ArticleBioengineering (Basel, Switzerland)2022
Smart-Data-Driven System for Alzheimer Disease Detection through Electroencephalographic Signals.
Article in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 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
- Machine learning-based stratification of chagas heart failure severity using ECG power spectral biomarkers.Medical & biological engineering & computing · 2026Article
- EENet-RLA: An Explainable Prediction Learning Framework for Alzheimer's Disease Classification from EEG Signals.Brain topography · 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
- Connectogram-COH: A Coherence-Based Time-Graph Representation for EEG-Based Alzheimer's Disease Detection.Diagnostics (Basel, Switzerland) · 2025Article
- Electroencephalogram-based time-frequency analysis for Alzheimer's disease detection using machine learning.Journal of biological methods · 2025Article
- EEG-based emotion recognition using multi-scale dynamic CNN and gated transformer.Scientific reports · 2024Article
- Exploring the Relationship Between CAIDE Dementia Risk and EEG Signal Activity in a Healthy Population.Brain sciences · 2024Article
- Fusing convolutional learning and attention-based Bi-LSTM networks for early Alzheimer's diagnosis from EEG signals towards IoMT.Scientific reports · 2024Article
- Novel methodology for detection and prediction of mild cognitive impairment using resting-state EEG.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- An EEG-based framework for automated discrimination of conversion to Alzheimer's disease in patients with amnestic mild cognitive impairment: an 18-month longitudinal study.Frontiers in aging neuroscience · 2024Article
- ETMT: A Tool for Eye-Tracking-Based Trail-Making Test to Detect Cognitive Impairment.Sensors (Basel, Switzerland) · 2023Article
- Automatic Analysis of MRI Images for Early Prediction of Alzheimer's Disease Stages Based on Hybrid Features of CNN and Handcrafted Features.Diagnostics (Basel, Switzerland) · 2023Article
- EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives.Bioengineering (Basel, Switzerland) · 2022Review
- Discriminant Subspace Low-Rank Representation Algorithm for Electroencephalography-Based Alzheimer's Disease Recognition.Frontiers in aging neuroscience · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundAlzheimer's Disease (AD) stands out as one of the main causes of dementia worldwide and it represents around 65% of all dementia cases, affecting mainly elderly people. AD is composed of three evolutionary stages: Mild Cognitive Impairment (MCI), Mild and Moderate AD (ADM) and Advanced AD (ADA). It is crucial to create a tool for assisting AD diagnosis in its early stages with the aim of halting the disease progression.
methodsThe main purpose of this study is to develop a system with the ability of differentiate each disease stage by means of Electroencephalographic Signals (EEG). Thereby, an EEG nonlinear multi-band analysis by Wavelet Packet was performed enabling to extract several features from each study group. Classic Machine Learning (ML) and Deep Learning (DL) methods have been used for data classification per EEG channel.
resultsThe maximum accuracies obtained were 78.9% (Healthy controls (C) vs. MCI), 81.0% (C vs. ADM), 84.2% (C vs. ADA), 88.9% (MCI vs. ADM), 93.8% (MCI vs. ADA), 77.8% (ADM vs. ADA) and 56.8% (All vs. All).
conclusionsThe proposed method outperforms previous studies with the same database by 2% in binary comparison MCI vs. ADM and central and parietal brain regions revealed abnormal activity as AD progresses.
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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.