Automatic Diagnosis of Mild Cognitive Impairment Using EEG Spectral Features

Abstract

Alzheimer’s disease (AD) is one of the most expensive and fatal disease in elderly population. Up to now no cure have been found for AD, so early stage diagnosis is the only way to control it. Mild Cognitive Impairment (MCI) usually is the early stage of AD which is defined as decreasing in mental abilities such a cognition, memory and speech not too severe to interfere daily activities. MCI diagnosis israther hard and usually assumed as normal consequences of aging. This study, proposes an accurate, mobile and non-expensive diagnostic approach based on EEG signal. EEG signals were recorded using 19 electrodes positioned according to the 10–20 International system at resting eyes closed state from 16 normal and 11 MCI participants. 19 Spectral features are computed for each channel and examined using a correlation-based algorithm to select the best discriminative features. Selected features are classified using a combination of neurofuzzy system and KNN classifier. Final results reach 88.89%, 100% and 83.33% for accuracy, sensitivity and specificity respectively which shows the potential of proposed method to be used as a MCI diagnostic tool especially for screening a large population.