Abstract
Clinical methods for dementia detection are expensive and prone to human errors. Despite various computer-aided methods using electroencephalography (EEG) signals and artificial intelligence, a reliable detection of Alzheimer’s disease (AD) remains a challenge. The existing EEG-based machine learning models have limited performance or high computation complexity. Hence, there is a need for an optimal deep learning model for the detection of AD. This paper proposes a low-complexity EEG-based AD detection CNN called LEADNet to generate disease-specific features. LEADNet employs spatiotemporal EEG signals as input, two convolution layers for feature generation, a max-pooling layer for asymmetric spatiotemporal redundancy reduction, two fully-connected layers for nonlinear feature transformation and selection, and a softmax layer for disease probability prediction. Different quantitative measures are calculated using an open-source AD dataset to compare LEADNet and four pre-trained CNN models. The results show that the lightweight architecture of LEADNet has at least a 150-fold reduction in network parameters and the highest testing accuracy of 99.24% compared to pre-trained models. The investigation of individual layers of LEADNet showed successive improvements in feature transformation and selection for detecting AD subjects. A comparison with the state-of-the-art AD detection models showed that the highest accuracy, sensitivity, and specificity were achieved by the LEADNet model.
Original language | English |
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Pages (from-to) | 113888-113897 |
Number of pages | 10 |
Journal | IEEE Access |
Volume | 12 |
Early online date | 30 Jul 2024 |
DOIs | |
Publication status | E-pub ahead of print - 30 Jul 2024 |
Keywords
- electroencephalogram
- pre-trained models
- Alzheimer’s disease
- convolutional neural network
- Alzheimer's disease