The Role of Quantitative EEG in the Diagnosis of Neuropsychiatric Disorders

Popa, Livia; Dragos, Hanna; Pantelemon, Cristina; Rosu, Olivia Verisezan; Strilciuc, Ștefan

doi:10.25122/jml-2019-0085

Cited by 67 publications

(35 citation statements)

References 86 publications

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“…Increased delta and theta absolute power in the right hemisphere and increased theta power in the posterior cerebral areas are QEEG characteristics associated with patients with depression, and these features reflect decreased regional cortical activity in the chronic stage of depression (Grin-Yatsenko et al, 2009 ; Livint Popa et al, 2020 ). Beta power enhancement in the frontal region has been correlated with early stage of depressive or anxiety symptoms, suggesting that EEG and metabolic activities are increased in this area (Grin-Yatsenko et al, 2009 ).…”

Section: Discussionmentioning

confidence: 99%

“…In our study, the beta 3 relative power was lower in both subject groups than in healthy controls and was even less in ESRD patients with MICS; this finding may indicate MCI in both subject groups, and comorbid MICS may increase the severity of MCI. Excessive delta and theta activity was the most common feature of both acute UE and MCI in adult ESRD patients (Amodio et al, 1999 ; Newson and Thiagarajan, 2019 ; Livint Popa et al, 2020 ). Higher delta activity was also a distinctive marker in the development of MCI due to cerebrovascular complications in ESRD patients (Lizio et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

“…Then, QEEG utilized spectral analysis values that were transformed to Z-scores by referencing a demographically (age, gender, and handedness) matched normative database (Thatcher et al, 2003 ). QEEG also allows neurologists to reliably and conveniently compare resting-state electrophysiological patterns between individuals (Livint Popa et al, 2020 ). Another method is microstate analysis, which assumes that there are quasistable EEG topographies known as microstates that represent different cognitive phenomena in the brain (Poulsen et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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QEEG characteristics associated with malnutrition-inflammation complex syndrome

Jatupornpoonsub

Thimachai²,

Supasyndh³

et al. 2023

Front. Hum. Neurosci.

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End-stage renal disease (ESRD) has been linked to cerebral complications due to the comorbidity of malnutrition and inflammation, which is referred to as malnutrition-inflammation complex syndrome (MICS). The severity of this condition is clinically assessed with the malnutrition-inflammation score (MIS), and a cutoff of five is used to optimally distinguish patients with and without MICS. However, this tool is still invasive and inconvenient, because it combines medical records, physical examination, and laboratory results. These steps require clinicians and limit MIS usage on a regular basis. Cerebral diseases in ESRD patients can be evaluated reliably and conveniently by using quantitative electroencephalogram (QEEG), which possibly reflects the severity of MICS likewise. Given the links between kidney and brain abnormalities, we hypothesized that some QEEG patterns might be associated with the severity of MICS and could be used to distinguish ESRD patients with and without MICS. Hence, we recruited 62 ESRD participants and divided them into two subgroups: ESRD with MICS (17 women (59%), age 60.31 ± 7.79 years, MIS < 5) and ESRD without MICS (20 women (61%), age 62.03 ± 9.29 years, MIS ≥ 5). These participants willingly participated in MIS and QEEG assessments. We found that MICS-related factors may alter QEEG characteristics, including the absolute power of the delta, theta, and beta 1 bands, the relative power of the theta and beta 3 subbands, the coherence of the delta and theta bands, and the amplitude asymmetry of the beta 1 band, in certain brain regions. Although most of these QEEG patterns are significantly correlated with MIS, the delta absolute power, beta 1 amplitude asymmetry, and theta coherence are the optimal inputs for the logistic regression model, which can accurately classify ESRD patients with and without MICS (90.0 ± 5.7% area under the receiver operating characteristic curve). We suggest that these QEEG features can be used not only to evaluate the severity of cerebral disorders in ESRD patients but also to noninvasively monitor MICS in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

QEEG characteristics associated with malnutrition-inflammation complex syndrome

Jatupornpoonsub

Thimachai²,

Supasyndh³

et al. 2023

Front. Hum. Neurosci.

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“…Despite much advancement in electrodiagnostic techniques and wide acceptance in seizure diagnosis, EEG is yet to attain high sensitivity and specificity in seizure diagnostic potential [ 1 ]. Computer-added quantitative EEG (qEEG), which has evolved recently, can add value to the potential of EEG in seizure diagnostics [ 2 ]. Pediatric seizures place further diagnostic and prognostic challenges owing to inconspicuous signs and symptoms and atypical EEG features associated with them.…”

Section: Introductionmentioning

confidence: 99%

The Electroencephalographic Characterization of Hypsarrhythmia in Older Pediatric Population With Epilepsy Using Computer-Added Quantitative Methods

Jha¹,

Kumar²,

Zabihullah³

et al. 2023

Cureus

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BackgroundHypsarrhythmia is a classical multifocal electroencephalographic finding in patients of infantile spasm and related epileptic syndromes of early childhood including West syndrome and Otahara syndrome. It usually presents in early infancy and persists up to the age of two years, after which it usually resolves. The persistence of hypsarrhythmia beyond the age of two years has rarely been reported in the literature. The present study is an attempt to investigate and compare the origin and activation pattern of epileptic activity between the subjects aged 3-10 years with and without hypsarrythmia. Material and methodsForty-one patients in the age group of 3-10 years with features suggestive of seizure have been studied for quantitative electroencephalographic characteristics after dividing into hypsarrythmic and normal seizure patterns. ResultThe power spectral density (PSD) of 15 patients with hypsarrhythmia showed a significantly predominant delta frequency in quantitative electrography (qEEG) in comparison to the seizure subjects with normal electroencephalography (EEG) patterns. The amplitude progression analysis of both groups showed that the origin of focus of the hypsarrhythmic pattern is from the occipital region while no such pattern has been noticed in the control group. Discussion and conclusionHypsarrythmia is known to show multifocal origin. Predominant occipital origin in older age group subjects distinguishes the condition from classical hypsarrythmia of early childhood. The occipital origin may be indicative of persistent immaturity of the thalamocortical synaptic pathway.

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“…Quantitative electroencephalography has been employed in the diagnosis of several neurological disorders ( Livint Popa et al, 2020 ), in view of the fact that atypical spectral properties during a certain state correspond to clinically relevant abnormalities. Furthermore, employment of a age- and sex-differentiated normative database can aid in the standardization of the spectral powers, which eliminates variations that arise due to differences in age and sex.…”

Section: Introductionmentioning

confidence: 99%

Novel quantitative electroencephalogram feature image adapted for deep learning: Verification through classification of Alzheimer’s disease dementia

Jeong

Park²,

Kang³

2022

Front. Neurosci.

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Quantitative electroencephalography (QEEG) analysis is commonly adopted for the investigation of various neurological disorders, revealing electroencephalogram (EEG) features associated with specific dysfunctions. Conventionally, topographies are widely utilized for spatial representation of EEG characteristics at specific frequencies or frequency bands. However, multiple topographies at various frequency bands are required for a complete description of brain activity. In consequence, use of topographies for the training of deep learning algorithms is often challenging. The present study describes the development and application of a novel QEEG feature image that integrates all required spatial and spectral information within a single image, overcoming conventional obstacles. EEG powers recorded at 19 channels defined by the international 10–20 system were pre-processed using the EEG auto-analysis system iSyncBrain®, removing the artifact components selected through independent component analysis (ICA) and rejecting bad epochs. Hereafter, spectral powers computed through fast Fourier transform (FFT) were standardized into Z-scores through iMediSync, Inc.’s age- and sex-specific normative database. The standardized spectral powers for each channel were subsequently rearranged and concatenated into a rectangular feature matrix, in accordance with their spatial location on the scalp surface. Application of various feature engineering techniques on the established feature matrix yielded multiple types of feature images. Such feature images were utilized in the deep learning classification of Alzheimer’s disease dementia (ADD) and non-Alzheimer’s disease dementia (NADD) data, in order to validate the use of our novel feature images. The resulting classification accuracy was 97.4%. The Classification criteria were further inferred through an explainable artificial intelligence (XAI) algorithm, which complied with the conventionally known EEG characteristics of AD. Such outstanding classification performance bolsters the potential of our novel QEEG feature images in broadening QEEG utility.

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The Role of Quantitative EEG in the Diagnosis of Neuropsychiatric Disorders

Cited by 67 publications

References 86 publications

QEEG characteristics associated with malnutrition-inflammation complex syndrome

QEEG characteristics associated with malnutrition-inflammation complex syndrome

The Electroencephalographic Characterization of Hypsarrhythmia in Older Pediatric Population With Epilepsy Using Computer-Added Quantitative Methods

Novel quantitative electroencephalogram feature image adapted for deep learning: Verification through classification of Alzheimer’s disease dementia

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