Detection of Alzheimer's Disease from EEG Signals Using Explainable Artificial Intelligence Analysis

Abstract

In this study, the evaluation of classification models with frequency and chaotic features was aimed for the classification of healthy individuals and Alzheimer's patients using EEG signals. Morlet wavelet transform was employed for calculating EEG features to determine the characteristics in the frequency domain. Additionally, Lyapunov exponents were utilized for the analysis of chaotic features, and significant EEG channels were identified from the obtained results of the wavelet transform. Using permutation importance, the impact of each feature on the performance of the classification model was assessed. In this evaluation, the Random Forest model stood out in overall performance, showing the highest accuracy (0.7614), precision (0.7546), and F1 score (0.793) compared to other models. Furthermore, the Naive Bayes model achieved the highest sensitivity (0.8662) in detecting positive instances.