ArticleScientific reports2022
The development of an automated machine learning pipeline for the detection of Alzheimer's Disease.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Development and validation of a Cog-Free risk predicting tool for dementia in a community setting.General psychiatry · 2026Article
- Interpretable self-supervised transformers for resting-state EEG analysis in Alzheimer's disease.Frontiers in neuroinformatics · 2026Article
- Reliability and diagnostic performance of an automated MRI-based classifier compared with radiologists in Alzheimer's disease.Frontiers in neuroinformatics · 2026Article
- A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer's disease detection.Cognitive neurodynamics · 2025Article
- EEG biomarkers for Alzheimer's disease: A novel automated pipeline for detecting and monitoring disease progression.Journal of Alzheimer's disease : JAD · 2025Article
- Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.Biomimetics (Basel, Switzerland) · 2025Review
- 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
- Hepatitis C Virus Saint Petersburg Variant Detection With Machine Learning Methods.Journal of medical virology · 2025Article
- Investigating the Key Trends in Applying Artificial Intelligence to Health Technologies: A Scoping Review.PloS one · 2025Article
- Event-related potential markers of subjective cognitive decline and mild cognitive impairment during a sustained visuo-attentive task.NeuroImage. Clinical · 2025Article
- Article
- STEADYNet: Spatiotemporal EEG analysis for dementia detection using convolutional neural network.Cognitive neurodynamics · 2024Article
- LCADNet: a novel light CNN architecture for EEG-based Alzheimer disease detection.Physical and engineering sciences in medicine · 2024Article
- Power spectral density-based resting-state EEG classification of first-episode psychosis.Scientific reports · 2024Article
- A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer's Disease with Electroencephalography in Resting-State Adults.Brain sciences · 2024Article
- SARS-CoV-2 alters neural synchronies in the brain with more severe effects in younger individuals.Scientific reports · 2023Article
- Complexity of fractal dimension patterns and machine learning-based classification of altered motor cortical oscillatory activity in rodent models of Parkinson disease.Frontiers in neurologyArticle
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Although Alzheimer's disease is the most prevalent form of dementia, there are no treatments capable of slowing disease progression. A lack of reliable disease endpoints and/or biomarkers contributes in part to the absence of effective therapies. Using machine learning to analyze EEG offers a possible solution to overcome many of the limitations of current diagnostic modalities. Here we develop a logistic regression model with an accuracy of 81% that addresses many of the shortcomings of previous works. To our knowledge, no other study has been able to solve the following problems simultaneously: (1) a lack of automation and unbiased removal of artifacts, (2) a dependence on a high level of expertise in data pre-processing and ML for non-automated processes, (3) the need for very large sample sizes and accurate EEG source localization using high density systems, (4) and a reliance on black box ML approaches such as deep neural nets with unexplainable feature selection. This study presents a proof-of-concept for an automated and scalable technology that could potentially be used to diagnose AD in clinical settings as an adjunct to conventional neuropsychological testing, thus enhancing efficiency, reproducibility, and practicality of AD diagnosis.
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