Quantitative Electroencephalogram Standardization: A Sex- and Age-Differentiated Normative Database

Ko, Juhee; Park, Ukeob; Kim, Daekeun; Kang, Seung Wan

doi:10.3389/fnins.2021.766781

Cited by 23 publications

(19 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Brain functionalities are known to vary with gender and clearly degenerate with age. To account for this, our study utilized iMediSync, Inc’s normative database ISB-NormDB which holds EEG data of 1,289 healthy control subjects (553 males, 736 females) aged 4.5–81 years old ( Ko et al, 2021 ). The database provides standardized age- and sex-specific features referred to as Z-scores, which are common and statistically robust measures of variation from norms and capture standard deviation.…”

Section: Methodsmentioning

confidence: 99%

“…The dataset was segregated into two groups, ADD and NADD. The NADD group consisted of subjective cognitive decline (SCD) and mild cognitive impairment (MCI) data, along with iMediSync, Inc.’s EEG data of healthy individuals from ISB-NormDB ( Ko et al, 2021 ). The inclusion of MCI data in the NADD group was crucial for the identification of significant ADD-specific characteristics that are not observed in the pre-clinical stage of ADD.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

Jeong

Park²,

Kang³

2022

Front. Neurosci.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

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.

Self Cite

View full text Add to dashboard Cite

show abstract

“…First, the power spectral density of the EEG rhythms was computed using Fast Fourier Transform (FFT) method with 0.25 Hz of frequency resolution using the iSyncBrain AI-driven autoanalysis system. Then, the signal was decomposed into the following frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha 1 (8-10 Hz), alpha 2 (10-12 Hz), beta 1 (12-15 Hz), beta 2 (15-20 Hz), beta 3 (20-30 Hz), and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). For each channel and frequency band, the average power during the recording was calculated and treated as a feature.…”

Section: Feature Generationmentioning

confidence: 99%

Characteristics of Attention-Deficit/Hyperactivity Disorder Subtypes in Children Classified Using Quantitative Electroencephalography

Ji¹,

Choi²,

Lee³

et al. 2022

NDT

Self Cite

View full text Add to dashboard Cite

This study used quantitative electroencephalography (QEEG) to investigate the characteristics of attention-deficit/hyperactivity disorder (ADHD) subtypes in children. Patients and Methods: There were 69 subjects (42 with ADHD and 27 neurotypical (NT)) in this study. A semi-structured interview was conducted with each participant for psychiatric diagnostic evaluation. We measured the absolute and relative power in 19 channels and analyzed QEEG using the following frequency ranges: delta (1-4 Hz), theta (4-8 Hz), alpha 1 (8-10 Hz), alpha 2 (10-12 Hz), beta 1 (12-15 Hz), beta 2 (15-20 Hz), beta 3 (20-30 Hz), and gamma (30-45 Hz). Group analyses and EEG noise preprocessing were conducted using iSyncBrain, a cloud-based, artificial intelligence EEG analysis platform. Analysis of covariance adjusted for IQ, age, and sex was used. Results: QEEG analysis revealed three ADHD subtypes, characterized by (A) elevated relative fast alpha and beta power, (B) elevated absolute slow frequency (delta and theta power), or (C) elevated absolute and relative beta power. A significant difference was found in the Korean ADHD Rating Scale (K-ARS) among the four groups (df=3, F=8.004, p<0.001); group C had the highest score (25.31 ±11.16), followed by group A (21.67±13.18). The score of group B (12.64±7.84) was similar to that of the NT group (11.07±6.12) and did not reach the cut-off point of the K-ARS. In the Wender-Utah Rating Scale (WURS), group B score (55.82±23.17) was significantly higher than the NT group score (42.81±13.26). Conclusion:These results indicate that children with ADHD do not constitute a neurophysiologically homogenous group. Children with QEEG subtype B (elevated slow frequency) may be difficult to distinguish from normal children using the K-ARS, which is the most common screening tool for ADHD. Moreover, parents of children with this subtype may be less sensitive to observing ADHD symptoms.

show abstract

“…ISB-NormDB is sex- and age-differentiated standardized QEEG normative database ( 11 ). ISB-NormDB has total 1,289 subjects’ QEEG data (553 men, 736 women, ages from 4.5 to 81 years).…”

Section: Eeg Analysismentioning

confidence: 99%

“…However, QEEG is user-independent data, which has lots of variability. Our previous study reported the sex- and age-differentiated standardized quantitative EEG (QEEG) normative database (ISB-NormDB), which can remove user-independent variability ( 11 ). Through this database and sex- and age-fitted model, band power data can be converted to sex- and age-matched standardized band power values (Z-scored band power).…”

Section: Introductionmentioning

confidence: 99%

Prediction model for potential depression using sex and age-reflected quantitative EEG biomarkers

2022

Self Cite

View full text Add to dashboard Cite

Depression is a prevalent mental disorder in modern society, causing many people to suffer or even commit suicide. Psychiatrists and psychologists typically diagnose depression using representative tests, such as the Beck’s Depression Inventory (BDI) and the Hamilton Depression Rating Scale (HDRS), in conjunction with patient consultations. Traditional tests, however, are time-consuming, can be trained on patients, and entailed a lot of clinician subjectivity. In the present study, we trained the machine learning models using sex and age-reflected z-score values of quantitative EEG (QEEG) indicators based on data from the National Standard Reference Data Center for Korean EEG, with 116 potential depression subjects and 80 healthy controls. The classification model has distinguished potential depression groups and normal groups, with a test accuracy of up to 92.31% and a 10-cross-validation loss of 0.13. This performance proposes a model with z-score QEEG metrics, considering sex and age as objective and reliable biomarkers for early screening for the potential depression.

show abstract

Quantitative Electroencephalogram Standardization: A Sex- and Age-Differentiated Normative Database

Cited by 23 publications

References 45 publications

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

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

Characteristics of Attention-Deficit/Hyperactivity Disorder Subtypes in Children Classified Using Quantitative Electroencephalography

Prediction model for potential depression using sex and age-reflected quantitative EEG biomarkers

Contact Info

Product

Resources

About