EEG Biomarker for Alzheimer’s Disease

Algin, Demet Ilhan; Adapınar, Demet Özbabalık; Erdinç, Oğuz Osman

doi:10.5772/intechopen.93711

Cited by 2 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 13 Patients with MCI and dementia sometimes exhibit anomalies in their EEG signals, such as slowing of the signals that are accompanied by a decrease in power frequencies of the alpha and beta bands of 8–30 Hz and an increase in the power frequencies of the delta and theta bands of 0.5–8 Hz. 14 …”

Section: Introductionmentioning

confidence: 99%

CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG

Simfukwe¹,

Youn²,

Kim³

et al. 2023

NDT

View full text Add to dashboard Cite

Purpose: Electroencephalogram (EEG) signals give detailed information on the electrical brain activities occurring in the cerebral cortex. They are used to study brain-related disorders such as mild cognitive impairment (MCI) and Alzheimer's disease (AD). Brain signals obtained using an EEG machine can be a neurophysiological biomarker for early diagnosis of dementia through quantitative EEG (qEEG) analysis. This paper proposes a machine learning methodology to detect MCI and AD from qEEG time-frequency (TF) images of the subjects in an eyes-closed resting state (ECR). Participants and Methods:The dataset consisted of 16,910 TF images from 890 subjects: 269 healthy controls (HC), 356 MCI, and 265 AD. First, EEG signals were transformed into TF images using a Fast Fourier Transform (FFT) containing different event-rated changes of frequency sub-bands preprocessed from the EEGlab toolbox in the MATLAB R2021a environment software. The preprocessed TF images were applied in a convolutional neural network (CNN) with adjusted parameters. For classification, the computed image features were concatenated with age data and went through the feed-forward neural network (FNN). Results: The trained models', HC vs MCI, HC vs AD, and HC vs CASE (MCI + AD), performance metrics were evaluated based on the test dataset of the subjects. The accuracy, sensitivity, and specificity were evaluated: HC vs MCI was 83%, 93%, and 73%, HC vs AD was 81%, 80%, and 83%, and HC vs CASE (MCI + AD) was 88%, 80%, and 90%, respectively. Conclusion:The proposed models trained with TF images and age can be used to assist clinicians as a biomarker in detecting cognitively impaired subjects at an early stage in clinical sectors.

show abstract

Section: Introductionmentioning

confidence: 99%

CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG

Simfukwe¹,

Youn²,

Kim³

et al. 2023

NDT

View full text Add to dashboard Cite

show abstract

“…Additionally, these signals often display a slowing pattern. 17 A standard method for analyzing EEG data involves using traditional power spectrum density (PSD) and coherence measurements through quantitative EEG (qEEG). 18 The qEEG helps clinicians efficiently summarize EEG data compared to conventional EEG data.…”

Section: Introductionmentioning

confidence: 99%

qEEG as Biomarker for Alzheimer’s Disease: Investigating Relative PSD Difference and Coherence Analysis

Simfukwe,

Han,

Jeong

et al. 2023

NDT

View full text Add to dashboard Cite

Purpose Electroencephalography (EEG) is a non-intrusive technique that provides comprehensive insights into the electrical activities of the brain’s cerebral cortex. The brain signals obtained from EEGs can be used as a neuropsychological biomarker to detect different stages of Alzheimer’s disease (AD) through quantitative EEG (qEEG) analysis. This paper investigates the difference in the abnormalities of resting state EEG (rEEG) signals between eyes-open (EOR) and eyes-closed (ECR) in AD by analyzing 19-scalp electrode EEG signals and making a comparison with healthy controls (HC). Participants and Methods The rEEG data from 534 subjects (ages 40–90) consisting of 269 HC and 265 AD subjects in South Korea were used in this study. The qEEG for EOR and ECR states were performed separately for HC and AD subjects to measure the relative power spectrum density (PSD) and coherence with functional connectivity to evaluate abnormalities. The rEEG data were preprocessed and analyzed using EEGlab and Brainstorm toolboxes in MATLAB R2021a software, and statistical analyses were carried out using ANOVA. Results Based on the Welch method, the relative PSD of the EEG EOR and ECR states difference in the AD group showed a significant increase in the delta frequency band of 19 EEG channels, particularly in the frontal, parietal, and temporal, than the HC groups. The delta power band on the source level was increased for the AD group and decreased for the HC group. In contrast, the source activities of alpha, beta, and gamma frequency bands were significantly reduced in the AD group, with a high decrease in the beta frequency band in all brain areas. Furthermore, the coherence of rEEG among different EEG electrodes was analyzed in the beta frequency band. It showed that pair-wise coherence between different brain areas in the AD group is remarkably increased in the ECR state and decreased after subtracting out the EOR state. Conclusion The findings suggest that examining PSD and functional connectivity through coherence analysis could serve as a promising and comprehensive approach to differentiate individuals with AD from normal, which may benefit our understanding of the disease.

show abstract

EEG Biomarker for Alzheimer’s Disease

Cited by 2 publications

References 29 publications

CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG

CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG

qEEG as Biomarker for Alzheimer’s Disease: Investigating Relative PSD Difference and Coherence Analysis

Contact Info

Product

Resources

About