Ensemble Techniques with Weighted Combination Rules for Early Diagnosis of Alzheimer´s Disease

As the population of our elderly suffering from Alzheimer´s disease increases rapidly, the need for an accurate, inexpensive and non-intrusive diagnostic procedure that can be made available to local community clinics becomes an increasingly critical public health concern. We propose multiresolution analysis of the electroencephalogram (EEG) followed by an ensemble based classification designed to fuse data from different EEG channels. Several classifier combination rules, including competence based weighted combination have been implemented to evaluate their data fusion performance, with particular emphasis on diagnosing the disease at its earliest stages. Diagnostic performance of the proposed approach has been very promising.