Detecting Alzheimer’s Dementia Degree

Abstract

The diagnosis of Alzheimer’s disease (AD) faces two important issues. They are how to extract the features of the rhythms of patients with AD and how to label them and reveal the degree of dementia in patients. This study defines 14 instantaneous power indicators of dementia judgment through Hilbert marginal spectrum (HMS) from rhythm waves. A warped infinite Gaussian mixture model (WiGMM) is proposed to learn the latent variables of these indicators to detect the degree of dementia. The experimental results show that HMS-based indicators are able to reflect the cognitive function of AD patients. This proposed method has the ability to detect brain cognitive status through a warped transform and Dirichlet process parameter prior to inference.