Relaxed Alert Electroencephalography Screening for Mild Traumatic Brain Injury in Athletes.

Due to the mildness of initial injury, many athletes with recurrent mild traumatic brain injury (mTBI) are misdiagnosed with other neuropsychiatric illnesses. This study was designed as a proof-of-principle feasibility trial for athletic trainers at a sports facility to generate electroencephalograms (EEGs) from student athletes for discriminating (mTBI) associated EEGs from uninjured ones. A total of 47 EEGs were generated, with 30 athletes recruited at baseline (BL) pre-season, after a concussive injury (IN), and post-season (PS). Outcomes included: 1) visual analyses of EEGs by a neurologist; 2) support vector machine (SVM) classification for inferences about whether particular groups belonged to the three subgroups of BL, IN, or PS; and 3) analyses of EEG synchronies including phase locking value (PLV) computed between pairs of distinct electrodes. All EEGs were visually interpreted as normal. SVM classification showed that BL and IN could be discriminated with 81% accuracy using features of EEG synchronies combined. Frontal inter-hemispheric phase synchronization measured by PLV was significantly lower in the IN group. It is feasible for athletic trainers to record high quality EEGs from student athletes. Also, spatially localized metrics of EEG synchrony can discriminate mTBI associated EEGs from control EEGs.

SARS-CoV-2 alters neural synchronies in the brain with more severe effects in younger individuals.

Coronavirus disease secondary to infection by SARS-CoV-2 (COVID19 or C19) causes respiratory illness, as well as severe neurological symptoms that have not been fully characterized. In a previous study, we developed a computational pipeline for the automated, rapid, high-throughput and objective analysis of electroencephalography (EEG) rhythms. In this retrospective study, we used this pipeline to define the quantitative EEG changes in patients with a PCR-positive diagnosis of C19 (n = 31) in the intensive care unit (ICU) of Cleveland Clinic, compared to a group of age-matched PCR-negative (n = 38) control patients in the same ICU setting. Qualitative assessment of EEG by two independent teams of electroencephalographers confirmed prior reports with regards to the high prevalence of diffuse encephalopathy in C19 patients, although the diagnosis of encephalopathy was inconsistent between teams. Quantitative analysis of EEG showed distinct slowing of brain rhythms in C19 patients compared to control (enhanced delta power and attenuated alpha-beta power). Surprisingly, these C19-related changes in EEG power were more prominent in patients below age 70. Moreover, machine learning algorithms showed consistently higher accuracy in the binary classification of patients as C19 versus control using EEG power for subjects below age 70 compared to older ones, providing further evidence for the more severe impact of SARS-CoV-2 on brain rhythms in younger individuals irrespective of PCR diagnosis or symptomatology, and raising concerns over potential long-term effects of C19 on brain physiology in the adult population and the utility of EEG monitoring in C19 patients.

Focal epilepsy features in a child with Congenital Zika Syndrome.

Zika virus (ZIKV) is a mosquito-borne, single-stranded DNA flavivirus that is teratogenic and neurotropic. Similar to the teratogenic effects of other TORCH infections, ZIKV infection during pregnancy can have an adverse impact on fetal and neonatal development. Epilepsy is detected in 48-96% of children with Congenital Zika Syndrome (CZS) and microcephaly. Early epilepsy surveillance is needed in children with prenatal ZIKV exposure; yet, most ZIKV-endemic regions do not have specialist epilepsy care. Here, we describe the demographic, clinical, imaging, and EEG characteristics of a 2-year-old child with CZS and microcephaly who presented with focal epileptiform activity, suboptimal growth, and severe neurodevelopmental delays. Administration of a brief seizure questionnaire by allied health professionals to the patient's caregiver helped to characterize the child's seizure semiology and differentiate focal from generalized seizure features. A telemedicine EEG interpretation platform provided valuable diagnostic information for the patient's local pediatrician to integrate into her treatment plan. This case illustrates that CZS can present with focal epilepsy features and that a telemedicine approach can be used to bridge the gap between epilepsy specialists and local care providers in resource limited ZIKV-endemic regions to achieve better seizure control in children with CZS.

Epilepsy surveillance in normocephalic children with and without prenatal Zika virus exposure.

Children with Congenital Zika Syndrome and microcephaly are at high risk for epilepsy; however, the risk is unclear in normocephalic children with prenatal Zika virus (ZIKV) exposure [Exposed Children (EC)]. In this prospective cohort study, we performed epilepsy screening in normocephalic EC alongside a parallel group of normocephalic unexposed children [Unexposed Children (UC)]. We compared the incidence rate of epilepsy among EC and UC at one year of life to global incidence rates. Pregnant women were recruited from public health centers during the ZIKV outbreak in Grenada, West Indies and assessed for prior ZIKV infection using a plasmonic-gold platform that measures IgG antibodies in serum. Normocephalic children born to mothers with positive ZIKV results during pregnancy were classified as EC and those born to mothers with negative ZIKV results during and after pregnancy were classified as UC. Epilepsy screening procedures included a pediatric epilepsy screening questionnaire and video electroencephalography (vEEG). vEEG was collected using a multi-channel microEEG® system for a minimum of 20 minutes along with video recording of participant behavior time-locked to the EEG. vEEGs were interpreted independently by two pediatric epileptologists, who were blinded to ZIKV status, via telemedicine platform. Positive screening cases were referred to a local pediatrician for an epilepsy diagnostic evaluation. Epilepsy screens were positive in 2/71 EC (IR: 0.028; 95% CI: 0.003-0.098) and 0/71 UC. In both epilepsy-positive cases, questionnaire responses and interictal vEEGs were consistent with focal, rather than generalized, seizures. Both children met criteria for a clinical diagnosis of epilepsy and good seizure control was achieved with carbamazepine. Our results indicate that epilepsy rates are modestly elevated in EC. Given our small sample size, results should be considered preliminary. They support the use of epilepsy screening procedures in larger epidemiological studies of children with congenital ZIKV exposure, even in the absence of microcephaly, and provide guidance for conducting epilepsy surveillance in resource limited settings.

Improving the ability of ED physicians to identify subclinical/electrographic seizures on EEG after a brief training module.

BACKGROUND: Approximately 5% of emergency department (ED) patients with altered mental status (AMS) have non-convulsive seizures (NCS). Patients with NCS should be diagnosed with EEG as soon as possible to initiate antiepileptic treatment. Since ED physicians encounter such patients first in the ED, they should be familiar with general EEG principles as well as the EEG patterns of NCS/NCSE. We evaluated the utility of a brief training module in enhancing the ED physicians' ability to identify seizures on EEG. METHODS: This was a randomized controlled trial conducted in three academic institutions. A slide presentation was developed describing the basic principles of EEG including EEG recording techniques, followed by characteristics of normal and abnormal patterns, the goal of which was to familiarize the participants with EEG seizure patterns. We enrolled board-certified emergency medicine physicians into the trial. Subjects were randomized to control or intervention groups. Participants allocated to the intervention group received a self-learning training module and were asked to take a quiz of EEG snapshots after reviewing the presentation, while the control group took the quiz without the training. RESULTS: A total of 30 emergency physicians were enrolled (10 per site, with 15 controls and 15 interventions). Participants were 52% male with median years of practice of 9.5 years (3, 14). The percentage of correct answers in the intervention group (65%, 63% and 75%) was significantly different (p = 0.002) from that of control group (50%, 45% and 60%). CONCLUSIONS: A brief self-learning training module improved the ability of emergency physicians in identifying EEG seizure patterns.

Can patients with non-convulsive seizure be identified in the emergency department?

BACKGROUND: Non-convulsive seizure (NCS) is an underdiagnosed, potentially treatable emergency with significant mortality and morbidity. The objective of this study is to examine the characteristics of patients with NCS presenting with altered mental status (AMS) and diagnosed with electroencephalography (EEG), to identify the factors that could increase the pre-test probability of NCS. METHODS: Retrospective study using the data collected prospectively. Inclusion criteria: patients older than 13 years with AMS. Exclusion criteria: (1) immediately correctable AMS (e.g., hypoglycemia, opiod overdose); (2) inability to undergo EEG; (3) hemodynamic instability. Outcomes compared between NCS and non-NCS cases: vital signs, lactate level, gender, witnessed seizure, use of anticonvulsive in the field or in the ED, history of seizure or stroke, head injury, abnormal neurological finding and new abnormal findings on head CT. Data presented as medians and quartiles for categorical and percentages with 95%CI for continuous variables. Univariate analyses were performed with Man-Whitney U and Fisher's Exact tests. A multivariate analysis model was used to test the predictive value of clinical variables in identifying NCS. RESULTS: From 332 patients (median age 66 years, quartiles 50-78), 16 were diagnosed with NCS (5%, 95%CI 3%-8%). Only age was significantly different between the NCS vs. non-NCS groups in both univariate (P=0.032) and multivariate analyses (P=0.016). CONCLUSION: Other than age, no other clinically useful variable could identify patients at high risk of NCS. ED physicians should have a high suspicion for NCS and should order EEG for these patients more liberally.

Can patients with non-convulsive seizure be identified in the emergency department? / 世界急诊医学杂志（英文）

@#BACKGROUND: Non-convulsive seizure (NCS) is an underdiagnosed, potentially treatable emergency with significant mortality and morbidity. The objective of this study is to examine the characteristics of patients with NCS presenting with altered mental status (AMS) and diagnosed with electroencephalography (EEG), to identify the factors that could increase the pre-test probability of NCS. METHODS: Retrospective study using the data collected prospectively. Inclusion criteria:patients older than 13 years with AMS. Exclusion criteria: (1) immediately correctable AMS (e.g., hypoglycemia, opiod overdose); (2) inability to undergo EEG; (3) hemodynamic instability. Outcomes compared between NCS and non-NCS cases: vital signs, lactate level, gender, witnessed seizure, use of anticonvulsive in the field or in the ED, history of seizure or stroke, head injury, abnormal neurological finding and new abnormal findings on head CT. Data presented as medians and quartiles for categorical and percentages with 95％CI for continuous variables. Univariate analyses were performed with Man-Whitney U and Fisher's Exact tests. A multivariate analysis model was used to test the predictive value of clinical variables in identifying NCS. RESULTS: From 332 patients (median age 66 years, quartiles 50–78), 16 were diagnosed with NCS (5％, 95％CI 3％–8％). Only age was significantly different between the NCS vs. non-NCS groups in both univariate (P=0.032) and multivariate analyses (P=0.016). CONCLUSION: Other than age, no other clinically useful variable could identify patients at high risk of NCS. ED physicians should have a high suspicion for NCS and should order EEG for these patients more liberally.

Diagnostic accuracy of microEEG: a miniature, wireless EEG device.

Measuring the diagnostic accuracy (DA) of an EEG device is unconventional and complicated by imperfect interrater reliability. We sought to compare the DA of a miniature, wireless, battery-powered EEG device ("microEEG") to a reference EEG machine in emergency department (ED) patients with altered mental status (AMS). Two hundred twenty-five ED patients with AMS underwent 3 EEGs. Two EEGs, EEG1 (Nicolet Monitor, "reference") and EEG2 (microEEG) were recorded simultaneously with EEG cup electrodes using a signal splitter. The remaining study, EEG3, was recorded with microEEG using an electrode cap immediately before or after EEG1/EEG2. The official EEG1 interpretation was considered the gold standard (EEG1-GS). EEG1, 2, and 3 were de-identified and blindly interpreted by two independent readers. A generalized mixed linear model was used to estimate the sensitivity and specificity of these interpretations relative to EEG1-GS and to compute a diagnostic odds ratio (DOR). Seventy-nine percent of EEG1-GS were abnormal. Neither the DOR nor the κf representing interrater reliabilities differed significantly between EEG1, EEG2, and EEG3. The mean setup time was 27 min for EEG1/EEG2 and 12 min for EEG3. The mean electrode impedance of EEG3 recordings was 12.6 kΩ (SD: 31.9 kΩ). The diagnostic accuracy of microEEG was comparable to that of the reference system and was not reduced when the EEG electrodes had high and unbalanced impedances. A common practice with many scientific instruments, measurement of EEG device DA provides an independent and quantitative assessment of device performance.

Effect of microEEG on clinical management and outcomes of emergency department patients with altered mental status: a randomized controlled trial.

OBJECTIVES: Altered mental status (AMS) is a common presentation in the emergency department (ED). A previous study revealed 78% electroencephalogram (EEG) abnormalities, including nonconvulsive seizure (NCS; 5%), in ED patients with AMS. The objective of this study was to assess the impact of EEG on clinical management and outcomes of ED patients with AMS. METHODS: This was a randomized controlled trial at two urban teaching hospitals. Adult patients (≥18 years old) with AMS were included. Excluded patients had immediately correctable AMS (e.g., hypoglycemia) or were admitted before enrollment. Patients were randomized to routine care (control) or routine care plus EEG (intervention). Research assistants used a scalp electrode set with a miniature, wireless EEG device (microEEG) to record standard 30-minute EEGs at presentation, and results were reported to the ED attending physician by an off-site epileptologist within 30 minutes. Primary outcomes included changes in ED management (differential diagnosis, diagnostic work-up, and treatment plan from enrollment to disposition) as determined by surveying the treating physicians. Secondary outcomes were length of ED and hospital stay, intensive care unit (ICU) requirement, and in-hospital mortality. RESULTS: A total of 149 patients were enrolled (76 control and 73 intervention). Patients in the two groups were comparable at baseline. EEG in the intervention group revealed abnormal findings in 93% (95% confidence interval [CI] = 85% to 97%), including NCS in 5% (95% CI = 2% to 13%). Using microEEG was associated with change in diagnostic work-up in 49% (95% CI = 38% to 60%) of cases and therapeutic plan in 42% (95% CI = 31% to 53%) of cases immediately after the release of EEG results. Changes in probabilities of differential diagnoses and the secondary outcomes were not statistically significant between the groups. CONCLUSIONS: An EEG can be obtained in the ED with minimal resources and can affect clinical management of AMS patients.

EEG interpretation reliability and interpreter confidence: a large single-center study.

The intrarater and interrater reliability (I&IR) of EEG interpretation has significant implications for the value of EEG as a diagnostic tool. We measured both the intrarater reliability and the interrater reliability of EEG interpretation based on the interpretation of complete EEGs into standard diagnostic categories and rater confidence in their interpretations and investigated sources of variance in EEG interpretations. During two distinct time intervals, six board-certified clinical neurophysiologists classified 300 EEGs into one or more of seven diagnostic categories and assigned a subjective confidence to their interpretations. Each EEG was read by three readers. Each reader interpreted 150 unique studies, and 50 studies were re-interpreted to generate intrarater data. A generalizability study assessed the contribution of subjects, readers, and the interaction between subjects and readers to interpretation variance. Five of the six readers had a median confidence of ≥99%, and the upper quartile of confidence values was 100% for all six readers. Intrarater Cohen's kappa (κc) ranged from 0.33 to 0.73 with an aggregated value of 0.59. Cohen's kappa ranged from 0.29 to 0.62 for the 15 reader pairs, with an aggregated Fleiss kappa of 0.44 for interrater agreement. Cohen's kappa was not significantly different across rater pairs (chi-square=17.3, df=14, p=0.24). Variance due to subjects (i.e., EEGs) was 65.3%, due to readers was 3.9%, and due to the interaction between readers and subjects was 30.8%. Experienced epileptologists have very high confidence in their EEG interpretations and low to moderate I&IR, a common paradox in clinical medicine. A necessary, but insufficient, condition to improve EEG interpretation accuracy is to increase intrarater and interrater reliability. This goal could be accomplished, for instance, with an automated online application integrated into a continuing medical education module that measures and reports EEG I&IR to individual users.

Prevalence of non-convulsive seizure and other electroencephalographic abnormalities in ED patients with altered mental status.

UNLABELLED: Four to ten percent of patients evaluated in emergency departments (ED) present with altered mental status (AMS). The prevalence of non-convulsive seizure (NCS) and other electroencephalographic (EEG) abnormalities in this population is unknown. OBJECTIVES: To identify the prevalence of NCS and other EEG abnormalities in ED patients with AMS. METHODS: A prospective observational study at 2 urban ED. Inclusion: patients ≥13 years old with AMS. Exclusion: An easily correctable cause of AMS (e.g. hypoglycemia). A 30-minute standard 21-electrode EEG was performed on each subject upon presentation. OUTCOME: prevalence of EEG abnormalities interpreted by a board-certified epileptologist. EEGs were later reviewed by 2 blinded epileptologists. Inter-rater agreement (IRA) of the blinded EEG interpretations is summarized with κ. A multiple logistic regression model was constructed to identify variables that could predict the outcome. RESULTS: Two hundred fifty-nine patients were enrolled (median age: 60, 54% female). Overall, 202/259 of EEGs were interpreted as abnormal (78%, 95% confidence interval [CI], 73-83%). The most common abnormality was background slowing (58%, 95% CI, 52-68%) indicating underlying encephalopathy. NCS (including non-convulsive status epilepticus [NCSE]) was detected in 5% (95% CI, 3-8%) of patients. The regression analysis predicting EEG abnormality showed a highly significant effect of age (P < .001, adjusted odds ratio 1.66 [95% CI, 1.36-2.02] per 10-year age increment). IRA for EEG interpretations was modest (κ: 0.45, 95% CI, 0.36-0.54). CONCLUSIONS: The prevalence of EEG abnormalities in ED patients with undifferentiated AMS is significant. ED physicians should consider EEG in the evaluation of patients with AMS and a high suspicion of NCS/NCSE.

Technical and clinical analysis of microEEG: a miniature wireless EEG device designed to record high-quality EEG in the emergency department.

BACKGROUND: We describe and characterize the performance of microEEG compared to that of a commercially available and widely used clinical EEG machine. microEEG is a portable, battery-operated, wireless EEG device, developed by Bio-Signal Group to overcome the obstacles to routine use of EEG in emergency departments (EDs). METHODS: The microEEG was used to obtain EEGs from healthy volunteers in the EEG laboratory and ED. The standard system was used to obtain EEGs from healthy volunteers in the EEG laboratory, and studies recorded from patients in the ED or ICU were also used for comparison. In one experiment, a signal splitter was used to record simultaneous microEEG and standard EEG from the same electrodes. RESULTS: EEG signal analysis techniques indicated good agreement between microEEG and the standard system in 66 EEGs recorded in the EEG laboratory and the ED. In the simultaneous recording the microEEG and standard system signals differed only in a smaller amount of 60 Hz noise in the microEEG signal. In a blinded review by a board-certified clinical neurophysiologist, differences in technical quality or interpretability were insignificant between standard recordings in the EEG laboratory and microEEG recordings from standard or electrode cap electrodes in the ED or EEG laboratory. The microEEG data recording characteristics such as analog-to-digital conversion resolution (16 bits), input impedance (>100MΩ), and common-mode rejection ratio (85 dB) are similar to those of commercially available systems, although the microEEG is many times smaller (88 g and 9.4 × 4.4 × 3.8 cm). CONCLUSIONS: Our results suggest that the technical qualities of microEEG are non-inferior to a standard commercially available EEG recording device. EEG in the ED is an unmet medical need due to space and time constraints, high levels of ambient electrical noise, and the cost of 24/7 EEG technologist availability. This study suggests that using microEEG with an electrode cap that can be applied easily and quickly can surmount these obstacles without compromising technical quality.

Cardiac repolarization indices in epilepsy patients.

OBJECTIVES: Although epilepsy may be associated with an increased risk for sudden cardiac death, its effects on Q-T intervals has not been established. METHODS: To determine whether changes in Q-T interval duration (QT(max c), QT(min c)) and dispersion (QT(D c)) occur in epileptic patients, we retrospectively studied 40 consecutive patients (age: 36.1 +/- 22.2 years) who have had a seizure disorder for 14.0 +/- 12.2 years and were seen in the Epilepsy Monitoring Unit, and 60 age-matched non-epileptic controls (age: 38.0 +/- 15.6 years). Q-T intervals were calculated from a single 12-lead ECG. RESULTS: QT(max c) (425 +/- 30 vs. 410 +/- 36 ms, p = 0.040) and QT(D c) (63.1 +/- 22.4 vs. 31.0 +/- 17.2 ms, p = 0.000) were higher, and QT(min c) (362 +/- 36 vs. 379 +/- 33 ms, p = 0.040) was lower in epilepsy patients. QT(max c) was significantly correlated with disease duration (r = -0.35, p = 0.028) before, but not after age correction (r = -0.31, p = 0.053). Neither age nor reported recent seizure frequency was correlated with any repolarization index. CONCLUSIONS: QT(max c) and QT(D c) are higher in epilepsy patients as compared to control subjects. While Q-T interval appears to be related to disease duration, particularly over the early history of disease, it is unrelated to patient age or recent reported seizure frequency.