Alternating diffusion maps for dementia severity assessment

Tal Shnitzer, Maya Rapaport, Noga Cohen, Natalya Yarovinsky, Ronen Talmon, Judith Aharon-Peretz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper we address the detection of Alzheimer's disease based solely on EEG recordings. We assume that the state of Alzheimer's disease can be described by a latent manifold, captured by the EEG sensors and apply alternating diffusion to reveal this common underlying manifold from multiple EEG sensors. We show that based on a small number of EEG electrodes, a new representation can be obtained, which allows a clear distinction between healthy subjects and Alzheimer patients in different disease stages.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
Pages831-835
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Country/TerritoryUnited States
CityNew Orleans
Period5/03/179/03/17

Keywords

  • Manifold Learning
  • Alternating Diffusion
  • Electroencephalogram
  • Alzheimer's Disease

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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