DETECTION OF ALZHEIMER'S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS

Abstract

Alzheimer's disease is a complex brain disease and is also the most common form of dementia that leads to impaired social and intellectual abilities. The disease only manifests itself with a simple forgetfulness, as the disease progresses, the patient forgets the recent events, cannot recognize his family members and close environment, and becomes in need of care in the last stage. Early detection is therefore crucial for medical intervention to prevent brain injury and prolong everyday functioning. In this study is aimed to detection of Alzheimer’s disease from EEG signals using the multitaper and ensemble learning methods. The dataset comprises of 24 healthy people and 24 Alzheimer's patients' EEG signals. 49 features were extracted by calculating the power spectral density (PSD) of the frequencies of the EEG signals between 1-49 Hz using the multitaper method. Then, the performances of AdaboostM1, Total Boost, Gentle Boost, Logit Boost, Robust Boost, and Bagging ensemble learning algorithms were compared. As a result of experiments, the Logit Boost algorithm has the highest performance. The algorithm has achieved a promising performance of 93.04% accuracy, 93.09% f1-score, 92.75% sensitivity, 93.43% precision, and 93.33% specificity.