ArticleFrontiers in aging neuroscience2023
Diagnosis of Alzheimer's disease via resting-state EEG: integration of spectrum, complexity, and synchronization signal features.
Article in Frontiers in aging neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- The EEG analysis and identification of Alzheimer's disease: a review.Frontiers in aging neuroscience · 2025Pooled it
- The AHEPA EEG benchmark: setting the standard for machine learning in dementia diagnosis, a scoping review.Cognitive neurodynamics · 2026Article
- Knowledge-Guided Deep Learning with Clinical EEG Biomarkers for Automated Dementia Detection and Staging.Diagnostics (Basel, Switzerland) · 2026Article
- A Consolidated Framework for the Detection of Alzheimer's Disease Using EEG Signals and Hybrid Models.Biomimetics (Basel, Switzerland) · 2026Article
- Deep Learning-Based Alzheimer's Disease Detection from Multi-Channel EEG Using Fused Time-Frequency Image Grids.Diagnostics (Basel, Switzerland) · 2026Article
- EEG-based classification of alzheimer's disease and frontotemporal dementia using functional connectivity.Scientific reports · 2026Article
- ScaleSpecter: a frequency-aware multi-scale patch framework for robust physiological classification under non-stationarity.Frontiers in human neuroscience · 2026Article
- Linking EEG markers of oscillopathy and default mode network dysfunction in Alzheimer's disease.Frontiers in human neuroscience · 2026Review
- Explainable machine learning for Alzheimer's disease characterization using small-sample EEG data.Frontiers in aging neuroscience · 2026Article
- Interpretable self-supervised transformers for resting-state EEG analysis in Alzheimer's disease.Frontiers in neuroinformatics · 2026Article
- NeuroFusionNet: a hybrid EEG feature fusion framework for accurate and explainable Alzheimer's Disease detection.Scientific reports · 2025Article
- Abnormal Enhanced Gamma Synchronization in the Default Mode Network Associated With Chronic Pain.European journal of neurology · 2025Article
- Investigating Gamma Frequency Band PSD in Alzheimer's Disease Using qEEG from Eyes-Open and Eyes-Closed Resting States.Journal of clinical medicine · 2025Article
- Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics.Scientific reports · 2025Article
- Ensemble Learning-Based Alzheimer's Disease Classification Using Electroencephalogram Signals and Clock Drawing Test Images.Sensors (Basel, Switzerland) · 2025Article
- Differences in brain function entropy due to cognitive impairment: Application of functional near infrared spectroscopy measures during dual-tasking.International journal of psychophysiology : official journal of the International Organization of Psychophysiology · 2025Article
- Machine learning based on event-related oscillations of working memory differentiates between preclinical Alzheimer's disease and normal aging.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2025Article
- Topological Biomarkers of Alzheimer's Disease from Functional Brain Network Analysis.Current Alzheimer research · 2025Article
- Electroencephalogram-based time-frequency analysis for Alzheimer's disease detection using machine learning.Journal of biological methods · 2025Article
- Alternating chimera states and synchronization in multilayer neuronal networks with ephaptic intralayer coupling.Cognitive neurodynamics · 2024Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Alzheimer's disease (AD) is the most common neurogenerative disorder, making up 70% of total dementia cases with a prevalence of more than 55 million people. Electroencephalogram (EEG) has become a suitable, accurate, and highly sensitive biomarker for the identification and diagnosis of AD. Methods: In this study, a public database of EEG resting state-closed eye recordings containing 36 AD subjects and 29 normal subjects was used. And then, three types of signal features of resting-state EEG, i.e., spectrum, complexity, and synchronization, were performed by applying various signal processing and statistical methods, to obtain a total of 18 features for each signal epoch. Next, the supervised machine learning classification algorithms of decision trees, random forests, and support vector machine (SVM) were compared in categorizing processed EEG signal features of AD and normal cases with leave-one-person-out cross-validation. Results: The results showed that compared to normal cases, the major change in EEG characteristics in AD cases was an EEG slowing, a reduced complexity, and a decrease in synchrony. The proposed methodology achieved a relatively high classification accuracy of 95.65, 95.86, and 88.54% between AD and normal cases for decision trees, random forests, and SVM, respectively, showing that the integration of spectrum, complexity, and synchronization features for EEG signals can enhance the performance of identifying AD and normal subjects. Conclusion: This study recommended the integration of EEG features of spectrum, complexity, and synchronization for aiding the diagnosis of AD.
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