Wavelet coherence model for diagnosis of Alzheimer disease.
This article presents a wavelet coherence investigation of electroencephalograph (EEG) readings acquired from patients with Alzheimer disease (AD)  and healthy controls. Pairwise electrode wavelet coherence is calculated over each frequency band (delta, theta, alpha, and beta). For comparing the synchronization fraction of 2 EEG signals, a wavelet coherence fraction is proposed which is defined as the fraction of the signal time during which the wavelet coherence value is above a certain threshold. A one-way analysis of variance test shows a set of statistically significant differences in wavelet coherence between AD and controls. The wavelet coherence method is effective for studying cortical connectivity at a high temporal resolution. Compared with other conventional AD coherence studies, this study takes into account the time-frequency changes in coherence of EEG signals and thus provides more correlational details. A set of statistically significant differences was found in the wavelet coherence among AD and controls. In particular, temporocentral regions show a significant decrease in wavelet coherence in AD in the delta band, and the parietal and central regions show significant declines in cortical connectivity with most of their neighbors in the theta and alpha bands. This research shows that wavelet coherence can be used as a powerful tool to differentiate between healthy elderly individuals and probable AD patients.
Department of Biomedical Engineering, Electrical and Computer Engineering, Ohio State University, OH 43210, USA.
Ziad
Z
Sankari
Hojjat
H
Adeli
Anahita
A
Adeli
D000368:Aged; D000513:Alpha Rhythm / Q000502:physiology; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000503:physiopathology; D001921:Brain / Q000503:physiopathology; D004566:Electrodes; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D013826:Theta Rhythm / Q000502:physiology; D058067:Wavelet Analysis