Multiresolution wavelet analysis and ensemble of classifiers for early diagnosis of Alzheimer's disease

The diagnosis of Alzheimer's disease at an early stage is a major concern due to the growing number of the elderly population affected, as well as the lack of a standard and effective diagnosis procedure available to community healthcare providers. Recent studies have used wavelets and other signal processing methods to analyze EEG signals in an attempt to find a non-invasive biomarker for Alzheimer's disease and had varying degrees of success. These studies have traditionally used automated classifiers such as neural networks; however the use of an ensemble of classifiers has not been previously explored and may prove to be beneficial. In this study, multiresolution wavelet analysis is performed on event related potentials of the EEG which are then used with the ensemble of classifiers based Learn++ algorithm. We describe the approach, and present our promising preliminary results.