Classification of Alzheimer’s Disease from EEG Signal Using Robust-PCA Feature Extraction

Abstract The encephalographic (EEG) signal is an electrical signal that measures the brain activity. Due to its noninvasive acquisition process, it is often used to investigate the presence of Alzheimer’s disease (AD) or other common forms of neurodegerative disorders due to brain changes, that occur most frequently in older adults. Early detection of prodromal stages of AD, in which an individual has mild but measurable cognitive deficiencies with no significant effect on the functional activity of daily living, may help to reduce mortality and morbidity. This paper proposes an investigation of the classification of AD from EEG signal using robust-principal component analysis (R-PCA) feature extraction algorithm. Four widely used machine learning algorithms such as k-nearest neighbor (kNN), decision tree (DT), support vector machine (SVM), and naive Bayes have been implemented and compared by using a custom dataset composed of 13 subjects healthy or affected by AD in order to asses their classification performance.