Classification of Alzheimer's disease and mild cognitive impairment by pattern recognition of EEG power and coherence

Kwaku Akrofi, Ranadip Pal, Mary C. Baker, Brian S. Nutter, Randolph W. Schiffer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

This paper describes a methodology used to classify Alzheimer's disease (AD) and mild cognitive impairment (MCI) with high accuracy using EEG data. The sequential forward floating search (SFFS) was used to select features from relative average power for channel locations in frequency bands delta, theta, alpha, and beta, and coherence between intrahemispheric channel pairs for the same frequency ranges. The selected feature sets allowed us to achieve close to 90% classifier accuracy when classifying MCI patients and normal subjects. Our results showed that selecting features from a combined set of power and coherence features produced better results than the use of either feature independently. The combined feature set also showed better classification rates than a Bayesian classifier fusion approach.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages606-609
Number of pages4
DOIs
StatePublished - 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: Mar 14 2010Mar 19 2010

Publication series

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

Conference

Conference2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
CountryUnited States
CityDallas, TX
Period03/14/1003/19/10

Keywords

  • Alzheimer's disease (AD)
  • Bayesian data fusion
  • Mild cognitive impairment (MCI)
  • Sequential floating forward search (SFFS)

Fingerprint Dive into the research topics of 'Classification of Alzheimer's disease and mild cognitive impairment by pattern recognition of EEG power and coherence'. Together they form a unique fingerprint.

  • Cite this

    Akrofi, K., Pal, R., Baker, M. C., Nutter, B. S., & Schiffer, R. W. (2010). Classification of Alzheimer's disease and mild cognitive impairment by pattern recognition of EEG power and coherence. In 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings (pp. 606-609). [5495193] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2010.5495193