Diagnostic Classification of Alzheimer Disease using EEG signal

Abstract

Alzheimer’s disease (AD) plays an important role in the medical image processing. It is an irreversible neurodegenerative dementia that often occurs at the age of 65. It is a kind of memory loss that related with thinking and behavior of people’s day to day lives. Therefore, the researchers are taking more efforts to find proper treatment and improve the quality of patient’s life. This paper organize totally 161 subjects of which 79 subjects EEG signals with AD and 82 subjects EEG signals with cognitive normal are analyzed  with 1 K Hz and 2 K Hz. From the results, it can be measured that AR pole tracking with 15th order using FBNN reported the highest classification accuracy of 97.5 per cent with specificity 97%, sensitivity 94% and F measure 91%.