SynthesisJournal of Alzheimer's disease reports2024
Assessing the Potential of EEG in Early Detection of Alzheimer's Disease: A Systematic Comprehensive Review (2000-2023).
Synthesis in Journal of Alzheimer's disease reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Electroencephalography in Subjective Cognitive Decline and Mild Cognitive Impairment: Systematic Review of Biomarkers, Classification, and Prognostic Evidence.Journal of medical Internet research · 2026Pooled it
- Screening for Alzheimer's disease in the community using an AI-driven screening platform: design of the PREDICTOM study.The journal of prevention of Alzheimer's disease · 2026Article
- ScaleSpecter: a frequency-aware multi-scale patch framework for robust physiological classification under non-stationarity.Frontiers in human neuroscience · 2026Article
- Electroencephalography for early Alzheimer's disease diagnosis: from advanced feature engineering to interpretable ai and clinical translation.Frontiers in psychiatry · 2026Review
- Clustered pattern projection for EEG dementia classification: evaluating the reliability of disorder patterns.Frontiers in neuroscience · 2026Article
- A Deep Learning Approach to Alzheimer's Diagnosis Using EEG Data: Dual-Attention and Optuna-Optimized SVM.Biomedicines · 2025Article
- The Role of Quantitative EEG in the Diagnosis of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Task-Related EEG as a Biomarker for Preclinical Alzheimer's Disease: An Explainable Deep Learning Approach.Biomimetics (Basel, Switzerland) · 2025Article
- Time-Frequency Domain Analysis of Quantitative Electroencephalography as a Biomarker for Dementia.Diagnostics (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: As the prevalence of Alzheimer's disease (AD) grows with an aging population, the need for early diagnosis has led to increased focus on electroencephalography (EEG) as a non-invasive diagnostic tool. Objective: This review assesses advancements in EEG analysis, including the application of machine learning, for detecting AD from 2000 to 2023. Methods: Following PRISMA guidelines, a search across major databases resulted in 25 studies that met the inclusion criteria, focusing on EEG's application in AD diagnosis and the use of novel signal processing and machine learning techniques. Results: Progress in EEG analysis has shown promise for early AD identification, with techniques like Hjorth parameters and signal compressibility enhancing detection capabilities. Machine learning has improved the precision of differential diagnosis between AD and mild cognitive impairment. However, challenges in standardizing EEG methodologies and data privacy remain. Conclusions: EEG stands out as a valuable tool for early AD detection, with the potential to integrate into multimodal diagnostic approaches. Future research should aim to standardize EEG procedures and explore collaborative, privacy-preserving research methods.
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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.