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

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.

Brain MR Image Classification for Alzheimer’s Disease Diagnosis Based on Multifeature Fusion

“…In the paper proposed by Torabi et al [17], 336 features extracted from gray-level cooccurrence matrix (GLMC) were used for classification of AD. Ghorbanian et al [18] used discrete wavelet transform to extract features to diagnose AD. At present, morphometric MRI can be adapted to improve the performance of diagnosing AD, which is safe, reliable, and noninvasive.…”

Epileptic seizure detection by combining robust‐principal component analysis and least square‐support vector machine

“…15 The human EEG rhythms include: d (< 4 Hz), u (4-8 Hz), a (8-13 Hz), b (13-30 Hz), g (> 30 Hz). 16 A pattern could be found from the correspondence between those DB4 bands and EEG rhythm, and that two DB4 bands always cover one EEG rhythm. Thus, we chose DB4 for decomposing EEG signals.…”

“…The residual signal (a5) represents 0–3 Hz, and the detail signals represent 50–100 Hz (d1), 25–50 Hz (d2), 12–25 Hz (d3), 6–12 Hz (d4), and 3–6 Hz (d5) . The human EEG rhythms include: δ (< 4 Hz), θ (4–8 Hz), α (8–13 Hz), β (13–30 Hz), γ (> 30 Hz) . A pattern could be found from the correspondence between those DB4 bands and EEG rhythm, and that two DB4 bands always cover one EEG rhythm.…”

Stochastic non-linear oscillator models of EEG: the Alzheimer's disease case

“…A global optimization routine is employed in order to match the output of with EEG recordings in terms of power spectrum, Shannon entropy, and sample entropy. The EEG signals were recorded under resting EC and EO conditions in an earlier pilot study of Alzheimer's disease (AD) patients vs. age-matched healthy control (CTL) subjects (Ghorbanian et al, 2013 ). The model parameters obtained for the oscillators representing EC and EO EEG signals for CTL and AD patients are compared in order to establish statistically significant, distinct models for AD and CTL subjects under each condition.…”