 20 min of electroencephalogram (EEG) background was blindly scored. Presence and location of magnetic resonance imaging (MRI) diffusion-weighted image (DWI) abnormalities were correlated with T2-weighted signal. Outcomes were categorized using Pediatric Cerebral Performance Category (PCPC) scores at hospital discharge, with “poor outcome” reflecting a PCPC score of 4–6. Logistic regression models examined the association of EEG and MRI variables with outcome. Results41 children met inclusion criteria and had both post-arrest EEG monitoring within 72 hours after ROSC and brain MRI performed within 8 days. Among the 19 children with poor outcome, 10 children did not survive to discharge. Severely abnormal EEG background (p < 0.0001) and any diffusion restriction (p < 0.0001) were associated with poor outcome. The area under the ROC curve (AUC) for identifying outcome based on EEG background alone was 0.86, which improved to 0.94 with combined EEG and MRI data (p = 0.02). ConclusionDiffusion abnormalities on MRI within 8 days after ROSC add to the prognostic value of EEG background in children surviving cardiac arrest.

Robust biometric system using session invariant multimodal EEG and keystroke dynamics by the ensemble of self-ONNs

Harnessing the inherent anti-spoofing quality from electroencephalogram (EEG) signals has become a potential field of research in recent years. Although several studies have been conducted, still there are some vital challenges present in the deployment of EEG-based biometrics, which is stable and capable of handling the real-world scenario. One of the key challenges is the large signal variability of EEG when recorded on different days or sessions which impedes the performance of biometric systems significantly. To address this issue, a session invariant multimodal Self-organized Operational Neural Network (Self-ONN) based ensemble model combining EEG and keystroke dynamics is proposed in this paper. Our model is tested successfully on a large number of sessions (10 recording days) with many challenging noisy and variable environments for the identification and authentication tasks. In most of the previous studies, training and testing were performed either over a single recording session (same day) only or without ensuring appropriate splitting of the data on multiple recording days. Unlike those studies, in our work, we have rigorously split the data so that train and test sets do not share the data of the same recording day. The proposed multimodal Self-ONN based ensemble model has achieved identification accuracy of 98% in rigorous validation cases and outperformed the equivalent ensemble of deep CNN models. A novel Self-ONN Siamese network has also been proposed to measure the similarity of templates during the authentication task instead of the commonly used simple distance measure techniques. The multimodal Siamese network reduces the Equal Error Rate (EER) to 1.56% in rigorous authentication. The obtained results indicate that the proposed multimodal Self-ONN model can automatically extract session invariant unique non-linear features to identify and authenticate users with high accuracy.

Variability of EEG electrode positions and their underlying brain regions: visualizing gel artifacts from a simultaneous EEG-fMRI dataset.

IntroductionWe investigated the between‐subject variability of EEG (electroencephalography) electrode placement from a simultaneously recorded EEG‐fMRI (functional magnetic resonance imaging) dataset.MethodsNeuro‐navigation software was used to localize electrode positions, made possible by the gel artifacts present in the structural magnetic resonance images. To assess variation in the brain regions directly underneath electrodes we used MNI coordinates, their associated Brodmann areas, and labels from the Harvard‐Oxford Cortical Atlas. We outline this relatively simple pipeline with accompanying analysis code.ResultsIn a sample of 20 participants, the mean standard deviation of electrode placement was 3.94 mm in x, 5.55 mm in y, and 7.17 mm in z, with the largest variation in parietal and occipital electrodes. In addition, the brain regions covered by electrode pairs were not always consistent; for example, the mean location of electrode PO7 was mapped to BA18 (secondary visual cortex), whereas PO8 was closer to BA19 (visual association cortex). Further, electrode C1 was mapped to BA4 (primary motor cortex), whereas C2 was closer to BA6 (premotor cortex).ConclusionsOverall, the results emphasize the variation in electrode positioning that can be found even in a fixed cap. This may be particularly important to consider when using EEG positioning systems to inform non‐invasive neurostimulation.

Nonlinear Analysis of Electroencephalogram Variability as a Measure of the Depth of Anesthesia

Electroencephalogram (EEG) has been widely used to measure the effect of anesthetics on the central nervous system. However, EEG signals are very prone to artifacts. In this article, EEG variability (EEGV) is proposed, and nonlinear models are used to analyze EEGV for measuring the depth of anesthesia (DoA). First, the time intervals between successive local maxima of EEG are extracted. Then, resampling and differentiation are applied to reconstruct the EEGV time series. Sample entropy (SampEn), permutation entropy (PeEn), detrended fluctuation analysis (DFA), and Poincaré plots (PoPs) are used to analyze EEGV. The area under the curve (AUC) and the correlation analysis between proposed measures and conscious level indicated by the bispectral index and expert-labeled data are investigated. A long short-term memory network is further used to combine multiple features for predicting DoA. The results from 59 patients show that these four indices can differentiate awake and unconscious states and track changes in anesthesia states. SampEn, DFA, and PoP derived from EEGV achieve significantly higher AUCs and correlation coefficients than those derived from EEG. The fusion of EEG and EEGV can measure DoA more precisely. In summary, it is the first time that four nonlinear methods are used to analyze the reconstructed EEGV signals instead of traditionally used raw EEG, which provides a robust way to measure DoA.

The Assessment of Effect of Occupational Manganese Exposure on Cognition and Quantitative EEG Variables

Occupational Mn exposure results in extrapyramidal system findings and cognitive impairment. We aimed to evaluate the effect of occupational exposure to Mn on cognition and quantitative EEG (qEEG) data in Mn exposed workers. One hundred seventy-five workers with occupational Mn exposure were included in this study. Pallidal index (PI) values were calculated in T1 weighted sequence of brain magnetic resonance imaging (MRI).The presence of T1 hyperintensity on MRI was evaluated by two independent neurologists. The serum, spot urine, and 24-hour urine samples were measuredMn levels. The total scores of MoCA test and sub-groups were determined. The qEEG measures were analyzed. Right/left PI values in manganese exposed group were statistically higher than in control group (p<0.001).A positive correlation was detected between serum Mn levels and PI values and MRI T1 hyperintensity. There was no correlation between PI values and MoCA total/subgroup scores or qEEG measures. MoCA total score, abstraction and memory scores were found to be low in workers with T1 hyperintensity. No association between PI values, MoCA total or subtotal scores and qEEG data was determined in Mn exposed workers. When T1 hyperintensity was detected by the clinician on MRI, abstraction and memory were found to be the first cognitive functions affected at that time. These parameters could be used by occupational physicians as a screening test to assess cognition in periodic examinations of welders.

Intra‐individual variability in processed EEG (bispectral index monitor) under general anesthesia predicts postoperative cognitive change after total knee arthroplasty

AbstractBackgroundPrevious research shows 1) clock drawing performance slows for older adults after total knee arthroplasty (TKA; Hizel et al., 2018), and 2) brain‐behavioral associations with intra‐individual variability on an EEG‐derived index during TKA (Hernaiz et al., 2019). The current study expanded upon these findings by examining how preoperative cognition and intraoperative responses to anesthesia may predict postoperative clock drawing performance. We hypothesized greater intraoperative intra‐individual BIS variability would explain a significant portion of variance in four primary latency measures to command and copy condition at 3‐weeks following surgery.MethodThe final prospectively recruited IRB consented participant sample included 54 non‐demented patients age 60+ receiving TKA under general anesthesia. Consistent with Hernaiz et al., (2019), derived 2‐channel EEG variability was studied from tourniquet inflation to tourniquet release, thereby establishing a consistent period of examination. Intra‐individual variability was calculated as the squared deviation from the mean BIS value over the duration of measurement. Clock drawing to command and copy was recorded with digital pen technology and scored with semiautomatic rater‐guided software (Souillard‐Mandar et al., 2016). Outcome latency variables of interest: 1) total completion time in seconds (TCT), 2) time prior to setting the first hand in seconds (pre‐first hand latency; PFHL), 3) seconds after drawing the clock face to next pen stroke (post‐clock face latency; PCFL).ResultAt 3‐weeks, participants took, on average, six seconds longer to complete the drawing compared to baseline performance, and two seconds longer to set the first clock hand. Correcting for baseline performance, separate hierarchical regressions showed intra‐individual EEG‐derived variability explained an additional 4.3% of variance in postoperative command TCT (F(2,51)=28.301, p&lt;.038, R2=.536, ∆R2=.043), such that those with greater intraoperative BIS variability took longer to complete the drawing to command.ConclusionIn non‐demented older adults undergoing TKA, higher intra‐individual BIS variability significantly contributed to total clock drawing time during the command condition. Command total completion time is sensitive to numerous cognitive domains including processing speed, language, working memory, and declarative memory (Dion et al., 2020). Fluctuations during general anesthesia as recorded on an EEG‐derived index may help explain postoperative slowing during clock drawing at 3‐weeks post‐surgery.

Electroencephalogram variability analysis for monitoring depth of anesthesia

Objective. In this paper, a new approach of extracting and measuring the variability in electroencephalogram (EEG) was proposed to assess the depth of anesthesia (DOA) under general anesthesia. Approach. The EEG variability (EEGV) was extracted as a fluctuation in time interval that occurs between two local maxima of EEG. Eight parameters related to EEGV were measured in time and frequency domains, and compared with state-of-the-art DOA estimation parameters, including sample entropy, permutation entropy, median frequency and spectral edge frequency of EEG. The area under the receiver-operator characteristics curve (AUC) and Pearson correlation coefficient were used to validate its performance on 56 patients. Main results. Our proposed EEGV-derived parameters yield significant difference for discriminating between awake and anesthesia stages at a significance level of 0.05, as well as improvement in AUC and correlation coefficient on average, which surpasses the conventional features of EEG in detection accuracy of unconscious state and tracking the level of consciousness. Significance. To sum up, EEGV analysis provides a new perspective in quantifying EEG and corresponding parameters are powerful and promising for monitoring DOA under clinical situations.

Brain fragility among middle-aged and elderly patients from electroencephalogram during induction of anaesthesia.

Brain fragility among middle-aged and elderly patients from electroencephalogram during induction of anaesthesia.

Proposed Pathway for the Utilization of Pediatric Ambulatory EEG.

The clinical utility of pediatric ambulatory-EEG (A-EEG) has been studied for decades, but limited information exists regarding which variables influence its utility. The authors aimed to evaluate clinical/EEG variables that may influence A-EEG yields and to develop a pathway for A-EEG utilization in children. Single-center retrospective review of A-EEGs performed from July 2019 to January 2021 in a tertiary referral center. The primary outcome was whether the A-EEG test successfully answered the referring physician's clinical question or influenced therapy. When it did, the A-EEG test was deemed useful. Clinical and EEG variables were assessed for their ability to predict utility. Further, the literature review generated 10 relevant prior studies whose details were used to generate a pathway for A-EEG utilization in children. One hundred forty-two A-EEG studies were included (mean age 8.8 years, 48% male patients, mean A-EEG duration 33.5 hours). Overall, A-EEG was considered useful in 106 children (75%) but heavily influenced by A-EEG indication. Specifically, it was deemed useful for 94% of patients evaluated for electrical status epilepticus in slow-wave sleep, 92% of those evaluated for interictal/ictal burden, and 63% of those undergoing spell classification. The test indication (P < 0.001), a diagnosis of epilepsy (P = 0.02), and an abnormal routine EEG (P = 0.04) were associated with A-EEG test utility, although the multivariate analysis confirmed the test indication as the only independent outcome predictor of A-EEG. Pediatric A-EEG is extremely useful for evaluating electrical status epilepticus in slow-wave sleep and interictal/ictal burden and is often helpful for spell classification. Among all clinical and EEG variables analyzed, the test indication was the only independent outcome predictor of obtaining a helpful A-EEG.

Using EEG in Resource-Limited Areas: Comparing Qualitative and Quantitative Interpretation Methods in Cerebral Malaria

BackgroundOur goal was to compare the strength of association and predictive ability of qualitative and quantitative electroencephalographic (EEG) factors with the outcomes of death and neurological disability in pediatric cerebral malaria (CM). MethodsWe enrolled children with a clinical diagnosis of CM admitted to Queen Elizabeth Central Hospital (Blantyre, Malawi) between 2012 and 2017. A routine-length EEG was performed within four hours of admission. EEG data were independently interpreted using qualitative and quantitative methods by trained pediatric neurophysiologists. EEG interpreters were unaware of patient discharge outcome. ResultsEEG tracings from 194 patients were reviewed. Multivariate modeling revealed several qualitative and quantitative EEG variables that were independently associated with outcomes. Quantitative methods modeled on mortality had better goodness of fit than qualitative ones. When modeled on neurological morbidity in survivors, goodness of fit was better for qualitative methods. When the probabilities of an adverse outcome were calculated using multivariate regression coefficients, only the model of quantitative EEG variables regressed on the neurological sequelae outcome showed clear separation between outcome groups. ConclusionsMultiple qualitative and quantitative EEG factors are associated with outcomes in pediatric CM. It may be possible to use quantitative EEG factors to create automated methods of study interpretation that have similar predictive abilities for outcomes as human-based interpreters, a rare resource in many malaria-endemic areas. Our results provide a proof-of-concept starting point for the development of quantitative EEG interpretation and prediction methodologies useful in resource-limited settings.

Subject-Specific Cognitive Workload Classification Using EEG-Based Functional Connectivity and Deep Learning.

Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other real-time and high-risk situations. Neuroimaging techniques have long been used for estimating cognitive workload. Given the portability, cost-effectiveness and high time-resolution of EEG as compared to fMRI and other neuroimaging modalities, an efficient method of estimating an individual’s workload using EEG is of paramount importance. Multiple cognitive, psychiatric and behavioral phenotypes have already been known to be linked with “functional connectivity”, i.e., correlations between different brain regions. In this work, we explored the possibility of using different model-free functional connectivity metrics along with deep learning in order to efficiently classify the cognitive workload of the participants. To this end, 64-channel EEG data of 19 participants were collected while they were doing the traditional n-back task. These data (after pre-processing) were used to extract the functional connectivity features, namely Phase Transfer Entropy (PTE), Mutual Information (MI) and Phase Locking Value (PLV). These three were chosen to do a comprehensive comparison of directed and non-directed model-free functional connectivity metrics (allows faster computations). Using these features, three deep learning classifiers, namely CNN, LSTM and Conv-LSTM were used for classifying the cognitive workload as low (1-back), medium (2-back) or high (3-back). With the high inter-subject variability in EEG and cognitive workload and recent research highlighting that EEG-based functional connectivity metrics are subject-specific, subject-specific classifiers were used. Results show the state-of-the-art multi-class classification accuracy with the combination of MI with CNN at 80.87%, followed by the combination of PLV with CNN (at 75.88%) and MI with LSTM (at 71.87%). The highest subject specific performance was achieved by the combinations of PLV with Conv-LSTM, and PLV with CNN with an accuracy of 97.92%, followed by the combination of MI with CNN (at 95.83%) and MI with Conv-LSTM (at 93.75%). The results highlight the efficacy of the combination of EEG-based model-free functional connectivity metrics and deep learning in order to classify cognitive workload. The work can further be extended to explore the possibility of classifying cognitive workload in real-time, dynamic and complex real-world scenarios.

Can occurrence of convulsive status epilepticus during video telemetry be predicted? Observations from an epilepsy monitoring unit

PurposeThe objective of the study was to evaluate the frequency, clinical, and video-electroencephalographic (VEEG) predictors of convlusive status epilepticus (CSE) in the epilepsy-monitoring unit (EMU). MethodsThe data of all patients who had CSE in our EMU between 2008 and 2017 were reviewed. For each case, two age- and diagnosis-matched subjects who underwent VEEG and did not develop CSE were taken as internal controls. Electro-clinical data of both the groups were compared. Predictors of CSE were assessed using logistic regression analysis. ResultsOut of 11,188 video-telemetries were conducted between 2008-17, forty-three events of CSE (0.38%) were recorded. On comparisons with 86 internal controls no differences were apparent on prevalence of cognitive impairment, structural lesion, number of baseline anti-seizure medications (ASM), ASM taper schedule, ictal patterns, and duration of VEEG monitoring. Inter-ictal rhythmic periodic patterns had significantly higher prevalence in cases (p = 0.028). Logistic regression analysis revealed that odds of CSE were higher with past history of SE [p = 0.008; adjusted odds ratio (OR) = 5.48 (confidence intervals {CI} 1.55–19.28)] and in presence of rhythmic spike and wave discharges [p = 0.016, OR = 33.518(CI = 1.93–581.4)]; the odds were lower if the first two seizures recorded did not evolve into CSE [p = 0.009, OR = 0.247 (CI = 0.08–0.70)] and if there was prior history of daily seizures [p = 0.02, OR = 0.250 (CI = 0.07–0.84)]. ConclusionCSE is a rare yet important adverse event in EMU. Clinical predictors are more relevant in comparison to EEG variables. Extent of ASM withdrawal may not directly account for occurrence of CSE; factors inherent to a patient’s epilepsy are deterministic.

Auditory Oddball Responses Across the Schizophrenia-Bipolar Spectrum and Their Relationship to Cognitive and Clinical Features.

Neural activations during auditory oddball tasks may be endophenotypes for psychosis and bipolar disorder. The authors investigated oddball neural deviations that discriminate multiple diagnostic groups across the schizophrenia-bipolar spectrum (schizophrenia, schizoaffective disorder, psychotic bipolar disorder, and nonpsychotic bipolar disorder) and clarified their relationship to clinical and cognitive features. Auditory oddball responses to standard and target tones from 64 sensor EEG recordings were compared across patients with psychosis (total N=597; schizophrenia, N=225; schizoaffective disorder, N=201; bipolar disorder with psychosis, N=171), patients with bipolar disorder without psychosis (N=66), and healthy comparison subjects (N=415) from the second iteration of the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP2) study. EEG activity was analyzed in voltage and in the time-frequency domain (low, beta, and gamma bands). Event-related potentials (ERPs) were compared with those from an independent sample collected during the first iteration of B-SNIP (B-SNIP1; healthy subjects, N=211; psychosis group, N=526) to establish the repeatability of complex oddball ERPs across multiple psychosis syndromes (r values >0.94 between B-SNIP1 and B-SNIP2). Twenty-six EEG features differentiated the groups; they were used in discriminant and correlational analyses. EEG variables from the N100, P300, and low-frequency ranges separated the groups along a diagnostic continuum from healthy to bipolar disorder with psychosis/bipolar disorder without psychosis to schizoaffective disorder/schizophrenia and were strongly related to general cognitive function (r=0.91). P50 responses to standard trials and early beta/gamma frequency responses separated the bipolar disorder without psychosis group from the bipolar disorder with psychosis group. P200, N200, and late beta/gamma frequency responses separated the two bipolar disorder groups from the other groups. Neural deviations during auditory processing are related to psychosis history and bipolar disorder. There is a powerful transdiagnostic relationship between severity of these neural deviations and general cognitive performance. These results have implications for understanding the neurobiology of clinical syndromes across the schizophrenia-bipolar spectrum that may have an impact on future biomarker research.

Identifying Individuals With Mild Cognitive Impairment Using Working Memory-Induced Intra-Subject Variability of Resting-State EEGs.

Individuals with mild cognitive impairment (MCI) are at high risk of developing into dementia (e. g., Alzheimer's disease, AD). A reliable and effective approach for early detection of MCI has become a critical challenge. Although compared with other costly or risky lab tests, electroencephalogram (EEG) seems to be an ideal alternative measure for early detection of MCI, searching for valid EEG features for classification between healthy controls (HCs) and individuals with MCI remains to be largely unexplored. Here, we design a novel feature extraction framework and propose that the spectral-power-based task-induced intra-subject variability extracted by this framework can be an encouraging candidate EEG feature for the early detection of MCI. In this framework, we extracted the task-induced intra-subject spectral power variability of resting-state EEGs (as measured by a between-run similarity) before and after participants performing cognitively exhausted working memory tasks as the candidate feature. The results from 74 participants (23 individuals with AD, 24 individuals with MCI, 27 HC) showed that the between-run similarity over the frontal and central scalp regions in the HC group is higher than that in the AD or MCI group. Furthermore, using a feature selection scheme and a support vector machine (SVM) classifier, the between-run similarity showed encouraging leave-one-participant-out cross-validation (LOPO-CV) classification performance for the classification between the MCI and HC (80.39%) groups and between the AD vs. HC groups (78%), and its classification performance is superior to other widely-used features such as spectral powers, coherence, and the complexity estimated by Katz's method extracted from single-run resting-state EEGs (a common approach in previous studies). The results based on LOPO-CV, therefore, suggest that the spectral-power-based task-induced intra-subject EEG variability extracted by the proposed feature extraction framework has the potential to serve as a neurophysiological feature for the early detection of MCI in individuals.

Recent developments, current challenges, and future directions in electrophysiological approaches to studying intelligence

EEG studies represent an important sub-discipline in the field of intelligence research and have significant potential to advance the theoretical understanding and practical applications of the construct. This commentary reviews key themes and major developments in the field from the last several decades, and outlines open questions and future directions for the next phase of research. Two main areas of progress in recent years relate to (1) improvements in study design and psychometric approaches, and (2) increased integration with cognitive psychology and neuroscience. In turn, these advances have clarified several themes and pressing issues. These include: The need to establish the replicability and effect sizes of key effects, the need to explicitly attend to the distinction between trait- and task-related sources of variance in correlations between intelligence and EEG variables, the need to systematically identify and test moderators of those relationships, the need for greater use of formal modeling at the level of measurement and theory, and the need for continued integration of theoretical advances from related disciplines. We argue that an increased focus on these issues can yield rapid progress in this area over the coming years. The commentary concludes with suggestions for both immediate priorities and long-term directions in basic and applied EEG research on intelligence.

Improving the prediction of epilepsy surgery outcomes using basic scalp EEG findings.

This study aims to evaluate the role of scalp electroencephalography (EEG; ictal and interictal patterns) in predicting resective epilepsy surgery outcomes. We use the data to further develop a nomogram to predict seizure freedom. We retrospectively reviewed the scalp EEG findings and clinical data of patients who underwent surgical resection at three epilepsy centers. Using both EEG and clinical variables categorized into 13 isolated candidate predictors and 6 interaction terms, we built a multivariable Cox proportional hazards model to predict seizure freedom 2years after surgery. Harrell's step-down procedure was used to sequentially eliminate the least-informative variables from the model until the change in the concordance index (c-index) with variable removal was less than 0.01. We created a separate model using only clinical variables. Discrimination of the two models was compared to evaluate the role of scalp EEG in seizure-freedom prediction. Four hundred seventy patient records were analyzed. Following internal validation, the full Clinical+EEG model achieved an optimism-corrected c-index of 0.65, whereas the c-index of the model without EEG data was 0.59. The presence of focal to bilateral tonic-clonic seizures (FBTCS), high preoperative seizure frequency, absence of hippocampal sclerosis, and presence of nonlocalizable seizures predicted worse outcome. The presence of FBTCS had the largest impact for predicting outcome. The analysis of the models' interactions showed that in patients with unilateral interictal epileptiform discharges (IEDs), temporal lobe surgery cases had a better outcome. In cases with bilateral IEDs, abnormal magnetic resonance imaging (MRI) predicted worse outcomes, and in cases without IEDs, patients with extratemporal epilepsy and abnormal MRI had better outcomes. This study highlights the value of scalp EEG, particularly the significance of IEDs, in predicting surgical outcome. The nomogram delivers an individualized prediction of postoperative outcome, and provides a unique assessment of the relationship between the outcome and preoperative findings.

Reconsidering the role of selective sodium channel blockers in genetic generalized epilepsy.

Selective sodium channel blockers (SSCBs) have a limited use in genetic generalized epilepsy (GGE), due to their well-known risk of seizure worsening. Although their therapeutic potential in GGE has been suggested by recent evidence, electro-clinical data supporting their prescription are lacking. We aimed to investigate SSCB safety and effectiveness in a GGE cohort. Subjects who received SSCBs and had ≥5-year follow-up were enrolled. Lamotrigine was excluded from analysis due to its broader pharmacodynamic spectrum and its better-documented efficacy in GGE. Fifty-six patients (median follow-up 28.5years) were included. The most used SSCB was carbamazepine in 40subjects. At the last medical observation, only 9subjects were still receiving SSCBs. The occurrence of generalized polyspike-wave discharges (GPSWDs) predicted reduced SSCB retention in Cox multivariate analysis. A seizure reduction ≥50% occurred in 53.5% (30/56) of subjects when considering all seizure types; however, the proportion of responders increased to 67.9% when considering only generalized tonic-clonic seizures (GTCS). GPSWDs were significantly associated with a reduced response rate, whereas GGE with GTCS only syndrome with a better outcome. The switch from SSCBs to antiseizure medications licensed for GGE improved seizure control in 65% of patients. Seizure worsening was reported in 5/56 patients; juvenile myoclonic epilepsy and a family history of epilepsy were significantly associated with seizure aggravation. SSCBs appeared effective on GTCS, but epilepsy aggravation and unsatisfactory control of other seizure types were not uncommon. Our study contributes to identifying new clinical and EEG variables associated with SSCB effectiveness and safety which may help neurologists in patients' management.

EEG spindles integrity in critical care adults. Analysis of a randomized trial.

ObjectivesOccurrence of EEG spindles has been recently associated with favorable outcome in ICU patients. Available data mostly rely on relatively small patients' samples, particular etiologies, and limited variables ascertainment. We aimed to expand previous findings on a larger dataset, to identify clinical and EEG patterns correlated with spindle occurrence, and explore its prognostic implications.MethodsRetrospective observational study of prospectively collected data from a randomized trial (CERTA, NCT03129438) assessing the relationship of continuous (cEEG) versus repeated routine EEG (rEEG) with outcome in adults with acute consciousness impairment. Spindles were prospectively assessed visually as 12‐16Hz activity on fronto‐central midline regions, at any time during EEG interventions. Uni‐ and multivariable analyses explored correlations between spindles occurrence, clinical and EEG variables, and outcome (modified Rankin Scale, mRS; mortality) at 6 months.ResultsAmong the analyzed 364 patients, spindles were independently associated with EEG background reactivity (OR 13.2, 95% CI: 3.11–56.26), and cEEG recording (OR 4.35, 95% CI: 2.5 – 7.69). In the cEEG subgroup (n=182), 33.5% had spindles. They had better FOUR scores (p=0.004), fewer seizures or status epilepticus (p=0.02), and lower mRS (p=0.02). Mortality was reduced (p=0.002), and independently inversely associated with spindle occurrence (OR 0.50, CI 95% 0.25–0.99) and increased EEG background continuity (OR 0.16, 95% CI: 0.07 – 0.41).ConclusionsBesides confirming that spindle activity occurs in up to one third of acutely ill patients and is associated with better outcome, this study shows that cEEG has a higher yield than rEEG in identifying them. Furthermore, it unravels associations with several clinical and EEG features in this clinical setting.

Intraoperative neurophysiologic monitoring during aortic arch surgery

ObjectiveTo evaluate the ability of intraoperative neurophysiologic monitoring (IONM) during aortic arch reconstruction with hypothermic circulatory arrest (HCA) to predict early (<48 hours) adverse neurologic events (ANE; stroke or transient ischemic attack) and operative mortality. MethodsThis was an observational study of aortic arch surgeries requiring HCA from 2010 to 2018. Patients were monitored with electroencephalogram (EEG) and somatosensory evoked potentials (SSEP). Baseline characteristics and postoperative outcomes were compared according to presence or absence of IONM changes, which were defined as any acute variation in SSEP or EEG, compared with baseline. Multivariable logistic regression analysis was used to assess the association of IONM changes with operative mortality and early ANE. ResultsA total of 563 patients underwent aortic arch reconstruction with HCA and IONM. Of these, 119 (21.1%) patients had an IONM change, whereas 444 (78.9%) did not. Patients with IONM changes had increased operative mortality (22.7% vs 4.3%) and increased early ANE (10.9% vs 2.9%). In multivariable analysis, SSEP changes were correlated with early ANE (odds ratio [OR], 4.68; 95% confidence interval [CI], 1.51-14.56; P = .008), whereas EEG changes were not (P = .532). Permanent SSEP changes were correlated with early ANE (OR, 4.56; 95% CI, 1.51-13.77; P = .007), whereas temperature-related SSEP changes were not (P = .997). Finally, any IONM change (either SSEP or EEG) was correlated with operative mortality (OR, 5.82; 95% CI, 2.72-12.49; P < .001). ConclusionsAbnormal IONM events during aortic arch reconstruction with HCA portend worse neurologic outcomes and operative mortality and have a negative predictive value of 97.1%. SSEP might be more sensitive than EEG for predicting early ANE, especially when SSEP changes are permanent.

Technology During Pandemics: Ecological Momentary Assessment as an Assessment Tool for Real Time Monitoring

Background: Ecological Momentary Assessment (EMA), a term coined in 1994 by Stone and Schiffman, is a modality by which participants can record their symptoms, triggers and thoughts in real time. This data is recorded electronically through the use of smartphones, tablets, and smartwatches as subjects live their daily lives. EMA can be thought of as an electronic diary by which a subject can respond to alerts by entering their observations several times throughout the day. These assessments involve different discrete methodologies such as interpersonal interaction diaries, ambulatory physiological monitoring, and collection of medication compliance data. EMA was developed to counteract the recall bias and inter-evaluator discrepancies that can occur during regularly scheduled outpatient appointments. Methods: A literature review was done using EMA as the search criteria. Results: Davidson et al discussed the use of EMA in suicidality through the use of paper journals, personal digital assistants and cellphones. The paper outlined two sampling schemes; event-based sampling which asks participants to respond to questions each time they experienced a behavior and time-based sampling which asks for responses at a set period of time. Bell et al, 2017 reviewed the application of EMA in treatment of psychotic disorders and reviewed current literature. Schueller et al, 2017 analyzed the use of EMA for depression and anxiety providing mechanisms, study designs and therapeutic measures that can be incorporated for design of an EMA for depression and anxiety. Smith et al 2019 reviewed EMA in the assessment of eating disorders (ED) by looking at realms of type, frequency and temporal sequencing of ED symptoms in natural environment. The use of EMA for child and adolescent patients using mobile based utilization was researched by Heron et al., 2017. Using these modalities children and adolescents may have improved reporting of pain, treatment adherence, sleep and disease symptoms. Raugh et al 2019 studied EMA for psychophysiological assessments. EMA was used for monitoring of EKG, Blood pressure, EEG, electroculography, electromyography and other variables. Conclusions: There are multiple advantages and disadvantages to EMA. Advantages include participants avoiding recall bias and providing real time assessments. Treatment is not dependent on respondent’s bias and immediate interventions can be enacted. EMA also allows a remote alert to providers and can be used for research. Disadvantages of EMA include it being dependent on equipment, technology and network connectivity. There is a risk of attrition as well as possible redundancy. There is a need for detailed study modeling. EMA requires motivation on the part of the participant to respond and participate regularly. Additionally, encryption is necessary to safeguard such sensitive data. More research is required investigating the benefits of this method.