ArticleMedical & biological engineering & computing2021
Novelty detection-based approach for Alzheimer's disease and mild cognitive impairment diagnosis from EEG.
Article in Medical & biological engineering & computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it, 54 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
- The EEG analysis and identification of Alzheimer's disease: a review.Frontiers in aging neuroscience · 2025Pooled it
- Assessing the Potential of EEG in Early Detection of Alzheimer's Disease: A Systematic Comprehensive Review (2000-2023).Journal of Alzheimer's disease reports · 2024Pooled it
- A deep-SVM hybrid framework with enhanced EEG feature engineering and SHAP-based explainability for Alzheimer's classification.Scientific reports · 2026Article
- Integrating memory-guided saccades and EEG for MCI screening: a multimodal LASSO modeling approach.Frontiers in aging neuroscience · 2026Article
- NeuroFusionNet: a hybrid EEG feature fusion framework for accurate and explainable Alzheimer's Disease detection.Scientific reports · 2025Article
- Electroencephalogram-based time-frequency analysis for Alzheimer's disease detection using machine learning.Journal of biological methods · 2025Article
- Fusing convolutional learning and attention-based Bi-LSTM networks for early Alzheimer's diagnosis from EEG signals towards IoMT.Scientific reports · 2024Article
- STEADYNet: Spatiotemporal EEG analysis for dementia detection using convolutional neural network.Cognitive neurodynamics · 2024Article
- An ensemble learning model for continuous cognition assessment based on resting-state EEG.npj aging · 2024Article
- Co-twin design in brain imaging-review on biomarkers of Alzheimer's disease.Cerebral cortex (New York, N.Y. : 1991) · 2023Review
- An Approach toward Artificial Intelligence Alzheimer's Disease Diagnosis Using Brain Signals.Diagnostics (Basel, Switzerland) · 2023Article
- A Greedy Optimized Intelligent Framework for Early Detection of Alzheimer's Disease Using EEG Signal.Computational intelligence and neuroscience · 2023Article
- Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework.Sensors (Basel, Switzerland) · 2022Article
- Sleep EEG-Based Approach to Detect Mild Cognitive Impairment.Frontiers in aging neuroscience · 2022Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Alzheimer's disease is diagnosed via means of daily activity assessment. The EEG recording evaluation is a supporting tool that can assist the practitioner to recognize the illness, especially in the early stages. This paper presents a new approach for detecting Alzheimer's disease and potentially mild cognitive impairment according to the measured EEG records. The proposed method evaluates the amount of novelty in the EEG signal as a feature for EEG record classification. The novelty is measured from the parameters of EEG signal adaptive filtration. A linear neuron with gradient descent adaptation was used as the filter in predictive settings. The extracted feature (novelty measure) is later classified to obtain Alzheimer's disease diagnosis. The proposed approach was cross-validated on a dataset containing EEG records of 59 patients suffering from Alzheimer's disease; seven patients with mild cognitive impairment (MCI) and 102 controls. The results of cross-validation yield 90.73% specificity and 89.51% sensitivity. The proposed method of feature extraction from EEG is completely new and can be used with any classifier for the diagnosis of Alzheimer's disease from EEG records.
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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.