Discrete wavelet transform EEG features of Alzheimer'S disease in activated states.
In this study, electroencephalogram (EEG) signals obtained by a single-electrode device from 24 subjects - 10 with Alzheimer's disease (AD) and 14 age-matched Controls (CN) - were analyzed using Discrete Wavelet Transform (DWT). The focus of the study is to determine the discriminating EEG features of AD patients while subjected to cognitive and auditory tasks, since AD is characterized by progressive impairments in cognition and memory. At each recording block, DWT extracts EEG features corresponding to major brain frequency bands. T-test and Kruskal-Wallis methods were used to determine the statistically significant features of EEG signals from AD patients compared to Controls. A decision tree algorithm was then used to identify the dominant features for AD patients. It was determined that the mean value of the low-δ (1 - 2 Hz) frequency band during the Paced Auditory Serial Addition Test with 2.0 (s) interval and the mean value of the δ frequency band (12 - 30 Hz) during 6 Hz auditory stimulation have higher mean values in AD patients than Controls. Due to artifacts, the less reliable low-δ features were removed and it was determined that the mean value of β frequency band during 6 Hz auditory stimulation followed by the standard deviation of θ (4 - 8 Hz) frequency band of one card learning cognitive task are higher for AD patients compared to Controls and thus the most dominant discriminating features of the disease.
Center for Nonlinear Dynamics and Control, Villanova University, Villanova, PA 19085, USA. parham.ghorbanian@villanova.edu
P
P
Ghorbanian
D M
DM
Devilbiss
A J
AJ
Simon
A
A
Bernstein
T
T
Hess
H
H
Ashrafiuon
D000465:Algorithms; D000544:Alzheimer Disease / Q000503:physiopathology*; D016022:Case-Control Studies; D004569:Electroencephalography / Q000379:methods*; D006801:Humans