Dementia is a neuro-degenerative disorder with a high death rate, mainly due to high human error, time, and cost of the current clinical diagnostic techniques. The existing dementia detection methods using hand-crafted electroencephalogram (EEG) signal features are unreliable. A convolution neural network using spatiotemporal EEG signals (STEADYNet) is presented to improve the dementia detection. The STEADYNet uses a multichannel temporal EEG signal as input. The network is grouped into feature extraction and classification components. The feature extraction comprises two convolution layers to generate complex features, a max-pooling layer to reduce the EEG signal's spatiotemporal redundancy, and a dropout layer to improve the network's generalization. The classification processes the feature extraction output nonlinearly using two fully-connected layers to generate salient features and a softmax layer to generate disease probabilities. Two publicly available multiclass datasets of dementia are used for evaluation. The STEADYNet outperforms existing automatic dementia detection methods with accuracies of , , and for Alzheimer's disease, mild cognitive impairment, and frontotemporal dementia, respectively. The STEADYNet has a low inference time and floating point operations, suitable for real-time applications. It may aid neurologists in efficient detection and treatment. A Python implementation of the STEADYNet is available at https://github.com/SandeepSangle12/STEADYNet.git.
Keywords: Alzheimer’s disease; Convolution neural network; Electroencephalogram; Frontotemporal dementia.
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