Paper Title
Detection of Alzheimer’s Disease using Optimized EEG data Acquisition and Signal Processing Techniques

Abstract
Alzheimer’s disease is a growing health problem in the elderly population. Efficient detection of Alzheimer’s disease is still a challenge. Previous studies have shown that EEG can be used for the detection of Alzheimer’s disease. However, this has been done using expensive EEG systems. This study is aimed at building a low cost EEG based Alzheimer’s disease detection system. This study proposes a low cost EEG based wireless headset system, EMOTIV EPOC, for the detection of Alzheimer’s disease. The slowness of EEG in alpha and beta bands were measured using spectral analysis methods. EEG signals of AD patients showed a significant increase (at p<0.05) in relative power of low frequency bands than those of control subjects. Most significant change was observed in Delta band (2-4 Hz) of AD patients for all 14 channels recorded. Reduced complexity of EEG signals was calculated using Approximate Entropy (ApEn) method to detect Alzheimer’s disease. It was observed that AD patients have significantly lower ApEn values than control subjects at electrodes F8, P7, P8 and T8 (p<0.05).