ArticlePhysical and engineering sciences in medicine2024
LCADNet: a novel light CNN architecture for EEG-based Alzheimer disease detection.
Article in Physical and engineering sciences in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- A novel diagnosis method based on multimodal-aware bi-objective competitive mechanism for Alzheimer's disease.Scientific reports · 2026Article
- EEG channel selection using metaheuristic algorithms in alcoholism detection using optimal wavelet transform.Scientific reports · 2026Article
- Learning patient-specific spatial biomarker dynamics via operator learning for Alzheimer's disease progression.NPJ systems biology and applications · 2026Article
- A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer's disease detection.Cognitive neurodynamics · 2025Article
- FHESA: fourier decomposition and hilbert transform based EEG signal analysis for Alzheimer's disease detection.Physical and engineering sciences in medicine · 2025Article
- A comprehensive EEG dataset and performance assessment for Autism Spectrum Disorder.Scientific reports · 2025Article
- A Deep Learning Approach to Alzheimer's Diagnosis Using EEG Data: Dual-Attention and Optuna-Optimized SVM.Biomedicines · 2025Article
- Improving EEG based brain computer interface emotion detection with EKO ALSTM model.Scientific reports · 2025Article
- A novel deep learning framework with artificial protozoa optimization-based adaptive environmental response for wind power prediction.Scientific reports · 2025Article
- Artificial intelligence-driven translational medicine: a machine learning framework for predicting disease outcomes and optimizing patient-centric care.Journal of translational medicine · 2025Article
- A multi-graph convolutional network method for Alzheimer's disease diagnosis based on multi-frequency EEG data with dual-mode connectivity.Frontiers in neuroscience · 2025Article
- [Fusion of electroencephalography multi-domain features and functional connectivity for early dementia recognition].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- STEADYNet: Spatiotemporal EEG analysis for dementia detection using convolutional neural network.Cognitive neurodynamics · 2024Article
- Article
- Contribution of Scalp Regions to Machine Learning-Based Classification of Dementia Utilizing Resting-State qEEG Signals.Neuropsychiatric disease and treatment · 2024Article
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9 authors.
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Abstract
Alzheimer's disease (AD) is a progressive and incurable neurologi-cal disorder with a rising mortality rate, worsened by error-prone, time-intensive, and expensive clinical diagnosis methods. Automatic AD detection methods using hand-crafted Electroencephalogram (EEG) signal features lack accuracy and reliability. A lightweight convolution neural network for AD detection (LCADNet) is investigated to extract disease-specific features while reducing the detection time. The LCADNet uses two convolutional layers for extracting complex EEG features, two fully connected layers for selecting disease-specific features, and a softmax layer for predicting AD detection probability. A max-pooling layer interlaced between convolutional layers decreases the time-domain redundancy in the EEG signal. The efficiency of the LCADNet and four pre-trained models using transfer learning is compared using a publicly available AD detection dataset. The LCADNet shows the lowest computation complexity in terms of both the number of floating point operations and inference time and the highest classification performance across six measures. The generalization of the LCADNet is assessed by cross-testing it with two other publicly available AD detection datasets. It outperforms existing EEG-based AD detection methods with an accuracy of 98.50%. The LCADNet may be a valuable aid for neurologists and its Python implemen- tation can be found at github.com/SandeepSangle12/LCADNet.git.
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