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Identification of Resting and Active State EEG Features of Alzheimer’s Disease using Discrete Wavelet Transform

Content Provider	Springer Nature Link
Author	Ghorbanian, Parham Devilbiss, David M. Verma, Ajay Bernstein, Allan Hess, Terry Simon, Adam J. Ashrafiuon, Hashem
Copyright Year	2013
Abstract	Alzheimer’s disease (AD) is associated with deficits in a number of cognitive processes and executive functions. Moreover, abnormalities in the electroencephalogram (EEG) power spectrum develop with the progression of AD. These features have been traditionally characterized with montage recordings and conventional spectral analysis during resting eyes-closed and resting eyes-open (EO) conditions. In this study, we introduce a single lead dry electrode EEG device which was employed on AD and control subjects during resting and activated battery of cognitive and sensory tasks such as Paced Auditory Serial Addition Test (PASAT) and auditory stimulations. EEG signals were recorded over the left prefrontal cortex (Fp1) from each subject. EEG signals were decomposed into sub-bands approximately corresponding to the major brain frequency bands using several different discrete wavelet transforms and developed statistical features for each band. Decision tree algorithms along with univariate and multivariate statistical analysis were used to identify the most predictive features across resting and active states, separately and collectively. During resting state recordings, we found that the AD patients exhibited elevated D4 (~4–8 Hz) mean power in EO state as their most distinctive feature. During the active states, however, the majority of AD patients exhibited larger minimum D3 (~8–12 Hz) values during auditory stimulation (18 Hz) combined with increased kurtosis of D5 (~2–4 Hz) during PASAT with 2 s interval. When analyzed using EEG recording data across all tasks, the most predictive AD patient features were a combination of the first two feature sets. However, the dominant discriminating feature for the majority of AD patients were still the same features as the active state analysis. The results from this small sample size pilot study indicate that although EEG recordings during resting conditions are able to differentiate AD from control subjects, EEG activity recorded during active engagement in cognitive and auditory tasks provide important distinct features, some of which may be among the most predictive discriminating features.
Starting Page	1243
Ending Page	1257
Page Count	15
File Format	PDF
ISSN	00906964
Journal	Annals of Biomedical Engineering
Volume Number	41
Issue Number	6
e-ISSN	15739686
Language	English
Publisher	Springer US
Publisher Date	2013-03-28
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	EEG Alzheimer’s disease Discrete wavelet transform Active brain states Decision tree Biomedicine general Biomedical Engineering Biophysics and Biological Physics Mechanics Biochemistry
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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