ArticleJournal of medical signals and sensors
Automatic Diagnosis of Mild Cognitive Impairment Using Electroencephalogram Spectral Features.
Article in Journal of medical signals and sensors. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review on Resting-State EEG for Alzheimer's Disease Diagnosis and Progression Assessment.Disease markers · 2018Pooled it
- A deep hybrid CNN-BiLSTM-BiGRU architecture with explainability for mild cognitive impairment detection using EEG.Brain informatics · 2026Article
- A deep learning approach to mild cognitive impairment detection from electroencephalogram signals.Dementia & neuropsychologia · 2026Article
- CDR-Net: A computerized framework to detect Alzheimer's diseases and mild cognitive impairment.PloS one · 2026Article
- The application of traditional machine learning and deep learning with EEG for mild cognitive impairment: a bibliometric analysis.Frontiers in aging neuroscience · 2026Article
- A Novel Working Memory Task-Induced EEG Response (WM-TIER) Feature Extraction Framework for Detecting Alzheimer's Disease and Mild Cognitive Impairment.Biosensors · 2025Article
- Multi-Class Detection of Neurodegenerative Diseases from EEG Signals Using Lightweight LSTM Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- EEG-Based Detection of Mild Cognitive Impairment Using DWT-Based Features and Optimization Methods.Diagnostics (Basel, Switzerland) · 2024Article
- Selecting EEG channels and features using multi-objective optimization for accurate MCI detection: validation using leave-one-subject-out strategy.Scientific reports · 2024Article
- Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review.Biosensors · 2024Review
- Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals.Cognitive neurodynamics · 2024Article
- Article
- A Review of EEG-based Localization of Epileptic Seizure Foci: Common Points with Multimodal Fusion of Brain Data.Journal of medical signals and sensors · 2024Review
- Novel methodology for detection and prediction of mild cognitive impairment using resting-state EEG.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer's disease.Scientific reports · 2023Article
- Primate brain pattern-based automated Alzheimer's disease detection model using EEG signals.Cognitive neurodynamics · 2023Article
- Multi Modal Feature Extraction for Classification of Vascular Dementia in Post-Stroke Patients Based on EEG Signal.Sensors (Basel, Switzerland) · 2023Article
- Multi-feature fusion learning for Alzheimer's disease prediction using EEG signals in resting state.Frontiers in neuroscience · 2023Article
- Selection of the optimal channel configuration for implementing wearable EEG devices for the diagnosis of mild cognitive impairment.Alzheimer's research & therapy · 2022Article
- Automatic Diagnosis of Mild Cognitive Impairment Based on Spectral, Functional Connectivity, and Nonlinear EEG-Based Features.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is one of the most expensive and fatal diseases in the elderly population. Up to now, no cure have been found for AD, so early stage diagnosis is the only way to control it. Mild cognitive impairment (MCI) usually is the early stage of AD which is defined as decreasing in mental abilities such a cognition, memory, and speech not too severe to interfere daily activities. MCI diagnosis is rather hard and usually assumed as normal consequences of aging. This study proposes an accurate, mobile, and nonexpensive diagnostic approach based on electroencephalogram (EEG) signal. EEG signals were recorded using 19 electrodes positioned according to the 10-20 International system at resting eyes closed state from 16 normal and 11 MCI participants. Nineteen Spectral features are computed for each channel and examined using a correlation based algorithm to select the best discriminative features. Selected features are classified using a combination of neurofuzzy system and k-nearest neighbor classifier. Final results reach 88.89%, 100%, and 83.33% for accuracy, sensitivity, and specificity, respectively, which shows the potential of proposed method to be used as an MCI diagnostic tool, especially for screening a large population.
Indexed as
Identifiers
27014609PMC4786960What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.