Various Parametric Classifications of Alzheimer’s Disease using EEG Signals
Abstract
Alzheimer’s disease (AD) also known as Dementia. It is a most vulnerable disease in our human brain which can be detected with the help of EEG signals. Further, this paper represents the review of AD and EEG signals, it consists of totally 161 subjects of which 79 subjects with AD and 82 subjects with cognitive normal.Besides, two parametric functions are compared for further results. Based on the experimental analysis, 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 94%, sensitivity 97% and F measure 91% when compared to FFNN algorithm.
