Quantitative EEG is an objective, sensitive, and reliable indicator of transient anesthetic effects during Wada tests

Seizures induced in electroconvulsive therapy as a human epilepsy model: A comparative case study

OBJECTIVE

Standardized investigation of epileptic seizures and the postictal state may contribute to a better understanding of ictal and postictal phenomena. This comparative case study aims to assess whether electrically induced seizures in electroconvulsive therapy (ECT) show sufficient similarities with spontaneous seizures to serve as a human epilepsy model.

METHODS

We compared six EEG recordings, three ECT-induced seizures and three generalized tonic-clonic seizures, using quantitative electroencephalography (EEG) analyses. EEG recordings during and after ECT sessions (under general anesthesia and muscle paralysis) were collected prospectively, whereas epilepsy data were selected retrospectively. Time-frequency representations, dominant ictal frequencies, and postictal alpha-delta ratios were calculated.

RESULTS

In all EEG recordings, a decrease in dominant ictal frequency was observed, as well as postictal suppression. Postictal alpha-delta ratio indicated the same trend for all: a gradual increase from predominantly delta to alpha frequencies on timescales of hours after the seizure. Postictal spectral representation was similar. Muscle artifacts were absent in ECT-induced seizures and present in spontaneous seizures. Ictal amplitude was higher in epileptic than in ECT-induced seizures. Temporospectral ictal dynamics varied slightly between groups.

SIGNIFICANCE

We show that ictal and postictal characteristics in ECT and patients with generalized tonic-clonic seizures are essentially similar. ECT-induced seizures may be used to investigate aspects of ictal and postictal states in a highly predictable manner and well-controlled environment. This suggests that clinical and electrophysiological observations during ECT may be extrapolated to epilepsy with generalized tonic-clonic seizures.

Quantitative EEG Changes Correlate With Post-Clamp Ischemia During Carotid Endarterectomy

INTRODUCTION

EEG monitoring is a critical tool for identifying cerebral ischemia during carotid endarterectomy (CEA). Quantitative EEG can be used to supplement visual EEG review, but which measures best predict post-clamp ischemia is unclear.

PURPOSE

To determine which quantitative EEG parameters reliably detect intraoperative ischemia during CEA.

METHODS

The authors identified patients who underwent carotid endarterectomy at Columbia University Medical Center from 2007 to 2014 with intraoperative EEG monitoring. Two masked physicians reviewed these EEGs retrospectively and determined whether there was post-clamp ischemia, categorizing patients into (1) ischemic-change and (2) no-ischemic-change groups. The authors then studied the performance of a battery of quantitative EEG measures (alpha, beta, theta, and delta power bands, alpha-delta ratio, beta-delta ratio, amplitude-integrated EEG, and 90% spectral edge frequency) against physician review as the gold standard.

RESULTS

Of 118 patients, 15 were included in the ischemic-change group and 103 in the no-ischemic-change group. Ipsilateral post-clamp trough values of all the quantitative EEG measures assessed were significantly decreased for patients in the ischemic-change group. Decreases in alpha, beta, and theta power of 52.1%, 41.6%, and 36.4% or greater, respectively, were most predictive of post-clamp ischemia.

CONCLUSIONS

Quantitative EEG monitoring during carotid endarterectomy, in addition to visual EEG monitoring, may improve the detection of cerebral ischemia and thus result in fewer perioperative strokes.

Quantitative EEG biomarkers for epilepsy and their relation to chemical biomarkers

The electroencephalogram (EEG) is the most important method to diagnose epilepsy. In clinical settings, it is evaluated by experts who identify patterns visually. Quantitative EEG is the application of digital signal processing to clinical recordings in order to automatize diagnostic procedures, and to make patterns visible that are hidden to the human eye. The EEG is related to chemical biomarkers, as electrical activity is based on chemical signals. The most well-known chemical biomarkers are blood laboratory tests to identify seizures after they have happened. However, research on chemical biomarkers is much less extensive than research on quantitative EEG, and combined studies are rarely published, but highly warranted. Quantitative EEG is as old as the EEG itself, but still, the methods are not yet standard in clinical practice. The most evident application is an automation of manual work, but also a quantitative description and localization of interictal epileptiform events as well as seizures can reveal important hints for diagnosis and contribute to presurgical evaluation. In addition, the assessment of network characteristics and entropy measures were found to reveal important insights into epileptic brain activity. Application scenarios of quantitative EEG in epilepsy include seizure prediction, pharmaco-EEG, treatment monitoring, evaluation of cognition, and neurofeedback. The main challenges to quantitative EEG are poor reliability and poor generalizability of measures, as well as the need for individualization of procedures. A main hindrance for quantitative EEG to enter clinical routine is also that training is not yet part of standard curricula for clinical neurophysiologists.

Diagnostic accuracy between readers for identifying electrographic seizures in critically ill adults

Objective

Electrographic seizures in critically ill patients are often equivocal. In this study, we sought to determine the diagnostic accuracy of electrographic seizure annotation in adult intensive care units (ICUs) and to identify affecting factors.

Methods

To investigate diagnostic accuracy, interreader agreement (IRA) measures were derived from 5,769 unequivocal and 6,263 equivocal seizure annotations by five experienced electroencephalogram (EEG) readers after reviewing 74 days of EEGs from 50 adult ICU patients. Factors including seizure equivocality (unequivocal vs. equivocal) and laterality (generalized, partial, or bilaterally independent), cyclicity (cyclic vs. noncyclic), persistency (occurrence of status epilepticus), and patient consciousness level (coma vs. noncoma) were further investigated for their influence on IRA measures.

Results

On average, 70% of seizures marked by a reference reader overlapped, at least in part, with those marked by a test reader (any-overlap sensitivity, AO-Sn). Agreed seizure duration between reader pairs (overlap-integral sensitivity, OI-Sn) was 62%, while agreed nonseizure duration (overlap-integral specificity, OI-Sp) was 99%. A test reader would annotate one additional seizure not overlapping with a reference reader's annotation in every 11.7 h of EEG, that is, the false-positive rate (FPR) was 0.0854/h. Classifying seizure patterns into unequivocal and equivocal improved specificity and FPR (unequivocal patterns) but compromised sensitivity only for equivocal patterns. Sensitivity of all and unequivocal annotations was higher for patients with status epilepticus. Specificity was higher for partial than for bilaterally independent unequivocal seizure patterns, and lower for cyclic all seizure patterns.

Significance

Diagnosing electrographic seizures in critically ill adults is highly specific and moderately sensitive. Improved criteria for diagnosing electrographic seizures in the ICU are needed.

An improved qEEG index for asymmetry detection during the Wada test

The Wada test is commonly used to evaluate language and memory lateralization in candidates for epilepsy surgery. The spatial Brain Symmetry Index (BSI) quantifies inter-hemispheric differences in the EEG. Its application has been shown to be feasible during Wada testing. We developed a method for the quantification of EEG asymmetry that matches visual assessments of the EEG better than BSI. Fifty-three patients' EEG data, with a total of 85 injections were analyzed. In a step-wise, data-driven manner, multiple electrode and frequency band combinations were evaluated. Eventually, BSI, calculated using only the frontal electrodes F3 and F4, was combined with a temporal measure of delta power in the central electrodes, C3 and C4, into a new measure: cBSI. Using the area under the ROC curve (AUC), we showed that cBSI performs significantly better relative to BSI (median AUC 0.98 versus 0.96, p=0.0015, Wilcoxon signed rank test). Our results showed that asymmetry detection was significantly improved by combining temporal with spatial qEEG measures. In the future, our combined qEEG measure could allow for a more objective way of monitoring EEG asymmetry, thereby increasing the feasibility of using EEG as a monitoring tool during the Wada test. Future studies should, however, validate our cBSI method in real time in the operating room or radiology suite.