Deep Learning-Based Multilevel Classification of Alzheimer's Disease Using Non-invasive Functional Near-Infrared Spectroscopy.

ABSTRACT: The timely diagnosis of Alzheimer's disease (AD) and its prodromal stages is critically important for the patients, who manifest different neurodegenerative severity and progression risks, to take intervention and early symptomatic treatments before the brain damage is shaped. As one of the promising techniques, functional near-infrared spectroscopy (fNIRS) has been widely employed to support early-stage AD diagnosis. This study aims to validate the capability of fNIRS coupled with Deep Learning (DL) models for AD multi-class classification. First, a comprehensive experimental design, including the resting, cognitive, memory, and verbal tasks was conducted. Second, to precisely evaluate the AD progression, we thoroughly examined the change of hemodynamic responses measured in the prefronta

SUBMITTER: Ho TKK 

PROVIDER: S-EPMC9087351 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature