A Wavelet Entropy Based Methodology for Classification Among Healthy, Mild Cognitive Impairment and Alzheimer’s Disease People

Abstract

Alzheimer’s disease (AD) and Mild Cognitive Impairment (MCI) are cognitive diminished conditions that requires neuropsychological, images and complementary test for diagnosis. The electroencephalogram equipment is a less expensive, less invasive and a more portable option that image ones so there is an increased interest in an EEG based methodology for cognitive impairment diagnosis. In this work is presented an eyes closed resting state condition EEG signal diagnosis tool, based on wavelet decomposition and wavelet entropy. The methodology allows discriminating among Healthy, Mild Cognitive Impairment and Alzheimer’s disease people. For this purpose theta band-EEG power ratio, beta band-EEG power ratio and entropy values distribution through time in 14 electrodes are used. Wavelet decomposition is performed on five levels using Haar wavelet mother on two seconds windows. After decomposition wavelet power ratio and entropy distribution calculation are performed. The characteristics are used in a Healthy–MCI, Healthy-AD and MCI-AD classification using Support Vector Machine with polynomial kernel providing six inputs to a neural network (two layer, 13 neurons in the hidden layer) in charge of the final classification. Data base is composed of 17 healthy, nine Mild Cognitive Impairment and 15 Alzheimer’s disease people registers. A precision of 92.68% to 97.56% is achieved, better or equal to other entropy-based methods with the advantage of separating the three groups and use a bigger database. This methodology reveals as a potential quantitative diagnosis-support tool especially between Healthy people and Mild Cognitive Impairment where some of the conventional test fails.