Efficient identification of Alzheimer's brain dynamics with Spatial-Temporal Autoencoder: A deep learning approach for diagnosing brain disorders

Alzheimer's disease (AD) is a progressive neurological disorder seriously affecting cognitive and behavior abilities of the older people. Accurate and early diagnosis of AD is critical for improving the therapeutic effect and alleviate the clinical symptom. In this work, we proposed an automatic EEG-based diagnosis scheme for AD patients with deep learning methods. A Spatial-Temporal Autoencoder (STAE) framework with Convolutional Neural Network (CNN)-Long Short-Term-Memory (LSTM) generative model was designed to estimate latent factors of the observed oscillatory activity in the brain from multi-channels electroencephalogram (EEG) signals via unsupervised learning. Based on latent factor analysis, Alzheimer's brain dynamics on single-trials were inferred and temporal evolution of latent brain state was analyzed in low-dimensional state space. The study mainly showed that: i) the brain state trajectories of AD patients were distinct from healthy subjects, resulting in different forms of ring manifolds and allowing to accurately identify AD; ii) experimental results demonstrated the efficiency and flexibility of the proposed deep learning-based diagnosis scheme, by which the classification of AD patients and the normal based on clinical EEG dataset achieved an accuracy of 96.30%, a sensitivity of 97.73% and a specificity of 94.69% with a multiple layer perception (MLP) classifier; iii) compared with other approaches for latent brain dynamics estimation, STAE exhibited superior performance with high accuracy of AD identification and strongly robust against instabilities in EEG recordings. The present results reveal the neuro-pathological mechanism of Alzheimer's disease with brain dynamics variations and provide a feasible diagnosis tool for brain disorders.