Robust EEG Based Biomarkers to Detect Alzheimer’s Disease

Al-nuaimi, Ali H.; Bluma, Marina; Al-Juboori, Shaymaa; Eke, Chima S.; Jammeh, Emmanuel; Sun, Lingfen; Ifeachor, Emmanuel

doi:10.3390/brainsci11081026

Cited by 21 publications

(11 citation statements)

References 176 publications

(306 reference statements)

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“…The EEG signals were preprocessed on the MATLAB version R2021a environment software (https://kr.mathworks.com/) using the EEGlab version 2010 toolbox (https://sccn.ucsd. edu/eeglab/index.php) by applying FFT to obtain qEEG time-frequency (TF) images with a dimension of 875×656 for ECR with sub-bands (delta [1-4 Hz], theta [4][5][6][7][8], alpha [8][9][10][11][12], beta [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], and gamma [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]) from each EEG channel and TF images colormap was normalized in the range of [− 20 20] dB. EEGlab is an interactive MATLAB toolkit for analyzing continuous and event-related EEG signals as well as other electrophysiological data.…”

Section: Eeg Recordings and Preprocessingmentioning

confidence: 99%

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

Simfukwe¹,

Youn²,

Kim³

et al. 2023

NDT

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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.

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Section: Eeg Recordings and Preprocessingmentioning

confidence: 99%

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

Simfukwe¹,

Youn²,

Kim³

et al. 2023

NDT

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“…Mechanistically nonspecific evidence of target engagement can be garnered from electroencephalography (EEG), evoked potentials, and functional MRI (fMRI) [ 66 , 67 , 68 , 69 ]. The restoration or slowing of deterioration of these measures suggest that circuit function has been beneficially affected compared to placebo, demonstrate that the drug has entered the brain, provide preliminary evidence in support of the POC of drug activity, and may show drug–placebo differences in smaller samples than those required to demonstrate clinical effects.…”

Section: Biomarker Definition and Classificationmentioning

confidence: 99%

Biomarkers for Alzheimer’s Disease: Context of Use, Qualification, and Roadmap for Clinical Implementation

Cummings

Kinney

2022

Medicina

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Background and Objectives: The US Food and Drug Administration (FDA) defines a biomarker as a characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention. Biomarkers may be used in clinical care or as drug development tools (DDTs) in clinical trials. The goal of this review and perspective is to provide insight into the regulatory guidance for the use of biomarkers in clinical trials and clinical care. Materials and Methods: We reviewed FDA guidances relevant to biomarker use in clinical trials and their transition to use in clinical care. We identified instructive examples of these biomarkers in Alzheimer’s disease (AD) drug development and their application in clinical practice. Results: For use in clinical trials, biomarkers must have a defined context of use (COU) as a risk/susceptibility, diagnostic, monitoring, predictive, prognostic, pharmacodynamic, or safety biomarker. A four-stage process defines the pathway to establish the regulatory acceptance of the COU for a biomarker including submission of a letter of intent, description of the qualification plan, submission of a full qualification package, and acceptance through a qualification recommendation. Biomarkers used in clinical care may be companion biomarkers, in vitro diagnostic devices (IVDs), or laboratory developed tests (LDTs). A five-phase biomarker development process has been proposed to structure the biomarker development process. Conclusions: Biomarkers are increasingly important in drug development and clinical care. Adherence to regulatory guidance for biomarkers used in clinical trials and patient care is required to advance these important drug development and clinical tools.

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“…An EEG is a low-cost, non-invasive method that has the sensitivity to detect early dementia and even measure its severity [ 14 ]. Quantitative EEG (QEEG) in cases of Alzheimer’s dementia has been reported in [ 15 , 16 , 17 ]. Characterization of EEG signals in cases of Lewy Body dementia reported in [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Multi Modal Feature Extraction for Classification of Vascular Dementia in Post-Stroke Patients Based on EEG Signal

Hadiyoso

Zakaria

Ong

et al. 2023

Sensors

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Dementia is a term that represents a set of symptoms that affect the ability of the brain’s cognitive functions related to memory, thinking, behavior, and language. At worst, dementia is often called a major neurocognitive disorder or senile disease. One of the most common types of dementia after Alzheimer’s is vascular dementia. Vascular dementia is closely related to cerebrovascular disease, one of which is stroke. Post-stroke patients with recurrent onset have the potential to develop dementia. An accurate diagnosis is needed for proper therapy management to ensure the patient’s quality of life and prevent it from worsening. The gold standard diagnostic of vascular dementia is complex, includes psychological tests, complete memory tests, and is evidenced by medical imaging of brain lesions. However, brain imaging methods such as CT-Scan, PET-Scan, and MRI have high costs and cannot be routinely used in a short period. For more than two decades, electroencephalogram signal analysis has been an alternative in assisting the diagnosis of brain diseases associated with cognitive decline. Traditional EEG analysis performs visual observations of signals, including rhythm, power, and spikes. Of course, it requires a clinician expert, time consumption, and high costs. Therefore, a quantitative EEG method for identifying vascular dementia in post-stroke patients is discussed in this study. This study used 19 EEG channels recorded from normal elderly, post-stroke with mild cognitive impairment, and post-stroke with dementia. The QEEG method used for feature extraction includes relative power, coherence, and signal complexity; the evaluation performance of normal-mild cognitive impairment-dementia classification was conducted using Support Vector Machine and K-Nearest Neighbor. The results of the classification simulation showed the highest accuracy of 96% by Gaussian SVM with a sensitivity and specificity of 95.6% and 97.9%, respectively. This study is expected to be an additional criterion in the diagnosis of dementia, especially in post-stroke patients.

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Robust EEG Based Biomarkers to Detect Alzheimer’s Disease

Cited by 21 publications

References 176 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

Biomarkers for Alzheimer’s Disease: Context of Use, Qualification, and Roadmap for Clinical Implementation

Multi Modal Feature Extraction for Classification of Vascular Dementia in Post-Stroke Patients Based on EEG Signal

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