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Cozac, Vitalii V., Ute Gschwandtner, Florian Hatz, Martin Hardmeier, Stephan Rüegg, and Peter Fuhr. "Quantitative EEG and Cognitive Decline in Parkinson’s Disease." Parkinson's Disease 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/9060649.
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Cognitive decline is common with the progression of Parkinson’s disease (PD). Different candidate biomarkers are currently studied for the risk of dementia in PD. Several studies have shown that quantitative EEG (QEEG) is a promising predictor of PD-related cognitive decline. In this paper we briefly outline the basics of QEEG analysis and analyze the recent publications addressing the predictive value of QEEG in the context of cognitive decline in PD. The MEDLINE database was searched for relevant publications from January 01, 2005, to March 02, 2015. Twenty-four studies reported QEEG findings in various cognitive states in PD. Spectral and connectivity markers of QEEG could help to discriminate between PD patients with different level of cognitive decline. QEEG variables correlate with tools for cognitive assessment over time and are associated with significant hazard ratios to predict PD-related dementia. QEEG analysis shows high test-retest reliability and avoids learning effects associated with some neuropsychological testing; it is noninvasive and relatively easy to repeat.
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Ng, MC, K. Gillis, and J. Nikkel. "P.076 Quantitative EEG in Canada: a national technologist survey." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 44, S2 (2017): S32—S33. http://dx.doi.org/10.1017/cjn.2017.160.
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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.
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Prichep, L. S., and E. R. John. "Quantitative EEG (QEEG) and psychiatric classification." Biological Psychiatry 42, no. 1 (1997): 64S. http://dx.doi.org/10.1016/s0006-3223(97)87138-7.
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Keski-Säntti, P., T. Kovala, A. Holm, HK Hyvärinen, and M. Sainio. "Quantitative EEG in occupational chronic solvent encephalopathy." Human & Experimental Toxicology 27, no. 4 (2008): 315–20. http://dx.doi.org/10.1177/0960327107082231.
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The aim of this study was to characterize the quantitative analyzed EEG (electroencephalogram) findings (qEEG) in chronic solvent encephalopathy (CSE) patients and study whether the qEEG findings associate with the duration and intensity of the solvent exposure. Also, the diagnostic value of qEEG in CSE is discussed. The EEG of 47 male CSE patients was analyzed. The laboratory’s own reference EEG values of 24 healthy male subjects formed the laboratory control group. We also used an age-matched control group of 100 male blue-collar workers without occupational solvent exposure. The main finding of our study was that the power of the frontal theta band is increased in the CSE patient group compared with the laboratory control group. This suggests that the frontal cortex may be susceptible to the noxious effects of solvents. However, this difference was not seen in comparison with the matched control group, and thus, the connection with solvent effects remains uncertain. The variables indicating the level of solvent exposure did not associate with the power of the theta activity in the frontal area. Because of the small amount and unspecificity of the observed abnormalities, qEEG cannot be recommended to be used in the clinical diagnostics of solvent encephalopathy.
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Roberson, Shawniqua Williams, and Kevin Haas. "Quantitative EEG Features of Level of Consciousness in Critically Ill Nonagenarians." Innovation in Aging 4, Supplement_1 (2020): 122. http://dx.doi.org/10.1093/geroni/igaa057.401.
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Abstract The standard for monitoring sedation levels in critically ill patients is intermittent bedside evaluation, and is prone to anchoring bias. Quantitative electroencephalography (qEEG) allows automated processing of recorded brain electrical activity and could be used to continuously monitor level of consciousness in critically ill patients. The majority of qEEG studies have included persons 80 years of age or less, and the qEEG profiles of nonagenarians have been incompletely characterized. Knowledge of the qEEG patterns of patients 90 years and older is essential for appropriate interpretation of such metrics in this population. This retrospective cohort study characterized qEEG profiles of acutely ill nonagenarians. We investigated whether the relationship between qEEG and level of consciousness differed between patients with and without a history of dementia. We included patients 90-100 years old admitted to Vanderbilt University Medical Center who underwent EEG and as part of their clinical care. We compared qEEG features to nursing-defined level of arousal as measured by the Richmond Agitation-Sedation Scale (RASS) in patients with and without history of dementia. Between January and December 2019, 26 nonagenarians underwent EEG for clinical purposes. One study was excluded due to excessive artifact. Of the remaining, 6 (24%) were male and 18 (72%) were Caucasian. Among all patients, RASS decreased with increases in EEG theta variability (coefficient -7.7, 95%CI -10.6 to -4.8). This relationship was not significantly modified by history of dementia (coefficient of interaction term -0.36, 95%CI -3.7 to 2.9). Dementia does not impact qEEG features of level of consciousness in nonagenarians.
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Braga, Aline Souza Marques da Silva, Bruno Della Ripa Rodrigues Assis, Jamil Thiago Rosa Ribeiro, et al. "Quantitative EEG evaluation in patients with acute encephalopathy." Arquivos de Neuro-Psiquiatria 71, no. 12 (2013): 937–42. http://dx.doi.org/10.1590/0004-282x20130204.
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Objective To investigate the use of quantitative EEG (qEEG) in patients with acute encephalopathies (AEs) and EEG background abnormalities. Method 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. Results 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. Conclusion qEEG may be useful in the evaluations of patients with AEs by assisting with the prognostic determination.
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Thatcher, Robert W., Cart J. Biver, and Duane M. North. "Quantitative EEG and the Frye and Daubert Standards of Admissibility." Clinical Electroencephalography 34, no. 2 (2003): 39–53. http://dx.doi.org/10.1177/155005940303400203.
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The 70-year-old Frye standards of “general acceptance” were replaced by the Supreme Court's 1993 Daubert criteria of the scientific method, which established the standards for admissibility of evidence in Federal Court. The four Daubert criteria were: 1- Hypothesis testing, 2- Estimates of error rates, 3- Peer reviewed publication and 4- General acceptance ( Daubert v. Merrell Dow Pharmaceuticals, 61 U.S.L.W 4805 (U.S. June 29, 1993)). The present paper starts with the Daubert four factors and then matches them, step by step, to the scientific peer reviewed literature of quantitative EEG (QEEG) in relation to different clinical evaluations. This process shows how the peer reviewed science of the Digital EEG and the Quantitative EEG (QEEG) meet all of the Daubert standards of scientific knowledge. Furthermore, the science and technical aspects of QEEG in measuring the effects of neurological and psychiatric dysfunction also match the recent Supreme Court standards of “technical” and “other specialized” knowledge ( General Electric Co v. Joiner, 1997; Kumho Tire Company, Ltd. v. Carmichael, 1999). Finally, it is shown that QEEG scientific knowledge and QEEG “technical” and “other specialized” knowledge meet the trilogy standards of the Supreme Court rulings in support of QEEG's admissibility as a clinically valid method in the evaluation of the nature and extent of neurological and psychiatric disorders.
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Tolonen, Antti, Mika O. K. Särkelä, Riikka S. K. Takala, et al. "Quantitative EEG Parameters for Prediction of Outcome in Severe Traumatic Brain Injury: Development Study." Clinical EEG and Neuroscience 49, no. 4 (2017): 248–57. http://dx.doi.org/10.1177/1550059417742232.
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Monitoring of quantitative EEG (QEEG) parameters in the intensive care unit (ICU) can aid in the treatment of traumatic brain injury (TBI) patients by complementing visual EEG review done by an expert. We performed an explorative study investigating the prognostic value of 59 QEEG parameters in predicting the outcome of patients with severe TBI. Continuous EEG recordings were done on 28 patients with severe TBI in the ICU of Turku University Hospital. We computed a set of QEEG parameters for each patient, and correlated these to patient outcome, measured by dichotomized Glasgow Outcome Scale (GOS) at a follow-up visit between 6 and 12 months, using area under receiver operating characteristic curve (AUC) as a nonlinear correlation measure. For 17 of the 59 QEEG parameters (28.8%), the AUC differed significantly from 0.5, most of these parameters measured EEG power or variability. The best QEEG parameters for outcome prediction were alpha power (AUC = 0.87, P < .01) and variability of the relative fast theta power (AUC = 0.84, P < .01). The results of this study indicate that QEEG parameters provide useful information for predicting outcome in severe TBI. Novel QEEG parameters with potential in outcome prediction were found, the prognostic value of these parameters should be confirmed in later studies. The results also provide further evidence of the usefulness of parameters studied in preexisting studies.
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Reeves, Roy R., Frederick A. Struve, and Gloria Patrick. "The Effects of Donepezil on Quantitative EEG in Patients with Alzheimer's Disease." Clinical Electroencephalography 33, no. 2 (2002): 93–96. http://dx.doi.org/10.1177/155005940203300209.
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Donepezil is a cholinesterase inhibitor which has been previously shown to affect the cognitive evoked potentials (EPs) of patients with Alzheimer's Disease (AD) during treatment with the drug. The purpose of this study was to determine the effect of treatment with donepezil 5 mg daily for 1 month on quantitative EEG (QEEG) in patients with AD. Treatment was associated with no significant differences between the pre- and post-treatment QEEGs for (1) absolute power (all four frequency bands), (2) percent relative power (all four frequency bands), (3) total mean frequency, (4) mean frequency for theta and beta, (5) absolute power asymmetry across homologous electrode pairs (all four frequency bands), and (6) interhemispheric coherence across homologous electrode pairs (all four frequency bands). There were significant decreases in mean alpha and delta frequencies that were consistent across broad electrode arrays except for an increase in the delta frequency at T3. The implications of these changes are not yet clear. Studies of QEEG changes with higher doses of donepezil over longer periods of time may yield a better understanding of the neurophysiological effects of the medication.
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Kaleem, Safa, and Christa B. Swisher. "3124 Early Electrographic Seizure Detection by Neuro ICU Nurses via Bedside Real-Time Quantitative EEG." Journal of Clinical and Translational Science 3, s1 (2019): 38. http://dx.doi.org/10.1017/cts.2019.93.
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OBJECTIVES/SPECIFIC AIMS: 1. Determine positive predictive value, negative predictive value, sensitivity, and specificity of Neuro ICU nurse interpretation of real-time bedside qEEG. 2. Determine difference in time to detection of first seizure between Neuro ICU nurse qEEG interpretation and EEG fellow reads of cEEG. 3. Determine what qualities of seizures make detection by neuro ICU nurses more or less likely – e.g. duration of seizures, type of seizures, spatial extent of seizures. METHODS/STUDY POPULATION: Recruit neuro ICU nurses taking care of 150 patients admitted to the Neuro ICU at Duke University Hospital who are initiated on cEEG monitoring. Nurses will be consented for their participation in the study. Neuro ICU nurses will evaluate the qEE RESULTS/ANTICIPATED RESULTS: From literature estimates of a 20% seizure prevalence in critical care settings, we hope to have 30 patients with seizures and 120 without. Based on prior study in the Duke Neuro ICU, we hypothesize that Neuro ICU nurses will have sensitivity and DISCUSSION/SIGNIFICANCE OF IMPACT: This is the first prospective study of neuro ICU nurse interpretation of real-time bedside qEEG in patients with unknown NCSE/NCS presence. If nurse sensitivity, specificity, and positive predictive value are clinically useful, which we deem would be so at a sensitivity of 70% or greater, with acceptable false alarm rate, nurse readings of qEEG could significantly decrease the time to treatment of seizures in the Neuro ICU patient population, and perhaps could improve patient outcomes.
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Geraedts, Victor J., Lennard I. Boon, Johan Marinus, et al. "Clinical correlates of quantitative EEG in Parkinson disease." Neurology 91, no. 19 (2018): 871–83. http://dx.doi.org/10.1212/wnl.0000000000006473.
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ObjectiveTo assess the relevance of quantitative EEG (qEEG) measures as outcomes of disease severity and progression in Parkinson disease (PD).MethodsMain databases were systematically searched (January 2018) for studies of sufficient methodologic quality that examined correlations between clinical symptoms of idiopathic PD and cortical (surface) qEEG metrics.ResultsThirty-six out of 605 identified studied were included. Results were classified into 4 domains: cognition (23 studies), motor function (13 studies), responsiveness to interventions (7 studies), and other (10 studies). In cross-sectional studies, EEG slowing correlated with global cognitive impairment and with diffuse deterioration in other domains. In longitudinal studies, decreased dominant frequency and increased θ power, reflecting EEG slowing, were biomarkers of cognitive deterioration at an individual level. Results on motor dysfunction and treatment yielded contrasting findings. Studies on functional connectivity at an individual level and longitudinal studies on other domains or on connectivity measures were lacking.ConclusionqEEG measures reflecting EEG slowing, particularly decreased dominant frequency and increased θ power, correlate with cognitive impairment and predict future cognitive deterioration. qEEG could provide reliable and widely available biomarkers for nonmotor disease severity and progression in PD, potentially promoting early diagnosis of nonmotor symptoms and an objective monitoring of progression. More studies are needed to clarify the role of functional connectivity and network analyses.
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Primavera, A., and P. Novello. "Quantitative EEG findings in patients with subcortical vascular encephalopathy." European Psychiatry 7, no. 3 (1992): 121–27. http://dx.doi.org/10.1017/s0924933800003059.
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SummaryTo characterize the neurophysiological profile of patients with Subcortical Vascular Encephalopathy (SVE) we compared the quantified electroencephalogram (qEEG) of 15 consecutive patients, demented and non-demented, with leukoaraiosis and multiple lacunar infarcts on tomographic scan and magnetic resonance investigation, with those of 30 patients with Alzheimer’s disease (AD) and 30 controls. The investigation was carried out using an EEG fast Fourier transform program, and the data obtained were transformed into mean frequency (MF), calculated in the left occipital derivation, and in percent difference (PD), calculated in eight derivations. The data obtained were compared with the scores of the mini-mental state examination (MMS). The results demonstrate that the patients with SVE displayed a different qEEG pattern in comparison with AD patients and controls, with a prevalence of EEG slowing in frontal derivations, and with good correlation between PD values and MMS scores. The qEEG may detect subtle and/or early changes in patients with SVE. The use of adequate spectral parameters may give valuable data for a better classification of dementia syndromes.
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Engedal, Knut, Jon Snaedal, Peter Hoegh, et al. "Quantitative EEG Applying the Statistical Recognition Pattern Method: A Useful Tool in Dementia Diagnostic Workup." Dementia and Geriatric Cognitive Disorders 40, no. 1-2 (2015): 1–12. http://dx.doi.org/10.1159/000381016.
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Background/Aim: The aim of this study was to examine the discriminatory power of quantitative EEG (qEEG) applying the statistical pattern recognition (SPR) method to separate Alzheimer's disease (AD) patients from elderly individuals without dementia and from other dementia patients. Methods: The participants were recruited from 6 Nordic memory clinics: 372 unselected patients [mean age 71.7 years (SD 8.6), 54% women] and 146 healthy elderly individuals [mean age 66.5 years (SD 7.7), 60% women]. After a standardized and comprehensive assessment, clinical diagnoses were made according to internationally accepted criteria by at least 2 clinicians. EEGs were recorded in a standardized way and analyzed independently of the clinical diagnoses, using the SPR method. Results: In receiver operating characteristic curve analyses, the qEEGs separated AD patients from healthy elderly individuals with an area under the curve (AUC) of 0.90, representing a sensitivity of 84% and a specificity of 81%. The qEEGs further separated patients with Lewy body dementia or Parkinson's disease dementia from AD patients with an AUC of 0.9, a sensitivity of 85% and a specificity of 87%. Conclusion: qEEG using the SPR method could be a useful tool in dementia diagnostic workup.
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Kanda, Paulo Afonso de Medeiros, Renato Anghinah, Magali Taino Smidth, and Jorge Mario Silva. "The clinical use of quantitative EEG in cognitive disorders." Dementia & Neuropsychologia 3, no. 3 (2009): 195–203. http://dx.doi.org/10.1590/s1980-57642009dn30300004.
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Abstract 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.
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Mas, F., LS Prichep, R. Cancro, ER John, and K. Alper. "Neurometric-quantitative EEG as a diagnostic adjunct in clinical psychiatry." European Psychiatry 6, no. 3 (1991): 131–39. http://dx.doi.org/10.1017/s0924933800000985.
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SummaryComputer based quantitative evolution of the electroencephalogram (QEEG) holds promise as an adjunct in the evaluation of psychiatric patients. One such method is neurometrics (N-QEEG) in which quantitative electrophysiological features are evaluated by statistical comparison with age appropriate normative data and compared with the profile of dysfunction seen in different psychiatric populations. This paper is based upon the experiences of the senior author in using this method in a series of 88 patients seen in a clinical setting. Neurometric testing provided a unique and significant contribution to the clinical diagnosis or management of 12% of these cases and gave some clinically useful information in another 44% of this population. In the remaining 44%, the method did not provide any additional contribution to the clinical diagnosis and/or to the management of the patient. Six case histories are provided to illustrate these 3 categories. It must be emphasized that N-QEEG is not a technique that can be substituted for any part of a systematic clinical evaluation, least of all for the process itself, which remains crucial. Once embedded in a solid clinical framework, N-QEEG has the capacity to enhance one’s diagnostic efforts and therapeutic strategies by providing objective quantitative data reflecting brain dysfunction. In such a context, the nominative and cost-effective nature of this technique can further adds to its practicality.
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Tedrus, Gloria Maria, Leandro M. Negreiros, Raquel S. Ballarim, Tamires A. Marques, and Lineu Correa Fonseca. "Correlations Between Cognitive Aspects and Quantitative EEG in Adults With Epilepsy." Clinical EEG and Neuroscience 50, no. 5 (2018): 348–53. http://dx.doi.org/10.1177/1550059418793553.
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Introduction. Cognitive impairment frequently occurs in adult patients with epilepsy (PWE), but its pathophysiological basis is not well known. This study assessed cognition and its correlations with quantitative EEG coherence (QEEG) of patients with epilepsy. Method. Eighty PWE seen consecutively in the clinic and 40 normal subjects (NC) were assessed by neurological evaluation, Mini Mental State Examination, immediate and delayed recall of 10 simple figures, phonemic verbal fluency (FAS), category fluency test (VF animals), clock drawing, and QEEG. The mean global inter- and intrahemispheric coherences for the delta, theta, alpha, and beta bands were calculated. Cognitive functions and QEEG coherence of the PWE and the NC were compared, and logistic regression analysis determined the factors associated with impaired cognitive functions in PWE. The significance level was set at P < .05. Results. Regression analysis showed that FAS impairment (14.5 ± 8.6 vs 24.3 ± 15.7, respectively) and delayed recall of figures in PWE (7.3 ± 2.07 vs 8.6 ± 1.48) differed significantly between the PWE and the NC (Nagelkerke R2 = 0.266). Absolute power was greater in all the frequency bands in PWE. Interhemispheric and intrahemispheric beta coherences in the theta frequency was higher in the PWE than in the NC. Logistic regression analysis showed a significant association between interhemispheric delta coherence and VF animal impairment (cutoff point of 12), and between an interhemipheric beta coherence with level of education and delayed recall of figures impairment (cutoff point of 7) (Nagelkerke R2 = 0.297). Other variables were not associated. Conclusions. There was cognitive impairment of PWE and it was significantly associated with QEEG, which suggests that QEEG measures may contribute to the understanding of physiopathological events and as a marker for cognitive alterations in epilepsy.
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Collura, Thomas F. "Quantitative EEG and Live Z-Score Neurofeedback—Current Clinical and Scientific Context." Biofeedback 45, no. 2 (2017): 25–29. http://dx.doi.org/10.5298/1081-5937-45.1.07.
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This article discusses the relevance of quantitative EEG (QEEG) and live z-score training (LZT) to the field of mental health in general, and to neurofeedback in particular. We examine what practitioners might learn about clients when QEEG is used for assessment, and the relevance of LZT as a treatment modality. Clinicians can benefit from viewing the brain as a dynamic system, and this point of view can provide a foundation for QEEG and LZT. This approach emphasizes understanding the value of brain activation as a basis for observed symptoms and behaviors. Of paramount importance are localization and frequency information, as well as connectivity metrics. The brain can be viewed as a complex self-controlled system operating with various identifiable networks and frequencies that, when dysregulated, produce what we commonly refer to as “disorders.”
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Lee, Seungha, Xuelong Zhao, Kathryn A. Davis, Alexis A. Topjian, Brian Litt, and Nicholas S. Abend. "Quantitative EEG predicts outcomes in children after cardiac arrest." Neurology 92, no. 20 (2019): e2329-e2338. http://dx.doi.org/10.1212/wnl.0000000000007504.
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ObjectiveTo determine whether quantitative EEG (QEEG) features predict neurologic outcomes in children after cardiac arrest.MethodsWe performed a single-center prospective observational study of 87 consecutive children resuscitated and admitted to the pediatric intensive care unit after cardiac arrest. Full-array conventional EEG data were obtained as part of clinical management. We computed 8 QEEG features from 5-minute epochs every hour after return of circulation. We developed predictive models utilizing random forest classifiers trained on patient age and 8 QEEG features to predict outcome. The features included SD of each EEG channel, normalized band power in alpha, beta, theta, delta, and gamma wave frequencies, line length, and regularity function scores. We measured outcomes using Pediatric Cerebral Performance Category (PCPC) scores. We evaluated the models using 5-fold cross-validation and 1,000 bootstrap samples.ResultsThe best performing model had a 5-fold cross-validation accuracy of 0.8 (0.88 area under the receiver operating characteristic curve). It had a positive predictive value of 0.79 and a sensitivity of 0.84 in predicting patients with favorable outcomes (PCPC score of 1–3). It had a negative predictive value of 0.8 and a specificity of 0.75 in predicting patients with unfavorable outcomes (PCPC score of 4–6). The model also identified the relative importance of each feature. Analyses using only frontal electrodes did not differ in prediction performance compared to analyses using all electrodes.ConclusionsQEEG features can standardize EEG interpretation and predict neurologic outcomes in children after cardiac arrest.
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Brito, Rodrigo, Adriana Baltar, Marina Berenguer-Rocha, et al. "Intrahemispheric EEG: A New Perspective for Quantitative EEG Assessment in Poststroke Individuals." Neural Plasticity 2021 (September 21, 2021): 1–8. http://dx.doi.org/10.1155/2021/5664647.
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The ratio between slower and faster frequencies of brain activity may change after stroke. However, few studies have used quantitative electroencephalography (qEEG) index of ratios between slower and faster frequencies such as the delta/alpha ratio (DAR) and the power ratio index (PRI; delta + theta / alpha + beta ) for investigating the difference between the affected and unaffected hemisphere poststroke. Here, we proposed a new perspective for analyzing DAR and PRI within each hemisphere and investigated the motor impairment-related interhemispheric frequency oscillations. Forty-seven poststroke subjects and twelve healthy controls were included in the study. Severity of upper limb motor impairment was classified according to the Fugl–Meyer assessment in mild/moderate ( n = 25 ) and severe ( n = 22 ). The qEEG indexes (PRI and DAR) were computed for each hemisphere (intrahemispheric index) and for both hemispheres (cerebral index). Considering the cerebral index (DAR and PRI), our results showed a slowing in brain activity in poststroke patients when compared to healthy controls. Only the intrahemispheric PRI index was able to find significant interhemispheric differences of frequency oscillations. Despite being unable to detect interhemispheric differences, the DAR index seems to be more sensitive to detect motor impairment-related frequency oscillations. The intrahemispheric PRI index may provide insights into therapeutic approaches for interhemispheric asymmetry after stroke.
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Puskás, Szilvia, Norbert Kozák, Dóra Sulina, László Csiba, and Mária Tünde Magyar. "Quantitative EEG in obstructive sleep apnea syndrome: a review of the literature." Reviews in the Neurosciences 28, no. 3 (2017): 265–70. http://dx.doi.org/10.1515/revneuro-2016-0064.
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AbstractObstructive sleep apnea syndrome (OSAS) is characterized by the recurrent cessation (apnea) or reduction (hypopnea) of airflow due to the partial or complete upper airway collapse during sleep. Respiratory disturbances causing sleep fragmentation and repetitive nocturnal hypoxia are responsible for a variety of nocturnal and daytime complaints of sleep apnea patients, such as snoring, daytime sleepiness, fatigue, or impaired cognitive functions. Different techniques, such as magnetic resonance imaging, magnetic resonance spectroscopy, and positron emission tomography, are used to evaluate the structural and functional changes in OSAS patients. With quantitative electroencephalographic (qEEG) analysis, the possible existence of alterations in the brain electrical activity of OSAS patients can be investigated. We review the articles on qEEG results of sleep apnea patients and summarize the possible explanations of these qEEG measures. Finally, we review the impact of continuous positive airway pressure (CPAP) treatment on these alterations to assess whether CPAP use can eliminate alterations in the brain activity of OSAS patients.
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Kaleem, Safa, Jennifer H. Kang, Alok Sahgal, Christian E. Hernandez, and Christa B. Swisher. "4209 Early Electrographic Seizure Detection by Neuro ICU Nurses via Bedside Real-Time Quantitative EEG." Journal of Clinical and Translational Science 4, s1 (2020): 28–29. http://dx.doi.org/10.1017/cts.2020.124.
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OBJECTIVES/GOALS: 1.Determine test characteristics of Neuro ICU nurse interpretation of real-time bedside qEEG for seizure detection2.Determine difference in time to detection of seizures between qEEG interpretation and raw cEEG reads3.Determine how seizure characteristics affect accuracy of qEEG readsMETHODS/STUDY POPULATION: Subjects: Nurses caring for patients admitted to the Neuro ICU at Duke University Hospital who are initiated on cEEG.Nurses evaluate qEEG display at the bedside on an hourly basis after undergoing a standardized qEEG training session. The standard practice of independent review of cEEG and treatment by the Neuro ICU team remains unchanged.Post-hoc review of cEEG data by two blinded, board-certified neurophysiologists will be performed for each patient. The raw cEEG data will be scored for the number of seizures present per hour, background, seizure duration, and seizure spatial extent.The time from first seizure occurrence to clinical recognition will be recorded.RESULTS/ANTICIPATED RESULTS: Thus far, 91 patients with 583 1-hour blocks of nurse interpretations have been studied, with 6 patients experiencing seizures while on study. Enrollment will be completed on 1/17/20Preliminary data show a sensitivity of 95.8% (79.9%, 99.9%), specificity of 95.2 (93.1%, 96.8%), positive predictive value of 46.0% (36.9%, 55.4%), negative predictive value of 99.8% (98.7%, 99.9%), positive likelihood ratio of 19.8 (13.6, 28.9), negative likelihood ratio (0.04 (0.01, 0.3). All confidence intervals are 95%. False alarm rate is 0.05/hour.Further analyses are pending completion of enrollment in January 2020.DISCUSSION/SIGNIFICANCE OF IMPACT: Nurse interpretation of real-time bedside qEEG for seizure detection is feasible in the Duke Neuro ICU. QEEG functions well as a screening tool with good specificity and low false alarm rate. Use of qEEG by nurses could lead to shorter time to seizure detection, which may improve patient outcomes. CONFLICT OF INTEREST DESCRIPTION: Safa Kaleem, BS: Research reported in this publication was supported by a Pfizer Foundation grant and the Duke Clinical Translational Science Institute (CTSI). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Pfizer Foundation or the Duke CTSI. Jennifer H. Kang, MD: None to declare. Alok Sahgal, MD: None to declare. Christa B. Swisher, MD: Received speaker’s honorarium from EISAI and UCB.
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Duffy, Frank H., John R. Hughes, Fernando Miranda, Peter Bernad, and Patricia Cook. "Status of Quantitative EEG (QEEG) in Clinical Practice, 1994." Clinical Electroencephalography 25, no. 4 (1994): vi—xxii. http://dx.doi.org/10.1177/155005949402500403.
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Arns, Martijn, and Evian Gordon. "Quantitative EEG (QEEG) in psychiatry: Diagnostic or prognostic use?" Clinical Neurophysiology 125, no. 8 (2014): 1504–6. http://dx.doi.org/10.1016/j.clinph.2014.01.014.
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Reeves, Roy R., Frederick A. Struve, and Gloria Patrick. "Topographic Quantitative EEG Response to Acute Caffeine Withdrawal: A Comprehensive Analysis of Multiple Quantitative Variables." Clinical Electroencephalography 33, no. 4 (2002): 178–88. http://dx.doi.org/10.1177/155005940203300409.
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Most previous studies of the neurophysiological effects of caffeine have focused on the effects of caffeine ingestion, and few studies have examined the effects of caffeine withdrawal. This open study evaluated the quantitative EEG (QEEG) changes occurring during a 4-day period of abstinence in subjects who habitually consume 300 mg or more of caffeine daily. Thirteen subjects underwent QEEG studies during their usual caffeine consumption (baseline) and on days 1,2, and 4 of a 4-day period of caffeine abstinence. Ten of the subjects underwent a second QEEG on day 4 that consisted of a period of recording after reinstitution of caffeine. A comprehensive analysis of multiple quantitative variables was performed for each study during the abstinence period and compared to the variables obtained at baseline for each subject. Changes occurring during caffeine abstinence included: 1) increases in theta absolute power over all cortical areas, 2) increases in delta absolute power over the frontal cortex, 3) decreases in the mean frequency of both the alpha and beta rhythm, 4) increase in theta relative power and decrease in beta relative power, and 5) significant changes in interhemispheric coherence. Most of these changes tended to return to pre-abstinence baseline levels rapidly after resumption of caffeine consumption. The caffeine withdrawal state affects a number of neurophysiological variables. Further investigation of the neurophysiological aspects of caffeine withdrawal using placebo controlled double blind assessment methods is warranted.
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Rodrigues, T., and I. Saavedra. "The Role of Quantitative Electroencephalography in Psychiatry." European Psychiatry 24, S1 (2009): 1. http://dx.doi.org/10.1016/s0924-9338(09)70935-6.
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Background:The more recent advances in the field of Neurosciences include the study of abnormal pathophysiological findings in patients suffering from Psychiatric disorders. Several new brain imaging technologies are conveying numerous data concerning structural and functional abnormalities in such patients.Aims:To provide an overview of the role of quantitative Electroencephalography (qEEG) in Psychiatry.Methods:Review of the literature.Results:Among the various imaging studies, the application of three-dimensional qEEG may be the most practical and economic alternative. The qEEG consists in the statistical analysis of the EEG parameters, with computer-treated data. It is a portable, radiation-free, non-invasive method that measures excitatory and inhibitory cortical neuronal activity directly.Conclusion:The latest literature regarding the potential role of the qEEG in Psychiatry debates its applicability in clinical settings. Several authors have been trying to evaluate its usefulness in clinical diagnosis and prediction of response to medication. We review the possible recommendations for the use of this test and the controversies surrounding them.
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Nitika Verma*,Gyanendra Kumar,B. D. Singh, Amit Kumar. "Comparative study of electroencephalographic findings in patients with depression and schizophrenia." Innovative Journal of Medical and Health Science 9, no. 8 (2019): 593–603. http://dx.doi.org/10.15520/ijmhs.v9i8.2633.
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Introduction: Schizophrenia and depression are two major and very common mentaldisorders worldwide. Many clinical questionnaire and scales have been formulated todiagnose these cases. Standard (qualitative) electroencephalography (EEG) has alsobeen routinely employed in the diagnostic evaluation of patients of these disorders.However quantitative EEG (qEEG) has not been frequently used in evaluation of thesedisorders. Qualitative EEG (qEEG) findings differ between patients with schizophreniaand patients with depression, but results are not consistent. We performed this study todetermine the differences in EEG parameters(qualitative and quantitative) betweenpatients with schizophrenia, patients with depression, and healthy subjects.Materials and Methods: Our study included 69 patients with schizophrenia, 69patients with depression, and 138 healthy subjects. All patients and healthy subjectswere aged between 18-50 years. All clinical diagnoses were made according to DSM-IVdiagnostic criteria. Standard EEG was performed on all study participants and artefact-free 100-second epochs were selected from the recorded material and analysed withFast Fourier Transformation (FFT) analysis.Observations and Result: Of the entire sample of patients, 40.58% had abnormal EEG.Patients with schizophrenia when compared with healthy subjects showed increaseddelta, theta, and beta activity and decreased alpha activity. Patients with depression alsoshowed similar results, but in fewer regions. In patients with schizo-phrenia, deltapower over some frontal regions was increased in comparison with those in patientswith depression. Schizophrenic patients and healthy subjects showed interhemisphericasymmetry, but it was absent in patients with depression.Conclusion: Abnormal EEG findings in patients with schizophrenia and depression isnot very common. However, on qEEG, the patients with schizophrenia had in-creaseddelta power in comparison to patients with depression. Hence, qEEG may have a rolein clinical differentiation between these two mental disorders and it may be especiallyimportant in cases of negative-symptom schizophrenia.Key words: Schizophrenia, Depression, quantitative electroencephalography(qEEG).
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Priyavathani J., Bruntha, Sriram Pothapregada, Anuradha Varadhan, and Suresh C. Thirunavukarasu. "A study of prevalence of abnormal EEG and its association between various clinical presentations of atypical febrile seizures." International Journal of Contemporary Pediatrics 8, no. 1 (2020): 120. http://dx.doi.org/10.18203/2349-3291.ijcp20205517.
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Background: Quantitative EEG is a rapidly emerging tool in the diagnosis and follow up of various neurological disorders and can act as predictive marker for subsequent epilepsy in children with complex febrile seizure. The present study aimed to estimate the prevalence of abnormal electroencephalogram (EEG) and to find the association between Quantitative EEG (qEEG) and various clinical features of atypical febrile seizures(AFS).Methods: EEG was recorded along with clinical features including the age at onset, duration of episode, number of episodes in a day, type of seizure and the recurrences from the children aged between 6-60 months with atypical febrile seizures. EEG recordings were classified into Normal and abnormal EEG with epileptiform changes by the expert interpretation and the distribution of above said clinical features in the both groups were analyzed. It is also attempted to find the association between qEEG and few of the clinical features.Results: Prevalence of abnormal EEG in atypical febrile seizures was 33.9%. There were no significant differences in the distribution of abnormal EEG and their association with various clinical features of AFS. Significantly increased absolute power of θ and α waves were recorded from the frontal montages in the children with epileptiform changes in the EEG.Conclusions: qEEG changes can be also considered as marker of severity of febrile seizure episodes. Many prospective studies with long-term follow up are required to establish the predictability of future epilepsy by qEEG.
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Calzada-Reyes, Ana, Alfredo Alvarez-Amador, Lídice Galán-García, and Mitchell Valdés-Sosa. "Sex Differences in QEEG in Psychopath Offenders." Clinical EEG and Neuroscience 51, no. 3 (2019): 146–54. http://dx.doi.org/10.1177/1550059419872414.
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Introduction. Functional brain differences related to sex in psychopathic behavior represent an important field of neuroscience research; there are few studies on this area, mainly in offender samples. Objective. The aim of this study was to investigate the presence of electrophysiological differences between male and female psychopath offenders; specifically, we wanted to assess whether the results in quantitative EEG, low-resolution electromagnetic tomography (LORETA), and changes in synchronous brain activity could be related to sex influence. Sample and Methods. The study included 31 male and 12 female psychopath offenders, according to the Hare Psychopathy Checklist–Revised criteria from 2 prisons located in Havana City. The EEG visual inspection characteristics and the use of frequency domain quantitative analysis techniques are described. Results. The resting EEG visual analyses revealed a high percentage of EEG abnormalities in both studied groups. Significant statistical differences between the mean parameters of cross spectral measures between psychopathic offender groups were found in the beta band at bilateral frontal derivation and centroparietal areas. LORETA showed differences especially in the paralimbic and parieto-occipital areas Synchronization likelihood revealed a significant group effect in the 26 to 30 Hz band. These results indicate that combining quantitative EEG, LORETA analysis, and synchronization likelihood may improve the neurofunctional differentiation between psychopath offenders of both sexes.
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Ianof, Jéssica Natuline, and Renato Anghinah. "Traumatic brain injury: An EEG point of view." Dementia & Neuropsychologia 11, no. 1 (2017): 3–5. http://dx.doi.org/10.1590/1980-57642016dn11-010002.
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ABSTRACT Traumatic brain injury (TBI) is a silent epidemic. Mild traumatic brain injury (mTBI) causes brain injury that results in electrophysiologic abnormalities visible on electroencephalography (EEG) recordings. The purpose of this brief review was to discuss the importance of EEG findings in traumatic brain injury. Relevant articles published during the 1996-2016 period were retrieved from Medline (PubMed). The keywords were in English and included "traumatic brain injury", "EEG" and "quantitative EEG". We found 460 articles, analyzed 52 and selected 13 articles. EEG after TBI shows slowing of the posterior dominant rhythm and increased diffuse theta slowing, which may revert to normal within hours or may clear more slowly over many weeks. There are no clear EEG or quantitative EEG (qEEG) features unique to mild traumatic brain injury. Although the literature indicates the promise of qEEG in reaching a diagnosis and indicating prognosis of mTBI, further study is needed to corroborate and refine these methods.
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Sneddon, Robert, William Rodman Shankle, Junko Hara, Anthony Rodriquez, Donald Hoffman, and Utpal Saha. "EEG Detection of Early Alzheimer's Disease Using Psychophysical Tasks." Clinical EEG and Neuroscience 36, no. 3 (2005): 141–50. http://dx.doi.org/10.1177/155005940503600304.
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In this study, we hypothesized that a quantitative EEG (qEEG) method for measuring EEG variability combined with specific psychophysical tasks could improve the classification accuracy of subjects with normal aging vs. mild cognitive impairment (MCI) or mild dementia due to Alzheimer's Disease and Related Disorders (ADRD). The cross-sectional sample consisted of 48 subjects (32 normal aging and 16 ADRD: n=3 mild dementia, n=13 MCI FAST stage 3). During EEG recording, subjects performed two visual, delayed recognition memory tasks as well as a task that tested their ability to perceive structure-from-motion (SFM). These EEG data were used to compute qEEG measures of the (normalized) variance of posterior cortical activity during the first 150 milliseconds (ms) after stimulus onset and the variance of anterior cortical activity during the second 150 ms epoch. The ratio, anterior/posterior cerebral qEEG value, was then computed for each subject, and the optimal cutoff value identified to discriminate normal from impaired subjects. An optimal qEEG cutoff value for the delayed recognition memory tasks correctly discriminated 30 of the 32 normal aging subjects (94% specificity) and 14 of 16 MCI-to-mild ADRD subjects (88% sensitivity). On the other hand, the application of this qEEG measure to EEG data recorded while subjects performed a SFM task did not distinguish between ADRD and normal aging any better than chance. In conclusion, this qEEG measure is specific to the psychophysical task being performed by the subject. When it was combined with delayed recognition memory tasks, it yielded results that are comparable to the accuracies reported by PET scan studies of normal aging vs. AD with mild cognitive impairment. These results warrant further evaluation.
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Winterer, G., B. Klöppel, and W. M. Herrmann. "Quantitative EEG (QEEG) predicts relapse in patients with chronic alcoholism." Electroencephalography and Clinical Neurophysiology 102, no. 4 (1997): P57. http://dx.doi.org/10.1016/s0013-4694(97)85327-8.
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Ostfeld, Barbara, David Mandelbaum, Stephen Kugler, Donna Valice, Mark Hiatt, and Thomas Hegyi. "NEONATAL QUANTITATIVE EEG(qEEG) FINDINGS AFTER INTRAUTERINE DRUG EXPOSURE† 993." Pediatric Research 41 (April 1997): 168. http://dx.doi.org/10.1203/00006450-199704001-01012.
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Winterer, G., B. Klöppel, A. Heinz, M. Ziller, L. G. Schmidt, and W. M. Herrmann. "Quantitative EEG (QEEG) predicts relapse in patients with chronic alcoholism." European Neuropsychopharmacology 6 (June 1996): 204. http://dx.doi.org/10.1016/0924-977x(96)88258-7.
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Guner, Derya, Bedile Irem Tiftikcioglu, Nilgun Tuncay, and Yasar Zorlu. "Contribution of Quantitative EEG to the Diagnosis of Early Cognitive Impairment in Patients With Idiopathic Parkinson’s Disease." Clinical EEG and Neuroscience 48, no. 5 (2016): 348–54. http://dx.doi.org/10.1177/1550059416662412.
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Cognitive dysfunction can emerge during the clinical course of Parkinson’s disease (PD) even beginning in early stages, which requires extended neuropsychological tests for diagnosis. There is need for rapid, feasible, and practical tests in clinical practice to diagnose and monitor the patients without causing any discomfort. We investigated the utility of quantitative analysis of digital EEG (qEEG) for diagnosing subtle cognitive impairment in PD patients without evident cognitive deficits (ie, “normal cognition”). We enrolled 45 patients with PD and age- matched 39 healthy controls in the study. All participants had Mini-Mental State Examination (MMSE) score greater than 25. qEEG analysis and extensive neuropsychological assessment battery were applied to all participants. Test scores for frontal executive functions, verbal memory processes, attention span, and visuospatial functions were significantly lower than healthy controls ( P < .01). qEEG analysis revealed a significant increase in delta, theta, and beta frequencies, and decrease in alpha frequency band in cerebral bioelectrical activity in patient group. In addition, power spectral ratios ([alpha + beta] / [delta + theta]) in frontal, central, temporal, parietal, and occipital regions were significantly decreased in patients compared with the controls. The slowing in EEG was moderately correlated with MMSE scores ( r = 0.411-0.593; P < .01). However, qEEG analysis and extensive neuropsychological assessment battery were only in weak correlation ( r = 0.230-0.486; P < .05). In conclusion, qEEG analysis could increase the diagnostic power in detecting subtle cognitive impairment in PD patients without evident cognitive deficit, perhaps years before the clinical onset of dementia.
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Costa, Laura, James E. Arruda, Robert A. Stern, Jessica A. Somerville, and Dominic Valentino. "Asymptomatic HIV-Infected Women: Preliminary Study of Quantitative EEG Activity and Performance on a Continuous Performance Test." Perceptual and Motor Skills 85, no. 3_suppl (1997): 1395–408. http://dx.doi.org/10.2466/pms.1997.85.3f.1395.
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Several studies have examined the electrophysiological correlates of human immunodeficiency virus (HIV) infection in medically asymptomatic men. Although the rates of HIV infection are increasing at a greater rate in women than men, there have been no publications to date of electrophysiological functioning in HIV-infected women. In the present study, quantitative electroencephalographic (qEEG) activity was measured in 22 women (11 asymptomatic HIV-seropositive and 11 HIV-seronegative) utilizing a procedure comprised of three auditory continuous performance tests and a set of qEEG components derived from principal components analysis. No significant group differences were found in qEEG or in performance on the continuous performance tests; however, task-related differences were detected across groups between simple and complex language tasks in EEG fast beta power, delta power, and a left-hemisphere principal components analysis-derived EEG component. In examining the electrophysiological correlates of HIV infection, researchers might employ a similar methodology while increasing the sample size and varying the task modality or difficulty.
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Schjønning Nielsen, Malene, Anja Hviid Simonsen, Volkert Siersma, et al. "Quantitative Electroencephalography Analyzed by Statistical Pattern Recognition as a Diagnostic and Prognostic Tool in Mild Cognitive Impairment: Results from a Nordic Multicenter Cohort Study." Dementia and Geriatric Cognitive Disorders Extra 8, no. 3 (2018): 426–38. http://dx.doi.org/10.1159/000490788.
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Aim: To examine diagnostic and prognostic potential of quantitative electroencephalography (qEEG) analyzed by the statistical pattern recognition (SPR) method in patients with cognitive impairment. We compared the differential diagnostic ability of SPR to visual EEG analysis. Correlation between SPR findings and cerebrospinal fluid (CSF) Alzheimer disease (AD) biomarkers were evaluated. Methods: It is a multicenter cohort study involving 129 patients, (mild cognitive impairment [MCI], AD, and healthy controls). Standardized EEG was performed at baseline. Patients were continuously clinically evaluated. Results: Receiver Operating Characteristic curves showed a low discriminative ability of SPR and no ability to predict clinical progression in patients with MCI. Moderate correlation between SPR analysis and CSF AD biomarkers was found. Conclusion: The diagnostic and prognostic abilities of qEEG were low. The SPR method was superior to the visual EEG analysis. The qEEG method correlates well to CSF AD biomarkers, suggesting association with pathology in AD.
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Wutzl, Betty, Stefan M. Golaszewski, Kenji Leibnitz, et al. "Narrative Review: Quantitative EEG in Disorders of Consciousness." Brain Sciences 11, no. 6 (2021): 697. http://dx.doi.org/10.3390/brainsci11060697.
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In this narrative review, we focus on the role of quantitative EEG technology in the diagnosis and prognosis of patients with unresponsive wakefulness syndrome and minimally conscious state. This paper is divided into two main parts, i.e., diagnosis and prognosis, each consisting of three subsections, namely, (i) resting-state EEG, including spectral power, functional connectivity, dynamic functional connectivity, graph theory, microstates and nonlinear measurements, (ii) sleep patterns, including rapid eye movement (REM) sleep, slow-wave sleep and sleep spindles and (iii) evoked potentials, including the P300, mismatch negativity, the N100, the N400 late positive component and others. Finally, we summarize our findings and conclude that QEEG is a useful tool when it comes to defining the diagnosis and prognosis of DOC patients.
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Goenka, Ajay, Alexis Boro, and Elissa Yozawitz. "Comparative sensitivity of quantitative EEG (QEEG) spectrograms for detecting seizure subtypes." Seizure 55 (February 2018): 70–75. http://dx.doi.org/10.1016/j.seizure.2018.01.008.
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Iosifescu, Dan V. "Prediction of Response to Antidepressants: Is Quantitative EEG (QEEG) an Alternative?" CNS Neuroscience & Therapeutics 14, no. 4 (2008): 263–65. http://dx.doi.org/10.1111/j.1755-5949.2008.00063.x.
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Linardakis, Alexandra, and Randy I. Pardell. "The integration of Quantitative EEG (QEEG) brain-mapping with rTMS therapy." Brain Stimulation 11, no. 6 (2018): e11. http://dx.doi.org/10.1016/j.brs.2018.07.009.
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Sand, T., and M. Bjørgaas. "P211 Quantitative EEG (QEEG) in children: Comparison of two activation methods." Electroencephalography and Clinical Neurophysiology 99, no. 4 (1996): 322. http://dx.doi.org/10.1016/0013-4694(96)88337-4.
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Ferreira, Daniel, Vesna Jelic, Lena Cavallin, et al. "Electroencephalography Is a Good Complement to Currently Established Dementia Biomarkers." Dementia and Geriatric Cognitive Disorders 42, no. 1-2 (2016): 80–92. http://dx.doi.org/10.1159/000448394.
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Background/Aims: Dementia biomarkers that are accessible and easily applicable in nonspecialized clinical settings are urgently needed. Quantitative electroencephalography (qEEG) is a good candidate, and the statistical pattern recognition (SPR) method has recently provided promising results. We tested the diagnostic value of qEEG-SPR in comparison to cognition, structural imaging, and cerebrospinal fluid (CSF) biomarkers. Methods: A total of 511 individuals were recruited from the multicenter NORD EEG study [141 healthy controls, 64 subjective cognitive decline, 124 mild cognitive impairment, 135 Alzheimer's disease (AD), 15 dementia with Lewy bodies/Parkinson's disease with dementia (DLB/PDD), 32 other dementias]. The EEG data were recorded in a standardized way. Structural imaging data were visually rated using scales of atrophy in the medial temporal, frontal, and posterior cortex. Results: qEEG-SPR outperformed structural imaging, cognition, and CSF biomarkers in DLB/PDD diagnosis, outperformed structural imaging in AD diagnosis, and improved the differential diagnosis of AD. In addition, qEEG-SPR allowed differentiation of two clinically different AD subtypes. Conclusion: Adding qEEG to the diagnostic workup substantially increases the detection of AD pathology even in pre-dementia stages and improves differential diagnosis. EEG could serve as a good complement to currently established dementia biomarkers since it is cheap, noninvasive, and extensively applied outside academic centers.
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Bjørk, MH, and T. Sand. "Quantitative EEG Power and Asymmetry Increase 36 h Before a Migraine Attack." Cephalalgia 28, no. 9 (2008): 960–68. http://dx.doi.org/10.1111/j.1468-2982.2008.01638.x.
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The aim was to estimate ictal, pre- and postictal brain function changes in migraine in a blinded paired quantitative EEG (QEEG) study. EEG recordings ( n = 119) from 40 migraineurs were retrospectively classified as ictal, interictal, preictal or postictal. δ, θ, α and β power, and hemispheric asymmetry in frontocentral, temporal and occipitoparietal regions were calculated from artefact-free EEG. Power and power asymmetry were calculated for two time-windows, 36 and 72 h before/after the attack, and compared with the interictal values. Frontocentral δ power increased ( P = 0.03), whereas frontocentral θ and α power tended to increase ( P < 0.09) within 36 h before the next attack compared with the interictal period. Occipitoparietal (α and θ) and temporal (α) power were more asymmetric before the attack compared with the interictal baseline ( P < 0.04). Ictal posterior a power increased slightly ( P = 0.01). Postictal power and power asymmetry were not significantly different from interictal baseline. EEG activity seems to change shortly before the attack. This suggests that migraineurs are most susceptible to attack when anterior QEEG δ power and posterior α and θ asymmetry values are high. Changed activity patterns in cholinergic brainstem or basal forebrain nuclei and thalamo-cortical connections before the migraine attack are hypothesized.
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Herron, Katherine, Derk-Jan Dijk, Philip Dean, Ellen Seiss, and Annette Sterr. "Quantitative Electroencephalography and Behavioural Correlates of Daytime Sleepiness in Chronic Stroke." BioMed Research International 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/794086.
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Sleepiness is common after stroke, but in contrast to its importance for rehabilitation, existing studies focus primarily on the acute state and often use subjective sleepiness measures only. We used quantitative electroencephalography (qEEG) to extract physiological sleepiness, as well as subjective reports, in response to motor-cognitive demand in stroke patients and controls. We hypothesised that (a) slowing of the EEG is chronically sustained after stroke; (b) increased power in lower frequencies and increased sleepiness are associated; and (c) sleepiness is modulated by motor-cognitive demand. QEEGs were recorded in 32 chronic stroke patients and 20 controls using a Karolinska Drowsiness Test protocol administered before and after a motor priming task. Subjective sleepiness was measured using the Karolinska Sleepiness Scale. The findings showed that power density was significantly increased in delta and theta frequency bands over both hemispheres in patients which were not associated with subjective sleepiness ratings. This effect was not observed in controls. The motor priming task induced differential hemispheric effects with greater increase in low-frequency bands and presumably compensatory increases in higher frequency bands. The results indicate sustained slowing in the qEEG in chronic stroke, but in contrast to healthy controls, these changes are not related to perceived sleepiness.
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Deslandes, Andréa, Heloisa Veiga, Mauricio Cagy, Adriana Fiszman, Roberto Piedade, and Pedro Ribeiro. "Quantitative electroencephalography (qEEG) to discriminate primary degenerative dementia from major depressive disorder (depression)." Arquivos de Neuro-Psiquiatria 62, no. 1 (2004): 44–50. http://dx.doi.org/10.1590/s0004-282x2004000100008.
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Electroencephalography (EEG) can be a valuable technique to assess electrophysiological changes related to dementia. In patients suspected of having dementia, the EEG is often quite informative. The sensitivity of the EEG to detect correlates of psychiatric disorders has been enhanced by means of quantitative methods of analysis (quantitative EEG). Quantitative features are extracted from, at least, 2 minutes of artifact-free, eyes closed, resting EEG, log-transformed to obtain Gaussianity, age-regressed, and Z-transformed relative to population norms (Neurometrics database). Using a subset of quantitative EEG (qEEG) features, forward stepwise discriminant analyses are used to construct classifier functions. Along this vein, the main objective of this experiment is to distinguish profiles of qEEG, which differentiate depressive from demented patients (n = 125). The results showed that demented patients present deviations above the control group in variables associated to slow rhythms: Normed Monopolar Relative Power Theta for Cz and Normed Bipolar Relative Power Theta for Head. On the other hand, the deviation below the control group occurs with the variable associated to alpha rhythm: Normed Monopolar Relative Power Alpha for P3, in dementia. Using this method, the present investigation demonstrated high discriminant accuracy in separating Primary Degenerative Dementia from Major Depressive Disorder (Depression).
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Calzada-Reyes, Ana, Alfredo Alvarez-Amador, Lídice Galán-García, and Mitchell Valdés-Sosa. "QEEG and LORETA in Teenagers With Conduct Disorder and Psychopathic Traits." Clinical EEG and Neuroscience 48, no. 3 (2016): 189–99. http://dx.doi.org/10.1177/1550059416645712.
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Background. Few studies have investigated the impact of the psychopathic traits on the EEG of teenagers with conduct disorder (CD). To date, there is no other research studying low-resolution brain electromagnetic tomography (LORETA) technique using quantitative EEG (QEEG) analysis in adolescents with CD and psychopathic traits. Objective. To find electrophysiological differences specifically related to the psychopathic traits. The current investigation compares the QEEG and the current source density measures between adolescents with CD and psychopathic traits and adolescents with CD without psychopathic traits. Methods. The resting EEG activity and LORETA for the EEG fast spectral bands were evaluated in 42 teenagers with CD, 25 with and 17 without psychopathic traits according to the Antisocial Process Screening Device. All adolescents were assessed using the DSM-IV-TR criteria. The EEG visual inspection characteristics and the use of frequency domain quantitative analysis techniques (narrow band spectral parameters) are described. Results. QEEG analysis showed a pattern of beta activity excess on the bilateral frontal-temporal regions and decreases of alpha band power on the left central-temporal and right frontal-central-temporal regions in the psychopathic traits group. Current source density calculated at 17.18 Hz showed an increase within fronto-temporo-striatal regions in the psychopathic relative to the nonpsychopathic traits group. Conclusions. These findings indicate that QEEG analysis and techniques of source localization may reveal differences in brain electrical activity among teenagers with CD and psychopathic traits, which was not obvious to visual inspection. Taken together, these results suggest that abnormalities in a fronto-temporo-striatal network play a relevant role in the neurobiological basis of psychopathic behavior.
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Lubińska-Kościółek, Elżbieta, Jolanta Zielińska, and Krzysztof Wołoszczuk. "Psychological diagnosis and quantitative electroencephalography analysis in cognitive rehabilitation for people with Down syndrome." Hrvatska revija za rehabilitacijska istraživanja 54, no. 2 (2019): 39–48. http://dx.doi.org/10.31299/hrri.54.2.4.
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U radu se predstavlja mogućnost primjene QEEG-a u psihološkoj obradi osoba s Downovim sindromom. Primijenjeni su testovi Mitsar EEG 202 i SON-R (2.5-7). U ispitivanju je sudjelovalo dvadeset ispitanika s Downovim sindromom. Detaljno su analizirani rezultati osamnaestogodišnje muške osobe. Rezultati su uputili na prenaglašenu ekspresivnost, posebice theta valova (4-6 Hz) registriranih u čeonom i tjemenom režnju. Podaci prikupljeni tijekom psihološke obrade potvrđuju iste rezultate QEEG-a kod pacijenta. Također iznose se pretpostavke neurofeedback-treninga i tipova zadataka koji bi se trebali upotrebljavati u kognitivnoj rehabilitaciji.
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Yadollahpour, Ali, and Hadi Nasrollahi. "Quantitative Electroencephalography for Objective and Differential Diagnosis of Depression: A Comprehensive Review." Global Journal of Health Science 8, no. 11 (2016): 249. http://dx.doi.org/10.5539/gjhs.v8n11p249.
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<p>Quantitative electroencephalography (QEEG) has been dramatically developed during recent years in cognitive neurosciences. It has shown significant potential in the diagnosis of cognitive neurological disorders as well as in the evaluation of treatment outcomes and response. Early diagnosis of depression, differential diagnosis, and assessing the treatment outcomes and response are currently the main research fields of QEEG in depression. Identifying reliable disorder-specific EEG-based biomarkers that have strong correlations with the depression specific cognitive functions is one of the major challenges in these fields. Such biomarkers not only allow early and cost-effective diagnosis of depression, but also may have differential diagnostic and predictive values for treatment response of a variety of treatments. This paper aims at a comprehensive review on the main principles of QEEG in developing biomarkers for MDD. The databases of PubMed (1985-2015), Web of Sciences (1985-2015), and Google Scholar (1980-2015) were searched using the set terms. The obtained results were screened for the title and abstract by two authors and they came to consensus whether the studies are related to the review. The main advantages of QEEG for mood disorders are also reviewed. In addition, different QEEG-based measures for objective diagnosis of MDD as well as for distinguishing depressed patients from healthy subjects are discussed.</p>
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Collura, Thomas F. "The Quantitative Electroencephalogram and the Use of Normative Databases." Biofeedback 47, no. 2 (2019): 26–35. http://dx.doi.org/10.5298/1081-5937-47.1.01.
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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.
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Yüksel, Merve, Şehri Ayaş, Mehmet Tuğrul Cabıoğlu, Derya Yılmaz, and Cağrı Cabıoğlu. "Quantitative Data for Transcutaneous Electrical Nerve Stimulation and Acupuncture Effectiveness in Treatment of Fibromyalgia Syndrome." Evidence-Based Complementary and Alternative Medicine 2019 (March 4, 2019): 1–12. http://dx.doi.org/10.1155/2019/9684649.
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Aim. To evaluate the effects of acupuncture and transcutaneous electric nerve stimulation (TENS) applications on the quantitative electroencephalography (qEEG) changes and to evaluate their therapeutic effects in patients with fibromyalgia syndrome (FMS). The study included 42 patients with FMS and 21 healthy volunteers. The patients were randomly assigned to two groups (n=21 in each) to undergo either TENS or acupuncture application. In both acupuncture and TENS groups, baseline electroencephalography (EEG) recording was performed for 10 min and, then, TENS or acupuncture was performed for 20 min, followed by another 10 min EEG recording. Baseline qEEG findings of FMS patients in the TENS and acupuncture groups were similar. Delta and theta powers over the frontal region of FMS patients were lower than controls. Theta powers of right posterior region were also lower than controls. In the TENS group, after the treatment, an increase was observed in the alpha power of the left anterior region as well as a decrease in pain scores. In the acupuncture group, an increase was determined in the alpha power of the right and left posterior regions as well as a decrease in pain score after the treatment. The power of low- and moderate-frequency waves on resting EEG was decreased in the patients with FMS. Decreased pain and increased inhibitor activity were found on qEEG after TENS and acupuncture applications. In conclusion, both TENS and acupuncture applications seem to be beneficial in FMS patients.