ArticlePloS one2018
Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 5 of them syntheses that pooled it.
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Who cites it
54 citing papers in PubMed, 5 syntheses or guidelines pooled it, 133 citations in OpenAlex.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- The effects of different acetylcholinesterase inhibitors on EEG patterns in patients with Alzheimer's disease: A systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Pooled it
- Assessing the Potential of EEG in Early Detection of Alzheimer's Disease: A Systematic Comprehensive Review (2000-2023).Journal of Alzheimer's disease reports · 2024Pooled it
- The Importance of Subjective Cognitive Decline Recognition and the Potential of Molecular and Neurophysiological Biomarkers-A Systematic Review.International journal of molecular sciences · 2023Pooled it
- Beyond Technologies of Electroencephalography-Based Brain-Computer Interfaces: A Systematic Review From Commercial and Ethical Aspects.Frontiers in neuroscience · 2020Pooled it
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.Biosensors · 2026Review
- A CNN-transformer fusion for EEG-based discrimination of Alzheimer's and frontotemporal dementia.Physical and engineering sciences in medicine · 2026Article
- Decoding the mechanisms of amyloid-β in synaptic toxicity.Journal of Alzheimer's disease : JAD · 2026Review
- AI-driven framework for accurate detection of Alzheimer's disease in EEG.Scientific reports · 2026Article
- Subjective cognitive decline in major depressive patients is associated with altered entropy and connectivity changes of temporal and insular region.Translational psychiatry · 2025Article
- Multi-Class Detection of Neurodegenerative Diseases from EEG Signals Using Lightweight LSTM Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- A Commentary: Invisibility of Older African-American Adults in Electrophysiological Research on Alzheimer's Disease.Ethnicity & disease · 2024Article
- Investigating the Effects of Transcranial Alternating Current Stimulation on Cortical Oscillations and Network Dynamics.Brain sciences · 2024Article
- Investigating Cortical Complexity in Mixed Dementia through Nonlinear Dynamic Analyses: A Resting-State EEG Study.Iranian journal of psychiatry · 2024Article
- From Gut Microbiota to Brain Waves: The Potential of the Microbiome and EEG as Biomarkers for Cognitive Impairment.International journal of molecular sciences · 2024Review
- Is EEG Entropy a Useful Measure for Alzheimer's Disease?Actas espanolas de psiquiatria · 2024Review
- Beyond Frequency Bands: Complementary-Ensemble-Empirical-Mode-Decomposition-Enhanced Microstate Sequence Non-Randomness Analysis for Aiding Diagnosis and Cognitive Prediction of Dementia.Brain sciences · 2024Article
- Unleashing light's healing power: an overview of photobiomodulation for Alzheimer's treatment.Future science OA · 2024Review
- Detection of Alzheimer's disease onset using MRI and PET neuroimaging: longitudinal data analysis and machine learning.Neural regeneration research · 2023Review
- Primate brain pattern-based automated Alzheimer's disease detection model using EEG signals.Cognitive neurodynamics · 2023Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study addresses the problem of Alzheimer's disease (AD) diagnosis with Electroencephalography (EEG). The use of EEG as a tool for AD diagnosis has been widely studied by comparing EEG signals of AD patients only to those of healthy subjects. By contrast, we perform automated EEG diagnosis in a differential diagnosis context using a new database, acquired in clinical conditions, which contains EEG data of 169 patients: subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, possible Alzheimer's disease (AD) patients, and patients with other pathologies. We show that two EEG features, namely epoch-based entropy (a measure of signal complexity) and bump modeling (a measure of synchrony) are sufficient for efficient discrimination between these groups. We studied the performance of our methodology for the automatic discrimination of possible AD patients from SCI patients and from patients with MCI or other pathologies. A classification accuracy of 91.6% (specificity = 100%, sensitivity = 87.8%) was obtained when discriminating SCI patients from possible AD patients and 81.8% to 88.8% accuracy was obtained for the 3-class classification of SCI, possible AD and other patients.
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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.