Development of Alzheimer Disease Classification System using Fractal Features
Alzheimer’s disease (AD) plays an important role in the medical signal processing using EEG signals. It is an irreversible neurodegenerative dementia that often occurs at the age of 70. 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 suitable diagnosis methods to improve the quality of AD patient’s life. This paper totally organize 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 observed that Box counting fractal feature with 20 orders using MFNN reported the highest classification accuracy of 90 per cent and the Box counting with 5th order using MFNN reported the lowest classification accuracy of 75 per cent.