ArticleHeliyon2023
A novel method for diagnosing Alzheimer's disease using deep pyramid CNN based on EEG signals.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The EEG analysis and identification of Alzheimer's disease: a review.Frontiers in aging neuroscience · 2025Pooled it
- A CNN-transformer fusion for EEG-based discrimination of Alzheimer's and frontotemporal dementia.Physical and engineering sciences in medicine · 2026Article
- Diagnosis of Alzheimer's disease with high accuracy via Petri net modeling of signaling pathways.Scientific reports · 2026Article
- Comprehensive methodology for sample enrichment in EEG biomarker studies for Alzheimer's risk classification.PloS one · 2026Article
- FHESA: fourier decomposition and hilbert transform based EEG signal analysis for Alzheimer's disease detection.Physical and engineering sciences in medicine · 2025Article
- A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer's disease detection.Cognitive neurodynamics · 2025Article
- Modeling Working Memory in Neurodegeneration: A Focus on EEG Methods.Diagnostics (Basel, Switzerland) · 2025Review
- A Multimodal Multi-Stage Deep Learning Model for the Diagnosis of Alzheimer's Disease Using EEG Measurements.Neurology international · 2025Article
- Connectogram-COH: A Coherence-Based Time-Graph Representation for EEG-Based Alzheimer's Disease Detection.Diagnostics (Basel, Switzerland) · 2025Article
- A novel EEG artifact removal algorithm based on an advanced attention mechanism.Scientific reports · 2025Article
- Identification of Alzheimer's disease brain networks based on EEG phase synchronization.Biomedical engineering online · 2025Article
- A Novel CNN-Based Framework for Alzheimer's Disease Detection Using EEG Spectrogram Representations.Journal of personalized medicine · 2025Article
- Multimodal diagnosis of Alzheimer's disease based on resting-state electroencephalography and structural magnetic resonance imaging.Frontiers in physiology · 2025Article
- Early heart disease prediction using feature engineering and machine learning algorithms.Heliyon · 2024Article
- Artificial intelligence for brain disease diagnosis using electroencephalogram signals.Journal of Zhejiang University. Science. B · 2024Review
- Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN.Heliyon · 2024Article
- Inter-hospital moderate and advanced Alzheimer's disease detection through convolutional neural networks.Heliyon · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The diagnosis of Alzheimer's disease (AD) using electroencephalography (EEG) has garnered more attention recently. New methods: In this paper, we present a novel approach for the diagnosis of AD, in terms of classifying the resting-state EEG of AD, mild cognitive impairment (MCI), and healthy control (HC). To overcome the hurdles of limited data available and the over-fitting problem of the deep learning models, we studied overlapping sliding windows to augment the one-dimensional EEG data of 100 subjects (including 49 AD subjects, 37 MCI subjects and 14 HC subjects). After constructing the appropriate dataset, the modified DPCNN was used to classify the augmented EEG. Furthermore, the model performance was evaluated by 5 times of 5-fold cross-validation and the confusion matrix has been obtained. Results: The average accuracy rate of the model for classifying AD, MCI, and HC is 97.10%, and the F1 score of the three-class classification model is 97.11%, which further proves the model's excellent performance. Conclusions: Therefore, the DPCNN proposed in this paper can accurately classify the one-dimensional EEG of AD and is worthy of reference for the diagnosis of the disease.
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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.