Deep learning techniques for automated Alzheimer's and mild cognitive impairment disease using EEG signals: A comprehensive review of the last decade (2013 - 2024).

Journal: Computer methods and programs in biomedicine
Published Date: Nov 12, 2024

Abstract

BACKGROUND AND OBJECTIVES: Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) are progressive neurological disorders that significantly impair the cognitive functions, memory, and daily activities. They affect millions of individuals worldwide, posing a significant challenge for its diagnosis and management, leading to detrimental impacts on patients' quality of lives and increased burden on caregivers. Hence, early detection of MCI and AD is crucial for timely intervention and effective disease management.

Authors

  • Madhav Acharya
    School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia. Electronic address: madhav.acharya@unisq.edu.au.
  • Ravinesh C Deo
    School of Agricultural Computational and Environmental Sciences, International Centre of Applied Climate Science (ICACS), University of Southern Queensland, Springfield, QLD, 4300, Australia. ravinesh.deo@usq.edu.au.
  • Xiaohui Tao
    School of Sciences, University of Southern Queensland, Toowoomba 4350, Australia.
  • Prabal Datta Barua
    Cogninet Australia, Sydney, NSW 2010 Australia.
  • Aruna Devi
    School of Education and Tertiary Access, University of the Sunshine Coast, Caboolture Campus, Australia.
  • Anirudh Atmakuru
    University of Massachusetts, Amherst, USA.
  • Ru-San Tan
    National Heart Centre Singapore, Singapore, Singapore.

Keywords

External Resources

Popular Topics
Recent Journals