Early Alzheimer's disease diagnosis based on EEG spectral images using deep learning

Early diagnosis of Alzheimer's disease (AD) is a proceeding hot issue along with a sharp upward trend in the incidence rate. Recently, early diagnosis of AD employing Electroencephalogram (EEG) as a specific hallmark has been an increasingly significant hot topic area. In consideration of the limited size of available EEG spectral images, how to extract more abstract features for better generalization still remains tremendously troubling. In this paper, we demonstrate that it can be settled well with multi-task learning strategy based on discriminative convolutional high-order Boltzmann Machine with hybrid feature maps. First, differently from our original model - Contractive Slab and Spike Convolutional Deep Boltzmann Machine (CssCDBM), we directly conduct EEG spectral image classification via inducing label layer, resulting in a discriminative version of CssCDBM, referred to as DCssCDBM. This demonstrates DCssCDBM can be extended well into the classification model instead of feature extractor alone previously. Then, the most important approach innovation is that we train our DCssCDBM with multi-task learning framework via EEG spectral images based Identification and verification tasks for overfitting reduction for the first time, which could increase the inter-subject variations and reduce the intra-subject variations respectively, both of which are essential to early diagnosis of AD. The proposed method shows the better ability of high-level representations extraction and demonstrates the advanced results over several state-of-the-art methods.