ArticleComputational intelligence and neuroscience2023
Using CNN Saliency Maps and EEG Modulation Spectra for Improved and More Interpretable Machine Learning-Based Alzheimer's Disease Diagnosis.
Article in Computational intelligence and neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 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
- Electroencephalography for early Alzheimer's disease diagnosis: from advanced feature engineering to interpretable ai and clinical translation.Frontiers in psychiatry · 2026Review
- Normalizing flow based neural processes for Alzheimer's disease progression prediction.PloS one · 2026Article
- Classifying Obsessive-Compulsive Disorder from Resting-State EEG Using Convolutional Neural Networks: A Pilot Study.Computational psychiatry (Cambridge, Mass.) · 2026Article
- A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer's disease detection.Cognitive neurodynamics · 2025Article
- Classifying Obsessive-Compulsive Disorder from Resting-State EEG using Convolutional Neural Networks: A Pilot Study.medRxiv : the preprint server for health sciences · 2025Article
- Connectogram-COH: A Coherence-Based Time-Graph Representation for EEG-Based Alzheimer's Disease Detection.Diagnostics (Basel, Switzerland) · 2025Article
- Estimation of Task-Related Dynamic Brain Connectivity via Data Inflation and Classification Model Explainability.Neuroinformatics · 2025Article
- Multianalyte nano-biosensor diagnostics: advances through microfluidic and AI integration.Frontiers in bioengineering and biotechnology · 2025Review
- Fusing convolutional learning and attention-based Bi-LSTM networks for early Alzheimer's diagnosis from EEG signals towards IoMT.Scientific reports · 2024Article
- Deep Learning for Alzheimer's Disease Prediction: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2024Review
- Inter-hospital moderate and advanced Alzheimer's disease detection through convolutional neural networks.Heliyon · 2024Article
- Utilizing portable electroencephalography to screen for pathology of Alzheimer's disease: a methodological advancement in diagnosis of neurodegenerative diseases.Frontiers in psychiatry · 2024Article
- EEGDecoder-x: an explainable deep learning framework for cross-subject EEG-based detection of Alzheimer's and Creutzfeldt-Jakob disease.Frontiers in neurologyArticle
Corrections and comments
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Biomarkers based on resting-state electroencephalography (EEG) signals have emerged as a promising tool in the study of Alzheimer's disease (AD). Recently, a state-of-the-art biomarker was found based on visual inspection of power modulation spectrograms where three "patches" or regions from the modulation spectrogram were proposed and used for AD diagnostics. Here, we propose the use of deep neural networks, in particular convolutional neural networks (CNNs) combined with saliency maps, trained on power modulation spectrogram inputs to find optimal patches in a data-driven manner. Experiments are conducted on EEG data collected from fifty-four participants, including 20 healthy controls, 19 patients with mild AD, and 15 moderate-to-severe AD patients. Five classification tasks are explored, including the three-class problem, early-stage detection (control vs. mild-AD), and severity level detection (mild vs. moderate-to-severe). Experimental results show the proposed biomarkers outperform the state-of-the-art benchmark across all five tasks, as well as finding complementary modulation spectrogram regions not previously seen via visual inspection. Lastly, experiments are conducted on the proposed biomarkers to test their sensitivity to age, as this is a known confound in AD characterization. Across all five tasks, none of the proposed biomarkers showed a significant relationship with age, thus further highlighting their usefulness for automated AD diagnostics.
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