Use Of Quantitative EEG Research Articles

Practice Guideline: Use of Quantitative EEG for the Diagnosis of Mild Traumatic Brain Injury: Report of the Guideline Committee of the American Clinical Neurophysiology Society.

Despite many decades of research, controversy regarding the utility of quantitative EEG (qEEG) for the accurate diagnosis of mild traumatic brain injury (mTBI) remains. This guideline is meant to assist clinicians by providing an expert review of the clinical usefulness of qEEG techniques for the diagnosis of mTBI. This guideline addresses the following primary aim: For patients with or without posttraumatic symptoms (abnormal cognition or behavior), does qEEG either at the time of injury or remote from the injury, as compared with current clinical diagnostic criteria, accurately identify those patients with mTBI (i.e., concussion)? Secondary aims included differentiating between mTBI and other diagnoses, detecting mTBI in the presence of central nervous system medications, and pertinence of statistical methods for measurements of qEEG components. It was found that for patients with or without symptoms of abnormal cognition or behavior, current evidence does not support the clinical use of qEEG either at the time of the injury or remote from the injury to diagnose mTBI (level U). In addition, the evidence does not support the use of qEEG to differentiate mTBI from other diagnoses or detect mTBI in the presence of central nervous system medications, and suitable statistical methods do not exist when using qEEG to identify patients with mTBI. Based upon the current literature review, qEEG remains an investigational tool for mTBI diagnosis (class III evidence).

Early Developmental EEG and Seizure Phenotypes in a Full Gene Deletion of Ubiquitin Protein Ligase E3A Rat Model of Angelman Syndrome.

Angelman syndrome (AS) is a neurodevelopmental disorder with unique behavioral phenotypes, seizures, and distinctive electroencephalographic (EEG) patterns. Recent studies identified motor, social communication, and learning and memory deficits in a CRISPR engineered rat model with a complete maternal deletion of the Ube3a gene. It is unknown whether this model recapitulates other aspects of the clinical disorder. We report here the effect of Ube3a maternal deletion in the rat on epileptiform activity, seizure threshold, and quantitative EEG. Using video-synchronized EEG (vEEG) monitoring, we assessed spectral power and epileptiform activity early postnatally through adulthood. While EEG power was similar to wild-type (WT) at 1.5 weeks postnatally, at all other ages analyzed, our findings were similar to the AS phenotype in mice and humans with significantly increased δ power. Analysis of epileptiform activity in juvenile and adult rats showed increased time spent in epileptiform activity in AS compared with WT rats. We evaluated seizure threshold using pentylenetetrazol (PTZ), audiogenic stimulus, and hyperthermia to provoke febrile seizures (FSs). Behavioral seizure scoring following PTZ induction revealed no difference in seizure threshold in AS rats, however behavioral recovery from the PTZ-induced seizure was longer in the adult group with significantly increased hippocampal epileptiform activity during this phase. When exposed to hyperthermia, AS rat pups showed a significantly lower temperature threshold to first seizure than WT. Our findings highlight an age-dependence for the EEG and epileptiform phenotypes in a preclinical model of AS, and support the use of quantitative EEG and increased δ power as a potential biomarker of AS.

Electroencephalogram in dementia with Lewy bodies: a systematic review.

Dementia with Lewy bodies (DLB) belongs to the spectrum of Lewy body dementia (LBD) that also encompasses Parkinson's disease dementia (PDD). It is a common neurodegenerative disorder characterized by memory decline, cognitive fluctuations, visual hallucinations, autonomic nervous system disturbance, REM sleep behavior disorder, and parkinsonism. Definite diagnosis can be established only through neuropathological confirmation of Lewy bodies' presence in brain tissue. Probable or possible diagnosis relies upon clinical features, imaging, polysomnography, and electroencephalogram (EEG) findings. Potential neurophysiological biomarkers for the diagnosis, management, and evaluation of treatment-response in DLB should be affordable and widely available outside academic centers. Increasing evidence supports the use of quantitative EEG (qEEG) as a potential DLB biomarker, with promising results in discriminating DLB from other dementias and in identifying subjects who are on the trajectory to develop DLB. Several studies evaluated the diagnostic value of EEG in DLB. Visual analysis and qEEG techniques have been implemented, showing a superiority of the last in terms of sensitivity and objectivity. In this systematic review, we attempt to provide a general synthesis of the current knowledge on EEG application in DLB. We review the findings from original studies and address the issues remaining to be further clarified.

The Quantitative Electroencephalogram and the Use of Normative Databases

This report describes the scientific, technical, and clinical bases for the use of quantitative EEG (QEEG) in the assessment of clients and in treatment monitoring. Specific attention is directed toward the use of normative databases and z-scores as a form of standardized referencing for reporting and training purposes. Normative databases have general value and are of particular value when connectivity metrics are being used. It is shown that the use of z = 0 as an average over time corresponds to a state of optimum flexibility, adaptability, and readiness. The use of the inverse solution (LORETA) methods is also described, as well as use of those methods within the QEEG and normative model. Advantages as well as shortcomings of this approach are described and discussed.

Network dynamics in the healthy and epileptic developing brain

Electroencephalography (EEG) allows recording of cortical activity at high temporal resolution. EEG recordings can be summarized along different dimensions using network-level quantitative measures, such as channel-to-channel correlation, or band power distributions across channels. These reveal network patterns that unfold over a range of different timescales and can be tracked dynamically. Here we describe the dynamics of network state transitions in EEG recordings of spontaneous brain activity in normally developing infants and infants with severe early infantile epileptic encephalopathies (n = 8, age: 1–8 months). We describe differences in measures of EEG dynamics derived from band power, and correlation-based summaries of network-wide brain activity. We further show that EEGs from different patient groups and controls may be distinguishable on a small set of the novel quantitative measures introduced here, which describe dynamic network state switching. Quantitative measures related to the sharpness of switching from one correlation pattern to another show the largest differences between groups. These findings reveal that the early epileptic encephalopathies are associated with characteristic dynamic features at the network level. Quantitative network-based analyses like the one presented here may in the future inform the clinical use of quantitative EEG for diagnosis.

P.076 Quantitative EEG in Canada: a national technologist survey

Background: Burgeoning EEG demand has largely gone unmet with insufficient supply of manpower and equipment. Quantitative EEG (QEEG) may help compress large volumes of data for expedited review. We sought to determine the current use of QEEG in Canada through a national EEG technologist survey. Methods: A 10-item questionnaire was administered to participants at the 2016 meeting of the Canadian Association of Electroneurophysiology Technologists, which occurred in parallel with the Canadian Neurological Sciences Federation meeting. Results: A response rate of 63% (14/22) represented 12 institutions (11 adult, 6 paediatric) over six provinces with 73% of the national population. Only academic institutions (9/12) used QEEG, representing five provinces with 70% of the national population. Most institutions generated QEEG either real-time or retrospectively in the critical care and epilepsy monitoring units for long-term monitoring and automated seizure detection. The most used trends were spectrographic, seizure detection, and artifact detection. Montage use, QEEG duration, and timebase settings were highly variable. Conclusions: QEEG is in surprisingly frequent use across Canada. There is no consensus on optimal QEEG use, which mirrors uncertainty in the literature. The relative ubiquity of QEEG in Canada offers promise for collaborative multicentre research into unlocking the full potential of QEEG in enhancing patient care.

Utilization of Quantitative EEG Trends for Critical Care Continuous EEG Monitoring: A Survey of Neurophysiologists.

Quantitative EEG (QEEG) can be used to assist with review of large amounts of data generated by critical care continuous EEG monitoring. This study aimed to identify current practices regarding the use of QEEG in critical care continuous EEG monitoring of critical care patients. An online survey was sent to 796 members of the American Clinical Neurophysiology Society (ACNS), instructing only neurophysiologists to participate. The survey was completed by 75 neurophysiologists that use QEEG in their practice. Survey respondents reported that neurophysiologists and neurophysiology fellows are most likely to serve as QEEG readers (97% and 52%, respectively). However, 21% of respondents reported nonneurophysiologists are also involved with QEEG interpretation. The majority of nonneurophysiologist QEEG data review is aimed to alert neurophysiologists to periods of concern, but 22% reported that nonneurophysiologists use QEEG to directly guide clinical care. Quantitative EEG was used most frequently for seizure detection (92%) and burst suppression monitoring (59%). A smaller number of respondents use QEEG for monitoring the depth of sedation (29%), ischemia detection (28%), vasospasm detection (28%) and prognosis after cardiac arrest (21%). About half of the respondents do not review every page of the raw critical care continuous EEG record when using QEEG. Respondents prefer a panel of QEEG trends displayed as hemispheric data, when applicable. There is substantial variability regarding QEEG trend preferences for seizure detection and ischemia detection. QEEG is being used by neurophysiologists and nonneurophysiologists for applications beyond seizure detection, but practice patterns vary widely. There is a need for standardization of QEEG methods and practices.

33.1 REVIEW OF THE USE OF QUANTITATIVE ELECTROENCEPHALOGRAPHIC NEUROFEEDBACK AND THETA BETA NEUROFEEDBACK IN CHILDREN WITH DYSLEXIA AND LEARNING DISORDERS

The goal of this session is to review the literature on the use of quantitative EEG (qEEG) neurofeedback (NF) and the use of theta/beta ratio (TBR) NF in the treatment of learning disorders (LD).

Quantitative EEG parameters correlate with the progression of human prion diseases

BackgroundPrion diseases are universally fatal and often rapidly progressive neurodegenerative diseases. EEG has long been used in the diagnosis of sporadic Creutzfeldt-Jakob disease; however, the characteristic waveforms do not occur...

Non-expert use of quantitative EEG displays for seizure identification in the adult neuro-intensive care unit

Video-EEG monitoring is the ultimate way to diagnose non-convulsive status epilepticus (NCSE) in intensive care units (ICU). Usually EEG recordings are evaluated once a day by an electrophysiologist, which may lead to delay in diagnosis. Digital EEG trend analysis methods like amplitude integrated EEG (aEEG) and density spectral array (DSA) have been developed to facilitate recognition of seizures. In this study, we aimed to investigate the diagnostic utility of these methods by non-expert physicians and ICU nurses for NCSE identification in an adult neurological ICU. Ten patients with NCSE and ten control patients without seizures were included in the study. The raw EEG recordings of all subjects were converted to both aEEG and DSA and displayed simultaneously without conventional EEG. After training for seizure recognition with both methods, two physicians and two nurses analyzed the visual displays individually, and marked seizure timings. Their results were compared with those of a study epileptologist. Participants analyzed 615h of EEG data with 700 seizures. Overall, 63% of the seizures were recognized by all, 15.6% by three, 11.6% by two, 8.3% by one rater and only 1.5% were missed by all of them (sensitivity was 88-99%, and specificity was 89-95% when the ratings were assessed as 1-h epochs). False positive rates were 1 per 2h in the study and 1 per 6h in the control groups. Interrater agreement was high (κ=0.79-0.81). Bilateral independent seizures and ictal recordings with lower amplitude and shorter duration were more likely to be missed. There was no difference in performance between the rating of physicians and nurses. Our study demonstrates that bedside nurses, ICU fellows and residents can achieve acceptable level of accuracy for seizure identification using the digital EEG trend analysis methods following brief training. This may help earlier notification of the electrophysiologist who is not always available in ICUs.

Continuous EEG monitoring: A survey of neurophysiologists and neurointensivists

Continuous EEG monitoring (cEEG) of critically ill adults is being used with increasing frequency, and practice guidelines on indications for cEEG monitoring have recently been published. However, data describing the current practice of cEEG in critically ill adults is limited. We aimed to describe the current practice of cEEG monitoring in adults in the United States. A survey assessing cEEG indications and procedures was sent to one intensivist and one neurophysiologist responsible for intensive care unit (ICU) cEEG at 151 institutions in the United States. At some institutions only one physician could be identified. One hundred thirty-seven physicians from 97 institutions completed the survey. Continuous EEG is utilized by nearly all respondents to detect nonconvulsive seizures (NCS) in patients with altered mental status following clinical seizures, intra cerebral hemorrhage (ICH), traumatic brain injury, and cardiac arrest, as well as to characterize abnormal movements suspected to be seizures. The majority of physicians monitor comatose patients for 24-48h. In an ideal situation with unlimited resources, 18% of respondents would increase cEEG duration. Eighty-six percent of institutions have an on-call EEG technologist available 24/7 for new patient hookups, but only 26% have technologists available 24/7 in-house. There is substantial variability in who reviews EEG and how frequently it is reviewed as well as use of quantitative EEG. Although there is general agreement regarding the indications for ICU cEEG, there is substantial interinstitutional variability in how the procedure is performed.

Quantitative EEG and neurofeedback in children and adolescents: anxiety disorders, depressive disorders, comorbid addiction and attention-deficit/hyperactivity disorder, and brain injury.

This article explores the science surrounding neurofeedback. Both surface neurofeedback (using 2-4 electrodes) and newer interventions, such as real-time z-score neurofeedback (electroencephalogram [EEG] biofeedback) and low-resolution electromagnetic tomography neurofeedback, are reviewed. The limited literature on neurofeedback research in children and adolescents is discussed regarding treatment of anxiety, mood, addiction (with comorbid attention-deficit/hyperactivity disorder), and traumatic brain injury. Future potential applications, the use of quantitative EEG for determining which patients will be responsive to medications, the role of randomized controlled studies in neurofeedback research, and sensible clinical guidelines are considered.

Quantitative EEG evaluation in patients with acute encephalopathy

To investigate the use of quantitative EEG (qEEG) in patients with acute encephalopathies (AEs) and EEG background abnormalities. Patients were divided into favorable outcome (group A, 43 patients) and an unfavorable outcome (group B, 5 patients). EEGLAB software was used for the qEEG analysis. A graphic of the spectral power from all channels was generated for each participant. Statistical comparisons between the groups were performed. In group A, spectral analysis revealed spectral peaks (theta and alpha frequency bands) in 84% (38/45) of the patients. In group B, a spectral peak in the delta frequency range was detected in one patient. The remainder of the patients in both groups did not present spectral peaks. Statistical analysis showed lower frequencies recorded from the posterior electrodes in group B patients. qEEG may be useful in the evaluations of patients with AEs by assisting with the prognostic determination.

Quantitative EEG in Hospital Encephalopathy

Hospital-acquired encephalopathy is a widely prevalent disorder. The quantitative changes in EEG associated with this condition have long been noted, including slowing of the background frequency and changes in the frequency band power. EEG has had limited clinical use, despite its ability to continuously track clinical severity. We review the development of the use of EEG and particularly quantitative EEG in the assessment of hospital-acquired encephalopathy. Recent advances in EEG technology have included network and microstate analyses, and continuous EEG monitoring, leading to renewed interest in the use of quantitative EEG. We describe the development of microstate analysis that has allowed novel quantitative analysis of the resting state background. We examined the microstates of 16 inpatients with encephalopathy and 20 control patients. The global variance explained by the four standard resting microstates was smaller in patients with encephalopathy. This suggests a decrease in microstate stability, indicating a breakdown in the resting state network dynamics. Modern analysis and acquisition techniques hold the promise of renewed interest in quantitative EEG techniques in the assessment of hospital-acquired encephalopathy.

Validity of the Quantitative EEG Statistical Pattern Recognition Method in Diagnosing Alzheimer’s Disease

Aims: To evaluate the use of quantitative EEG (qEEG) statistical pattern recognition in diagnosing Alzheimer’s disease (AD). Methods: qEEG was performed on 104 patients referred to a memory clinic. The qEEG results were compared to the clinical diagnosis made without access to the EEG results. Results: Of 30 patients with a clinical diagnosis of AD, 22 were test positive. Of the 74 patients without AD, 34 were test negative. The qEEG result was found to correlate with atrophy of the medial temporal lobe demonstrated on cerebral MRI (p = 0.002) and with scores on neuropsychological tests. Conclusion: The qEEG was poor at diagnosing AD, as it produced many false-positive results.

Gamma oscillations and schizophrenia

In the last 2 decades, it has become apparent that brain regions communicate by coordinating the firing of populations of neurons. When neurons synchronize their firing, their rhythmic input is reflected in the extracellular field potential as brain oscillations. Rhythmic brain activity in animals and humans can be recorded using noninvasive techniques such as electroencephalography (EEG) by placing recording electrodes on the surface of the scalp and, more rarely, intracranially at the surface of a brain region, such as the temporal lobe or hippocampus. Human EEG was developed as early as the 1920s by German psychiatrist Hans Berger and has been in constant use since then. However, in the last 25 years, advances in signal processing and methods for the analysis of EEG spectra have intensified interest in the use of quantitative EEG for research purposes. Additionally, more recently developed magnetoencephalographic (MEG) techniques have shown great potential for measuring weak magnetic fields generated by rhythmic currents in the brain. With these methods, rhythmic activity of various frequencies such as delta (0–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz) and gamma (30–200 Hz) can be detected. These neural oscillations are correlated with and are believed to play a role in normal cognitive processes including memory, attention, perceptual binding and consciousness.1

The clinical use of quantitative EEG in cognitive disorders.

The primary diagnosis of most cognitive disorders is clinically based, but the EEG plays a role in evaluating, classifying and following some of these disorders. There is an ongoing debate over routine use of qEEG. Although many findings regarding the clinical use of quantitative EEG are awaiting validation by independent investigators while confirmatory clinical follow-up studies are also needed, qEEG can be cautiously used by a skilled neurophysiologist in cognitive dysfunctions to improve the analysis of background activity, slow/fast focal activity, subtle asymmetries, spikes and waves, as well as in longitudinal follow-ups.

Use of quantitative EEG in infants with port-wine birthmark to assess for Sturge–Weber brain involvement

Many infants born with a facial port-wine (PW) birthmark will not develop brain involvement of Sturge-Weber syndrome (SWS). Previous studies have shown asymmetry in quantitative EEG (qEEG) correlates with degree of clinical impairment in children and adults with known SWS. We hope to determine if quantitative qEEG can be used as a method to predict which infants are most likely to develop SWS brain involvement on MRI. The current study looks at the ability of qEEG to differentiate between infants with radiographically demonstrated SWS and those without. We first performed an observational study of qEEG results on eight infants with facial PW birthmark (four had SWS brain involvement). We recorded standard clinical EEGs and then derived a measure of asymmetry. We subsequently validated this threshold through a study of an additional nine infants with PW birthmark (five with SWS brain involvement). Quantitative EEG correctly identified infants with SWS brain involvement in all cases in the Validation cohort. This technique was at least as good as a pediatric electroencephalographer with extensive experience reading SWS EEGs. This study demonstrates the ability for qEEG to discriminate between those infants with SWS brain involvement and those with neurologically asymptomatic PW birthmark. This study represents an important step toward the development of a qEEG technique able to predict which infants with PW birthmark will develop SWS brain involvement.

Electroencephalography Technology for Predicting Response to Antidepressant Medications

<h4>EXCERPT</h4> <p>Finding an effective antidepressant agent for a patient is a process of trial and error. Improvement generally requires 4 to 12 weeks of treatment. Patients may stop taking medication too soon because of delayed response or intolerable side effects. Fewer than half of patients achieve full remission on the first drug that is tried. Predicting how a patient might respond to treatment would help identify an effective treatment more quickly, avoid unnecessary medication changes, and encourage treatment adherence. Electroencephalography (EEG) is an old technology used to measure brain waves and diagnose epilepsy. In this article, I describe the use of quantitative EEG (QEEG) for predicting antidepressant treatment response.</p>

Quantitative electroencephalographic findings in beagles anaesthetized with propofol

The purpose of this study was to assess quantitative electroencephalography (q-EEG) in 10 healthy beagle dogs under propofol anaesthesia in order to determine objective guidelines for diagnostic electroencephalographic (EEG) recordings and interpretation. The basic pattern after preliminary visual examination of EEG recordings was characterized by spindles, k-complexes, vertex sharp transients, and positive occipital transients that were superimposed on the slow background activity. The results of the q-EEG were characterized by the prevalence of slow rhythms δ and θ, both in absolute and relative power spectrum analysis, while fast rhythms ( α and β) were poorly represented. The distribution of single frequency bands was widespread for δ, focal for frontal and central for θ, as well as for most α and β patterns. The present study has shown that the use of quantitative EEG gives information on the frequency content of the bio-electrical activity and defines the distribution of the single frequency bands under a standardized anaesthetic protocol.