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

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 delt...

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Bibliographic Details
Published in2010 IEEE International Conference on Acoustics, Speech and Signal Processing pp. 606 - 609
Main Authors Akrofi, Kwaku, Pal, Ranadip, Baker, Mary C, Nutter, Brian S, Schiffer, Randolph W
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2010
Subjects
Aging
Alzheimer's disease
Alzheimer's disease (AD)
Bayesian data fusion
Bayesian methods
Coherence
Dementia
Electroencephalography
Frequency
mild cognitive impairment (MCI)
Pattern recognition
Power engineering and energy
Power engineering computing
Sequential floating forward search (SFFS)
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Summary: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.
ISBN:9781424442959
1424442958
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2010.5495193