Comparative multiresolution wavelet analysis of ERP spectral bands using an ensemble of classifiers approach for early diagnosis of Alzheimer's disease

Abstract

Early diagnosis of Alzheimer's disease (AD) is becoming an increasingly important healthcare concern. Prior approaches analyzing event-related potentials (ERPs) had varying degrees of success, primarily due to smaller study cohorts, and the inherent difficulty of the problem. A new effort using multiresolution analysis of ERPs is described. Distinctions of this study include analyzing a larger cohort, comparing different wavelets and different frequency bands, using ensemble-based decisions and, most importantly, aiming the earliest possible diagnosis of the disease. Surprising yet promising outcomes indicate that ERPs in response to novel sounds of oddball paradigm may be more reliable as a biomarker than the more commonly used responses to target sounds.

Figures

Figure 1

(a&b) Expected P300 behavior from normal and AD patients, (c&d) not all cases follow this behavior.

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The 10–20 International EEG electrode placement system

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7-level DWT decomposition

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Reconstructed detail and approximation signals of a cognitively normal person

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Reconstructed detail and approximation signals of a probable–AD patient.

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Learn++ pseudocode

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Learn++ block diagram